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| 001 A Practical Example_ What You Will Learn in This Course.en.srt |
6.60KB |
| 001 A Practical Example_ What You Will Learn in This Course.mp4 |
49.03MB |
| 002 What Does the Course Cover.en.srt |
5.26KB |
| 002 What Does the Course Cover.mp4 |
62.25MB |
| 003 Download All Resources and Important FAQ.html |
22.58KB |
| 003 FAQ-The-Data-Science-Course.pdf |
306.10KB |
| 004 Data Science and Business Buzzwords_ Why are there so Many_.en.srt |
6.87KB |
| 004 Data Science and Business Buzzwords_ Why are there so Many_.mp4 |
81.41MB |
| 005 What is the difference between Analysis and Analytics.en.srt |
5.28KB |
| 005 What is the difference between Analysis and Analytics.mp4 |
53.55MB |
| 006 365-DataScience-Diagram.pdf |
323.08KB |
| 006 Business Analytics, Data Analytics, and Data Science_ An Introduction.en.srt |
11.05KB |
| 006 Business Analytics, Data Analytics, and Data Science_ An Introduction.mp4 |
64.51MB |
| 007 365-DataScience.png |
6.92MB |
| 007 365-DataScience-Diagram.pdf |
323.08KB |
| 007 Continuing with BI, ML, and AI.en.srt |
12.32KB |
| 007 Continuing with BI, ML, and AI.mp4 |
108.98MB |
| 008 365-DataScience.png |
6.92MB |
| 008 A Breakdown of our Data Science Infographic.en.srt |
5.31KB |
| 008 A Breakdown of our Data Science Infographic.mp4 |
67.74MB |
| 009 Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.en.srt |
9.33KB |
| 009 Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.mp4 |
126.87MB |
| 010 The Reason Behind These Disciplines.en.srt |
6.75KB |
| 010 The Reason Behind These Disciplines.mp4 |
81.18MB |
| 011 Techniques for Working with Traditional Data.en.srt |
11.03KB |
| 011 Techniques for Working with Traditional Data.mp4 |
138.30MB |
| 012 Real Life Examples of Traditional Data.en.srt |
2.33KB |
| 012 Real Life Examples of Traditional Data.mp4 |
29.93MB |
| 013 Techniques for Working with Big Data.en.srt |
5.89KB |
| 013 Techniques for Working with Big Data.mp4 |
75.50MB |
| 014 Real Life Examples of Big Data.en.srt |
1.96KB |
| 014 Real Life Examples of Big Data.mp4 |
22.03MB |
| 015 Business Intelligence (BI) Techniques.en.srt |
8.96KB |
| 015 Business Intelligence (BI) Techniques.mp4 |
89.94MB |
| 016 Real Life Examples of Business Intelligence (BI).en.srt |
2.21KB |
| 016 Real Life Examples of Business Intelligence (BI).mp4 |
29.54MB |
| 017 Techniques for Working with Traditional Methods.en.srt |
11.41KB |
| 017 Techniques for Working with Traditional Methods.mp4 |
111.65MB |
| 018 Real Life Examples of Traditional Methods.en.srt |
3.72KB |
| 018 Real Life Examples of Traditional Methods.mp4 |
42.78MB |
| 019 Machine Learning (ML) Techniques.en.srt |
9.07KB |
| 019 Machine Learning (ML) Techniques.mp4 |
99.32MB |
| 020 Types of Machine Learning.en.srt |
10.91KB |
| 020 Types of Machine Learning.mp4 |
125.14MB |
| 021 Real Life Examples of Machine Learning (ML).en.srt |
3.01KB |
| 021 Real Life Examples of Machine Learning (ML).mp4 |
36.81MB |
| 022 Necessary Programming Languages and Software Used in Data Science.en.srt |
7.57KB |
| 022 Necessary Programming Languages and Software Used in Data Science.mp4 |
103.51MB |
| 023 Finding the Job - What to Expect and What to Look for.en.srt |
4.67KB |
| 023 Finding the Job - What to Expect and What to Look for.mp4 |
54.38MB |
| 024 Debunking Common Misconceptions.en.srt |
5.49KB |
| 024 Debunking Common Misconceptions.mp4 |
72.85MB |
| 025 Course-Notes-Basic-Probability.pdf |
371.05KB |
| 025 The Basic Probability Formula.en.srt |
9.24KB |
| 025 The Basic Probability Formula.mp4 |
85.91MB |
| 026 Computing Expected Values.en.srt |
6.93KB |
| 026 Computing Expected Values.mp4 |
75.68MB |
| 027 Frequency.en.srt |
6.65KB |
| 027 Frequency.mp4 |
61.73MB |
| 028 Events and Their Complements.en.srt |
6.95KB |
| 028 Events and Their Complements.mp4 |
59.15MB |
| 029 Course-Notes-Combinatorics.pdf |
226.12KB |
| 029 Fundamentals of Combinatorics.en.srt |
1.35KB |
| 029 Fundamentals of Combinatorics.mp4 |
16.21MB |
| 030 Permutations and How to Use Them.en.srt |
4.22KB |
| 030 Permutations and How to Use Them.mp4 |
42.72MB |
| 031 Simple Operations with Factorials.en.srt |
3.38KB |
| 031 Simple Operations with Factorials.mp4 |
36.11MB |
| 032 Solving Variations with Repetition.en.srt |
3.59KB |
| 032 Solving Variations with Repetition.mp4 |
34.00MB |
| 033 Solving Variations without Repetition.en.srt |
4.70KB |
| 033 Solving Variations without Repetition.mp4 |
43.14MB |
| 034 Combinations-With-Repetition.pdf |
207.41KB |
| 034 Solving Combinations.en.srt |
5.80KB |
| 034 Solving Combinations.mp4 |
57.34MB |
| 035 Symmetry-Explained.pdf |
85.04KB |
| 035 Symmetry of Combinations.en.srt |
4.46KB |
| 035 Symmetry of Combinations.mp4 |
40.30MB |
| 036 Solving Combinations with Separate Sample Spaces.en.srt |
3.87KB |
| 036 Solving Combinations with Separate Sample Spaces.mp4 |
33.15MB |
| 037 Combinatorics in Real-Life_ The Lottery.en.srt |
4.31KB |
| 037 Combinatorics in Real-Life_ The Lottery.mp4 |
41.29MB |
| 038 A Recap of Combinatorics.en.srt |
3.87KB |
| 038 A Recap of Combinatorics.mp4 |
38.49MB |
| 039 Additional-Exercises-Combinatorics.pdf |
106.58KB |
| 039 Additional-Exercises-Combinatorics-Solutions.pdf |
245.67KB |
| 039 A Practical Example of Combinatorics.en.srt |
14.45KB |
| 039 A Practical Example of Combinatorics.mp4 |
134.31MB |
| 040 Course-Notes-Bayesian-Inference.pdf |
386.01KB |
| 040 Sets and Events.en.srt |
5.24KB |
| 040 Sets and Events.mp4 |
53.46MB |
| 041 Ways Sets Can Interact.en.srt |
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| 041 Ways Sets Can Interact.mp4 |
47.42MB |
| 042 Intersection of Sets.en.srt |
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| 042 Intersection of Sets.mp4 |
26.96MB |
| 043 Union of Sets.en.srt |
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| 043 Union of Sets.mp4 |
57.19MB |
| 044 Mutually Exclusive Sets.en.srt |
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| 044 Mutually Exclusive Sets.mp4 |
25.39MB |
| 045 Dependence and Independence of Sets.en.srt |
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| 045 Dependence and Independence of Sets.mp4 |
34.78MB |
| 046 The Conditional Probability Formula.en.srt |
5.10KB |
| 046 The Conditional Probability Formula.mp4 |
45.86MB |
| 047 The Law of Total Probability.en.srt |
3.61KB |
| 047 The Law of Total Probability.mp4 |
34.93MB |
| 048 The Additive Rule.en.srt |
2.83KB |
| 048 The Additive Rule.mp4 |
26.97MB |
| 049 The Multiplication Law.en.srt |
4.79KB |
| 049 The Multiplication Law.mp4 |
49.02MB |
| 050 Bayes' Law.en.srt |
7.45KB |
| 050 Bayes' Law.mp4 |
49.93MB |
| 051 A Practical Example of Bayesian Inference.en.srt |
19.98KB |
| 051 A Practical Example of Bayesian Inference.mp4 |
145.12MB |
| 051 Bayesian-Homework.pdf |
27.26KB |
| 051 Bayesian-Homework-Solutions.pdf |
30.35KB |
| 051 CDS-E7-E8-Hamilton.pdf |
845.31KB |
| 052 Course-Notes-Probability-Distributions.pdf |
463.95KB |
| 052 Fundamentals of Probability Distributions.en.srt |
7.80KB |
| 052 Fundamentals of Probability Distributions.mp4 |
73.40MB |
| 053 Types of Probability Distributions.en.srt |
9.62KB |
| 053 Types of Probability Distributions.mp4 |
71.06MB |
| 054 Characteristics of Discrete Distributions.en.srt |
2.55KB |
| 054 Characteristics of Discrete Distributions.mp4 |
22.70MB |
| 055 Discrete Distributions_ The Uniform Distribution.en.srt |
2.83KB |
| 055 Discrete Distributions_ The Uniform Distribution.mp4 |
24.39MB |
| 056 Discrete Distributions_ The Bernoulli Distribution.en.srt |
3.97KB |
| 056 Discrete Distributions_ The Bernoulli Distribution.mp4 |
34.13MB |
| 057 Discrete Distributions_ The Binomial Distribution.en.srt |
8.59KB |
| 057 Discrete Distributions_ The Binomial Distribution.mp4 |
68.83MB |
| 058 Discrete Distributions_ The Poisson Distribution.en.srt |
6.80KB |
| 058 Discrete Distributions_ The Poisson Distribution.mp4 |
55.75MB |
| 058 Poisson-Expected-Value-and-Variance.pdf |
145.99KB |
| 059 Characteristics of Continuous Distributions.en.srt |
8.97KB |
| 059 Characteristics of Continuous Distributions.mp4 |
84.12MB |
| 059 Solving-Integrals.pdf |
343.85KB |
| 060 Continuous Distributions_ The Normal Distribution.en.srt |
4.94KB |
| 060 Continuous Distributions_ The Normal Distribution.mp4 |
48.24MB |
| 060 Normal-Distribution-Exp-and-Var.pdf |
144.08KB |
| 061 Continuous Distributions_ The Standard Normal Distribution.en.srt |
5.46KB |
| 061 Continuous Distributions_ The Standard Normal Distribution.mp4 |
47.90MB |
| 062 Continuous Distributions_ The Students' T Distribution.en.srt |
2.87KB |
| 062 Continuous Distributions_ The Students' T Distribution.mp4 |
27.18MB |
| 063 Continuous Distributions_ The Chi-Squared Distribution.en.srt |
2.85KB |
| 063 Continuous Distributions_ The Chi-Squared Distribution.mp4 |
26.34MB |
| 064 Continuous Distributions_ The Exponential Distribution.en.srt |
4.28KB |
| 064 Continuous Distributions_ The Exponential Distribution.mp4 |
40.23MB |
| 065 Continuous Distributions_ The Logistic Distribution.en.srt |
5.20KB |
| 065 Continuous Distributions_ The Logistic Distribution.mp4 |
47.05MB |
| 066 A Practical Example of Probability Distributions.en.srt |
20.63KB |
| 066 A Practical Example of Probability Distributions.mp4 |
157.82MB |
| 066 Customers-Membership.xlsx |
9.69KB |
| 066 Customers-Membership-post.xlsx |
15.62KB |
| 066 Daily-Views.xlsx |
9.53KB |
| 066 Daily-Views-post.xlsx |
20.21KB |
| 066 FIFA19.csv |
8.64MB |
| 066 FIFA19-post.csv |
8.64MB |
| 067 Probability in Finance.en.srt |
10.18KB |
| 067 Probability in Finance.mp4 |
99.06MB |
| 067 Probability-in-Finance-Homework.pdf |
110.68KB |
| 067 Probability-in-Finance-Solutions.pdf |
184.46KB |
| 068 Probability in Statistics.en.srt |
8.73KB |
| 068 Probability in Statistics.mp4 |
77.28MB |
| 069 Probability-Cheat-Sheet.pdf |
320.28KB |
| 069 Probability in Data Science.en.srt |
6.89KB |
| 069 Probability in Data Science.mp4 |
63.49MB |
| 070 Course-notes-descriptive-statistics.pdf |
482.21KB |
| 070 Population and Sample.en.srt |
5.67KB |
| 070 Population and Sample.mp4 |
58.11MB |
| 070 Statistics-Glossary.xlsx |
20.26KB |
| 071 Course-notes-descriptive-statistics.pdf |
482.21KB |
| 071 Glossary.xlsx |
19.97KB |
| 071 Types of Data.en.srt |
6.17KB |
| 071 Types of Data.mp4 |
72.52MB |
| 072 Levels of Measurement.en.srt |
4.71KB |
| 072 Levels of Measurement.mp4 |
54.38MB |
| 073 2.3.Categorical-variables.Visualization-techniques-lesson.xlsx |
30.77KB |
| 073 Categorical Variables - Visualization Techniques.en.srt |
6.53KB |
| 073 Categorical Variables - Visualization Techniques.mp4 |
36.64MB |
| 074 2.3.Categorical-variables.Visualization-techniques-exercise.xlsx |
15.24KB |
| 074 2.3.Categorical-variables.Visualization-techniques-exercise-solution.xlsx |
41.11KB |
| 074 Categorical Variables Exercise.html |
986B |
| 074 Statistics-PDF-with-Excel-Solutions-that-dont-visualize-properly.pdf |
289.12KB |
| 075 2.4.Numerical-variables.Frequency-distribution-table-lesson.xlsx |
11.44KB |
| 075 Numerical Variables - Frequency Distribution Table.en.srt |
4.52KB |
| 075 Numerical Variables - Frequency Distribution Table.mp4 |
25.85MB |
| 076 2.4.Numerical-variables.Frequency-distribution-table-exercise-solution.xlsx |
13.15KB |
| 076 Numerical Variables Exercise.html |
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| 077 2.5.The-Histogram-lesson.xlsx |
18.63KB |
| 077 The Histogram.en.srt |
3.11KB |
| 077 The Histogram.mp4 |
13.78MB |
| 078 2.5.The-Histogram-exercise.xlsx |
15.50KB |
| 078 2.5.The-Histogram-exercise-solution.xlsx |
17.10KB |
| 078 Histogram Exercise.html |
974B |
| 078 Statistics-PDF-with-Excel-Solutions-that-dont-visualize-properly.pdf |
289.12KB |
| 079 2.6.Cross-table-and-scatter-plot.xlsx |
26.12KB |
| 079 Cross Tables and Scatter Plots.en.srt |
6.93KB |
| 079 Cross Tables and Scatter Plots.mp4 |
39.80MB |
| 080 2.6.Cross-table-and-scatter-plot-exercise.xlsx |
16.28KB |
| 080 2.6.Cross-table-and-scatter-plot-exercise-solution.xlsx |
40.44KB |
| 080 Cross Tables and Scatter Plots Exercise.html |
995B |
| 081 2.7.Mean-median-and-mode-lesson.xlsx |
10.49KB |
| 081 Mean, median and mode.en.srt |
5.96KB |
| 081 Mean, median and mode.mp4 |
37.12MB |
| 082 2.7.Mean-median-and-mode-exercise.xlsx |
10.87KB |
| 082 2.7.Mean-median-and-mode-exercise-solution.xlsx |
11.35KB |
| 082 Mean, Median and Mode Exercise.html |
986B |
| 083 2.8.Skewness-lesson.xlsx |
34.63KB |
| 083 Skewness.en.srt |
3.78KB |
| 083 Skewness.mp4 |
19.40MB |
| 084 2.8.Skewness-exercise.xlsx |
9.49KB |
| 084 2.8.Skewness-exercise-solution.xlsx |
19.78KB |
| 084 Skewness Exercise.html |
973B |
| 085 2.9.Variance-lesson.xlsx |
10.08KB |
| 085 Variance.en.srt |
7.81KB |
| 085 Variance.mp4 |
50.95MB |
| 086 2.9.Variance-exercise.xlsx |
10.83KB |
| 086 2.9.Variance-exercise-solution.xlsx |
11.05KB |
| 086 Variance Exercise.html |
1.38KB |
| 087 2.10.Standard-deviation-and-coefficient-of-variation-lesson.xlsx |
10.97KB |
| 087 Standard Deviation and Coefficient of Variation.en.srt |
6.86KB |
| 087 Standard Deviation and Coefficient of Variation.mp4 |
45.12MB |
| 088 2.10.Standard-deviation-and-coefficient-of-variation-exercise.xlsx |
11.61KB |
| 088 2.10.Standard-deviation-and-coefficient-of-variation-exercise-solution.xlsx |
12.60KB |
| 088 Standard Deviation and Coefficient of Variation Exercise.html |
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| 089 2.11.Covariance-lesson.xlsx |
24.92KB |
| 089 Covariance.en.srt |
5.11KB |
| 089 Covariance.mp4 |
27.48MB |
| 090 2.11.Covariance-exercise.xlsx |
20.23KB |
| 090 2.11.Covariance-exercise-solution.xlsx |
29.51KB |
| 090 Covariance Exercise.html |
975B |
| 091 Correlation Coefficient.en.srt |
4.89KB |
| 091 Correlation Coefficient.mp4 |
29.38MB |
| 092 2.12.Correlation-exercise.xlsx |
29.30KB |
| 092 2.12.Correlation-exercise-solution.xlsx |
29.48KB |
| 092 Correlation Coefficient Exercise.html |
988B |
| 093 2.13.Practical-example.Descriptive-statistics-lesson.xlsx |
146.51KB |
| 093 Practical Example_ Descriptive Statistics.en.srt |
21.61KB |
| 093 Practical Example_ Descriptive Statistics.mp4 |
160.46MB |
| 094 2.13.Practical-example.Descriptive-statistics-exercise.xlsx |
120.27KB |
| 094 2.13.Practical-example.Descriptive-statistics-exercise-solution.xlsx |
146.38KB |
| 094 Practical Example_ Descriptive Statistics Exercise.html |
1006B |
| 095 Course-notes-inferential-statistics.pdf |
382.32KB |
| 095 Introduction.en.srt |
1.69KB |
| 095 Introduction.mp4 |
15.50MB |
| 096 3.2.What-is-a-distribution-lesson.xlsx |
19.46KB |
| 096 Course-notes-inferential-statistics.pdf |
382.32KB |
| 096 What is a Distribution.en.srt |
6.10KB |
| 096 What is a Distribution.mp4 |
61.59MB |
| 097 The Normal Distribution.en.srt |
5.08KB |
| 097 The Normal Distribution.mp4 |
49.85MB |
| 098 3.4.Standard-normal-distribution-lesson.xlsx |
10.38KB |
| 098 The Standard Normal Distribution.en.srt |
4.09KB |
| 098 The Standard Normal Distribution.mp4 |
22.50MB |
| 099 3.4.Standard-normal-distribution-exercise.xlsx |
11.99KB |
| 099 3.4.Standard-normal-distribution-exercise-solution.xlsx |
24.04KB |
| 099 The Standard Normal Distribution Exercise.html |
997B |
| 100 Central Limit Theorem.en.srt |
5.85KB |
| 100 Central Limit Theorem.mp4 |
62.88MB |
| 101 Standard error.en.srt |
2.11KB |
| 101 Standard error.mp4 |
22.77MB |
| 102 Estimators and Estimates.en.srt |
3.85KB |
| 102 Estimators and Estimates.mp4 |
47.83MB |
| 103 What are Confidence Intervals_.en.srt |
3.39KB |
| 103 What are Confidence Intervals_.mp4 |
49.98MB |
| 104 3.9.Population-variance-known-z-score-lesson.xlsx |
11.21KB |
| 104 3.9.The-z-table.xlsx |
25.58KB |
| 104 Confidence Intervals; Population Variance Known; Z-score.en.srt |
10.15KB |
| 104 Confidence Intervals; Population Variance Known; Z-score.mp4 |
78.20MB |
| 105 3.9.Population-variance-known-z-score-exercise.xlsx |
10.83KB |
| 105 3.9.Population-variance-known-z-score-exercise-solution.xlsx |
11.16KB |
| 105 3.9.The-z-table.xlsx |
25.58KB |
| 105 Confidence Intervals; Population Variance Known; Z-score; Exercise.html |
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| 106 Confidence Interval Clarifications.en.srt |
5.59KB |
| 106 Confidence Interval Clarifications.mp4 |
57.03MB |
| 107 Student's T Distribution.en.srt |
4.27KB |
| 107 Student's T Distribution.mp4 |
35.43MB |
| 108 3.11.Population-variance-unknown-t-score-lesson.xlsx |
10.78KB |
| 108 3.11.The-t-table.xlsx |
15.85KB |
| 108 Confidence Intervals; Population Variance Unknown; T-score.en.srt |
5.93KB |
| 108 Confidence Intervals; Population Variance Unknown; T-score.mp4 |
32.20MB |
| 109 3.11.Population-variance-unknown-t-score-exercise.xlsx |
10.62KB |
| 109 3.11.Population-variance-unknown-t-score-exercise-solution.xlsx |
11.10KB |
| 109 3.11.The-t-table.xlsx |
15.85KB |
| 109 Confidence Intervals; Population Variance Unknown; T-score; Exercise.html |
1.00KB |
| 110 Margin of Error.en.srt |
6.38KB |
| 110 Margin of Error.mp4 |
47.23MB |
| 111 3.13.Confidence-intervals.Two-means.Dependent-samples-lesson.xlsx |
10.47KB |
| 111 Confidence intervals. Two means. Dependent samples.en.srt |
8.33KB |
| 111 Confidence intervals. Two means. Dependent samples.mp4 |
70.47MB |
| 112 3.13.Confidence-intervals.Two-means.Dependent-samples-exercise.xlsx |
13.74KB |
| 112 3.13.Confidence-intervals.Two-means.Dependent-samples-exercise-solution.xlsx |
14.24KB |
| 112 Confidence intervals. Two means. Dependent samples Exercise.html |
1015B |
| 113 3.14.Confidence-intervals.Two-means.Independent-samples-Part-1-lesson.xlsx |
9.83KB |
| 113 Confidence intervals. Two means. Independent Samples (Part 1).en.srt |
6.30KB |
| 113 Confidence intervals. Two means. Independent Samples (Part 1).mp4 |
28.75MB |
| 114 3.14.Confidence-intervals.Two-means.Independent-samples-Part-1-exercise.xlsx |
9.83KB |
| 114 3.14.Confidence-intervals.Two-means.Independent-samples-Part-1-exercise-solution.xlsx |
10.12KB |
| 114 Confidence intervals. Two means. Independent Samples (Part 1). Exercise.html |
1.00KB |
| 115 3.15.Confidence-intervals.Two-means.Independent-samples-Part-2-lesson.xlsx |
9.52KB |
| 115 Confidence intervals. Two means. Independent Samples (Part 2).en.srt |
4.67KB |
| 115 Confidence intervals. Two means. Independent Samples (Part 2).mp4 |
26.82MB |
| 116 3.15.Confidence-intervals.Two-means.Independent-samples-Part-2-exercise.xlsx |
9.17KB |
| 116 3.15.Confidence-intervals.Two-means.Independent-samples-Part-2-exercise-solution.xlsx |
9.79KB |
| 116 Confidence intervals. Two means. Independent Samples (Part 2). Exercise.html |
1.00KB |
| 117 Confidence intervals. Two means. Independent Samples (Part 3).en.srt |
2.03KB |
| 117 Confidence intervals. Two means. Independent Samples (Part 3).mp4 |
19.93MB |
| 118 3.17.Practical-example.Confidence-intervals-lesson.xlsx |
1.74MB |
| 118 Practical Example_ Inferential Statistics.en.srt |
14.17KB |
| 118 Practical Example_ Inferential Statistics.mp4 |
102.66MB |
| 119 3.17.Practical-example.Confidence-intervals-exercise.xlsx |
1.73MB |
| 119 3.17.Practical-example.Confidence-intervals-exercise-solution.xlsx |
1.82MB |
| 119 Practical Example_ Inferential Statistics Exercise.html |
1006B |
| 120 Course-notes-hypothesis-testing.pdf |
656.44KB |
| 120 Null vs Alternative Hypothesis.en.srt |
7.21KB |
| 120 Null vs Alternative Hypothesis.mp4 |
92.04MB |
| 121 Further Reading on Null and Alternative Hypothesis.html |
3.19KB |
| 122 Course-notes-hypothesis-testing.pdf |
656.44KB |
| 122 Rejection Region and Significance Level.en.srt |
8.99KB |
| 122 Rejection Region and Significance Level.mp4 |
82.61MB |
| 123 Type I Error and Type II Error.en.srt |
5.91KB |
| 123 Type I Error and Type II Error.mp4 |
43.93MB |
| 124 4.4.Test-for-the-mean.Population-variance-known-lesson.xlsx |
10.96KB |
| 124 Test for the Mean. Population Variance Known.en.srt |
8.46KB |
| 124 Test for the Mean. Population Variance Known.mp4 |
54.22MB |
| 125 4.4.Test-for-the-mean.Population-variance-known-exercise.xlsx |
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| 125 4.4.Test-for-the-mean.Population-variance-known-exercise-solution.xlsx |
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| 127 4.6.Test-for-the-mean.Population-variance-unknown-lesson.xlsx |
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| 130 4.7.Test-for-the-mean.Dependent-samples-exercise.xlsx |
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| 130 Test for the Mean. Dependent Samples Exercise.html |
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| 131 Test for the mean. Independent Samples (Part 1).en.srt |
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| 136 4.10.Hypothesis-testing-section-practical-example-exercise.xlsx |
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| 136 4.10.Hypothesis-testing-section-practical-example-exercise-solution.xlsx |
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| 136 Practical Example_ Hypothesis Testing Exercise.html |
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| 137 Introduction to Programming.en.srt |
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| 137 Introduction to Programming.mp4 |
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| 138 Why Python_.en.srt |
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| 139 Why Jupyter_.en.srt |
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| 139 Why Jupyter_.mp4 |
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| 140 Installing Python and Jupyter.en.srt |
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| 140 Installing Python and Jupyter.mp4 |
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| 141 Understanding Jupyter's Interface - the Notebook Dashboard.en.srt |
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| 141 Understanding Jupyter's Interface - the Notebook Dashboard.mp4 |
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| 142 Prerequisites for Coding in the Jupyter Notebooks.en.srt |
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| 142 Prerequisites for Coding in the Jupyter Notebooks.mp4 |
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| 143 Python-Introduction-Course-Notes.pdf |
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| 143 Variables.en.srt |
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| 143 Variables.mp4 |
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| 143 Variables-Exercise-Py3.ipynb |
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| 143 Variables-Solution-Py3.ipynb |
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| 144 Numbers-and-Boolean-Values-Exercise-Py3.ipynb |
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| 144 Numbers and Boolean Values in Python.en.srt |
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| 144 Numbers and Boolean Values in Python.mp4 |
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| 144 Numbers-and-Boolean-Values-Lecture-Py3.ipynb |
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| 144 Numbers-and-Boolean-Values-Solution-Py3.ipynb |
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| 145 Python Strings.en.srt |
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| 145 Strings-Exercise-Py3.ipynb |
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| 145 Strings-Lecture-Py3.ipynb |
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| 146 Arithmetic-Operators-Exercise-Py3.ipynb |
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| 146 Arithmetic-Operators-Lecture-Py3.ipynb |
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| 146 Arithmetic-Operators-Solution-Py3.ipynb |
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| 146 Using Arithmetic Operators in Python.en.srt |
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| 146 Using Arithmetic Operators in Python.mp4 |
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| 147 The Double Equality Sign.en.srt |
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| 147 The Double Equality Sign.mp4 |
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| 147 The-Double-Equality-Sign-Exercise-Py3.ipynb |
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| 147 The-Double-Equality-Sign-Lecture-Py3.ipynb |
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| 147 The-Double-Equality-Sign-Solution-Py3.ipynb |
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| 148 How to Reassign Values.en.srt |
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| 148 How to Reassign Values.mp4 |
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| 148 Reassign-Values-Exercise-Py3.ipynb |
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| 148 Reassign-Values-Lecture-Py3.ipynb |
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| 148 Reassign-Values-Solution-Py3.ipynb |
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| 149 Add Comments.en.srt |
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| 149 Add-Comments-Lecture-Py3.ipynb |
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| 150 Line-Continuation-Exercise-Py3.ipynb |
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| 150 Line-Continuation-Lecture-Py3.ipynb |
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| 150 Understanding Line Continuation.en.srt |
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| 151 Indexing Elements.en.srt |
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| 151 Indexing Elements.mp4 |
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| 151 Indexing-Elements-Exercise-Py3.ipynb |
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| 151 Indexing-Elements-Lecture-Py3.ipynb |
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| 151 Indexing-Elements-Solution-Py3.ipynb |
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| 152 Structure-Your-Code-with-Indentation-Exercise-Py3.ipynb |
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| 152 Structure-Your-Code-with-Indentation-Lecture-Py3.ipynb |
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| 152 Structuring with Indentation.en.srt |
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| 152 Structuring with Indentation.mp4 |
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| 153 Comparison Operators.en.srt |
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| 153 Comparison Operators.mp4 |
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| 153 Comparison-Operators-Exercise-Py3.ipynb |
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| 153 Comparison-Operators-Lecture-Py3.ipynb |
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| 153 Comparison-Operators-Solution-Py3.ipynb |
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| 154 Logical and Identity Operators.en.srt |
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| 154 Logical and Identity Operators.mp4 |
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| 154 Logical-and-Identity-Operators-Lecture-Py3.ipynb |
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| 154 Logical-and-Identity-Operators-Solution-Py3.ipynb |
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| 155 Introduction-to-the-If-Statement-Exercise-Py3.ipynb |
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| 155 Introduction-to-the-If-Statement-Lecture-Py3.ipynb |
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| 155 Introduction-to-the-If-Statement-Solution-Py3.ipynb |
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| 155 The IF Statement.en.srt |
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| 155 The IF Statement.mp4 |
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| 156 Add-an-Else-Statement-Exercise-Py3.ipynb |
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| 156 Add-an-Else-Statement-Lecture-Py3.ipynb |
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| 156 Add-an-Else-Statement-Solution-Py3.ipynb |
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| 156 The ELSE Statement.en.srt |
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| 156 The ELSE Statement.mp4 |
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| 157 Else-If-for-Brief-Elif-Exercise-Py3.ipynb |
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| 157 Else-If-for-Brief-Elif-Lecture-Py3.ipynb |
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| 157 Else-If-for-Brief-Elif-Solution-Py3.ipynb |
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| 157 The ELIF Statement.en.srt |
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| 157 The ELIF Statement.mp4 |
25.07MB |
| 158 A Note on Boolean Values.en.srt |
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| 158 A Note on Boolean Values.mp4 |
8.90MB |
| 158 A-Note-on-Boolean-Values-Lecture-Py3.ipynb |
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| 159 Defining a Function in Python.en.srt |
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| 159 Defining a Function in Python.mp4 |
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| 159 Defining-a-Function-in-Python-Lecture-Py3.ipynb |
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| 160 Creating-a-Function-with-a-Parameter-Exercise-Py3.ipynb |
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| 160 Creating-a-Function-with-a-Parameter-Lecture-Py3.ipynb |
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| 160 Creating-a-Function-with-a-Parameter-Solution-Py3.ipynb |
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| 160 How to Create a Function with a Parameter.en.srt |
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| 160 How to Create a Function with a Parameter.mp4 |
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| 161 Another-Way-to-Define-a-Function-Exercise-Py3.ipynb |
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| 161 Another-Way-to-Define-a-Function-Lecture-Py3.ipynb |
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| 161 Another-Way-to-Define-a-Function-Solution-Py3.ipynb |
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| 161 Defining a Function in Python - Part II.en.srt |
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| 161 Defining a Function in Python - Part II.mp4 |
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| 162 0.6.4-Using-a-Function-in-another-Function-Exercise-Py3.ipynb |
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| 162 0.6.4-Using-a-Function-in-another-Function-Lecture-Py3.ipynb |
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| 162 0.6.4-Using-a-Function-in-another-Function-Solution-Py3.ipynb |
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| 162 How to Use a Function within a Function.en.srt |
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| 162 How to Use a Function within a Function.mp4 |
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| 163 Combining-Conditional-Statements-and-Functions-Exercise-Py3.ipynb |
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| 163 Combining-Conditional-Statements-and-Functions-Lecture-Py3.ipynb |
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| 163 Combining-Conditional-Statements-and-Functions-Solution-Py3.ipynb |
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| 163 Conditional Statements and Functions.en.srt |
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| 163 Conditional Statements and Functions.mp4 |
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| 164 Creating-Functions-Containing-a-Few-Arguments-Lecture-Py3.ipynb |
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| 164 Functions Containing a Few Arguments.en.srt |
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| 164 Functions Containing a Few Arguments.mp4 |
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| 165 Built-in Functions in Python.en.srt |
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| 165 Built-in Functions in Python.mp4 |
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| 165 Notable-Built-In-Functions-in-Python-Exercise-Py3.ipynb |
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| 165 Notable-Built-In-Functions-in-Python-Lecture-Py3.ipynb |
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| 165 Notable-Built-In-Functions-in-Python-Solution-Py3.ipynb |
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| 166 Lists.en.srt |
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| 166 Lists-Exercise-Py3.ipynb |
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| 166 Lists-Lecture-Py3.ipynb |
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| 166 Lists-Solution-Py3.ipynb |
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| 167 Help-Yourself-with-Methods-Exercise-Py3.ipynb |
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| 167 Help-Yourself-with-Methods-Lecture-Py3.ipynb |
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| 167 Help-Yourself-with-Methods-Solution-Py3.ipynb |
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| 167 Using Methods.en.srt |
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| 168 List Slicing.en.srt |
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| 168 List-Slicing-Exercise-Py3.ipynb |
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| 168 List-Slicing-Lecture-Py3.ipynb |
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| 168 List-Slicing-Solution-Py3.ipynb |
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| 169 Tuples.en.srt |
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| 169 Tuples-Exercise-Py3.ipynb |
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| 169 Tuples-Solution-Py3.ipynb |
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| 170 Dictionaries.en.srt |
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| 170 Dictionaries.mp4 |
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| 170 Dictionaries-Exercise-Py3.ipynb |
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| 170 Dictionaries-Lecture-Py3.ipynb |
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| 170 Dictionaries-Solution-Py3.ipynb |
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| 171 For Loops.en.srt |
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| 171 For Loops.mp4 |
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| 171 For-Loops-Solution-Py3.ipynb |
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| 172 While Loops and Incrementing.en.srt |
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| 172 While-Loops-and-Incrementing-Exercise-Py3.ipynb |
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| 172 While-Loops-and-Incrementing-Lecture-Py3.ipynb |
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| 172 While-Loops-and-Incrementing-Solution-Py3.ipynb |
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| 173 Create-Lists-with-the-range-Function-Exercise-Py3.ipynb |
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| 173 Create-Lists-with-the-range-Function-Lecture-Py3.ipynb |
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| 173 Create-Lists-with-the-range-Function-Solution-Py3.ipynb |
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| 173 Lists with the range() Function.en.srt |
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| 173 Lists with the range() Function.mp4 |
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| 174 Conditional Statements and Loops.en.srt |
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| 174 Conditional Statements and Loops.mp4 |
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| 174 Use-Conditional-Statements-and-Loops-Together-Exercise-Py3.ipynb |
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| 174 Use-Conditional-Statements-and-Loops-Together-Lecture-Py3.ipynb |
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| 174 Use-Conditional-Statements-and-Loops-Together-Solution-Py3.ipynb |
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| 175 All-In-Exercise-Py3.ipynb |
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| 175 All-In-Lecture-Py3.ipynb |
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| 175 All-In-Solution-Py3.ipynb |
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| 175 Conditional Statements, Functions, and Loops.en.srt |
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| 175 Conditional Statements, Functions, and Loops.mp4 |
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| 176 How to Iterate over Dictionaries.en.srt |
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| 176 How to Iterate over Dictionaries.mp4 |
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| 176 Iterating-over-Dictionaries-Exercise-Py3.ipynb |
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| 176 Iterating-over-Dictionaries-Lecture-Py3.ipynb |
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| 176 Iterating-over-Dictionaries-Solution-Py3.ipynb |
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| 177 Object Oriented Programming.en.srt |
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| 177 Object Oriented Programming.mp4 |
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| 178 Modules and Packages.en.srt |
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| 178 Modules and Packages.mp4 |
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| 179 What is the Standard Library_.en.srt |
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| 179 What is the Standard Library_.mp4 |
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| 180 Importing Modules in Python.en.srt |
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| 180 Importing Modules in Python.mp4 |
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| 181 Course-notes-regression-analysis.pdf |
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| 181 Introduction to Regression Analysis.en.srt |
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| 181 Introduction to Regression Analysis.mp4 |
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| 182 Course-notes-regression-analysis.pdf |
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| 182 The Linear Regression Model.en.srt |
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| 182 The Linear Regression Model.mp4 |
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| 183 Correlation vs Regression.en.srt |
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| 183 Correlation vs Regression.mp4 |
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| 184 Geometrical Representation of the Linear Regression Model.en.srt |
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| 184 Geometrical Representation of the Linear Regression Model.mp4 |
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| 185 Python Packages Installation.en.srt |
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| 185 Python Packages Installation.mp4 |
40.58MB |
| 186 1.01.Simple-linear-regression.csv |
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| 186 First Regression in Python.en.srt |
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| 186 First Regression in Python.mp4 |
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| 186 Simple-linear-regression.ipynb |
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| 186 Simple-linear-regression-with-comments.ipynb |
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| 187 First Regression in Python Exercise.html |
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| 187 real-estate-price-size.csv |
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| 187 Simple-Linear-Regression-Exercise.ipynb |
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| 187 Simple-Linear-Regression-Exercise-Solution.ipynb |
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| 188 Using Seaborn for Graphs.en.srt |
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| 188 Using Seaborn for Graphs.mp4 |
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| 189 How to Interpret the Regression Table.en.srt |
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| 189 How to Interpret the Regression Table.mp4 |
44.64MB |
| 190 Decomposition of Variability.en.srt |
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| 190 Decomposition of Variability.mp4 |
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| 191 What is the OLS_.en.srt |
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| 191 What is the OLS_.mp4 |
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| 192 R-Squared.en.srt |
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| 192 R-Squared.mp4 |
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| 193 Multiple Linear Regression.en.srt |
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| 193 Multiple Linear Regression.mp4 |
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| 194 1.02.Multiple-linear-regression.csv |
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| 194 Adjusted R-Squared.en.srt |
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| 194 Adjusted R-Squared.mp4 |
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| 194 Multiple-linear-regression-and-Adjusted-R-squared.ipynb |
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| 194 Multiple-linear-regression-and-Adjusted-R-squared-with-comments.ipynb |
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| 195 Multiple Linear Regression Exercise.html |
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| 195 Multiple-Linear-Regression-Exercise.ipynb |
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| 195 Multiple-Linear-Regression-Exercise-Solution.ipynb |
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| 195 real-estate-price-size-year.csv |
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| 196 Test for Significance of the Model (F-Test).en.srt |
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| 196 Test for Significance of the Model (F-Test).mp4 |
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| 197 OLS Assumptions.en.srt |
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| 197 OLS Assumptions.mp4 |
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| 198 A1_ Linearity.en.srt |
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| 198 A1_ Linearity.mp4 |
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| 199 A2_ No Endogeneity.en.srt |
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| 199 A2_ No Endogeneity.mp4 |
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| 200 A3_ Normality and Homoscedasticity.en.srt |
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| 200 A3_ Normality and Homoscedasticity.mp4 |
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| 201 A4_ No Autocorrelation.en.srt |
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| 201 A4_ No Autocorrelation.mp4 |
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| 202 A5_ No Multicollinearity.en.srt |
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| 202 A5_ No Multicollinearity.mp4 |
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| 203 1.03.Dummies.csv |
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| 203 Dealing with Categorical Data - Dummy Variables.en.srt |
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| 203 Dealing with Categorical Data - Dummy Variables.mp4 |
55.66MB |
| 203 Dummy-Variables.ipynb |
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| 203 Dummy-variables-with-comments.ipynb |
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| 204 Dealing with Categorical Data - Dummy Variables.html |
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| 204 Multiple-Linear-Regression-with-Dummies-Exercise.ipynb |
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| 204 Multiple-Linear-Regression-with-Dummies-Exercise-Solution.ipynb |
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| 204 real-estate-price-size-year-view.csv |
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| 205 Making-predictions.ipynb |
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| 205 Making-predictions-with-comments.ipynb |
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| 205 Making Predictions with the Linear Regression.en.srt |
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| 205 Making Predictions with the Linear Regression.mp4 |
24.69MB |
| 206 What is sklearn and How is it Different from Other Packages.en.srt |
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| 206 What is sklearn and How is it Different from Other Packages.mp4 |
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| 207 How are we Going to Approach this Section_.en.srt |
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| 207 How are we Going to Approach this Section_.mp4 |
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| 208 1.01.Simple-linear-regression.csv |
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| 208 Simple Linear Regression with sklearn.en.srt |
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| 208 Simple Linear Regression with sklearn.mp4 |
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| 208 sklearn-Simple-Linear-Regression.ipynb |
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| 208 sklearn-Simple-Linear-Regression-with-comments.ipynb |
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| 209 1.01.Simple-linear-regression.csv |
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| 209 Simple Linear Regression with sklearn - A StatsModels-like Summary Table.en.srt |
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| 209 Simple Linear Regression with sklearn - A StatsModels-like Summary Table.mp4 |
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| 209 sklearn-Simple-Linear-Regression.ipynb |
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| 209 sklearn-Simple-Linear-Regression-with-comments.ipynb |
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| 210 A Note on Normalization.html |
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| 211 1.01.Simple-linear-regression.csv |
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| 211 Simple Linear Regression with sklearn - Exercise.html |
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| 211 sklearn-Simple-Linear-Regression.ipynb |
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| 211 sklearn-Simple-Linear-Regression-with-comments.ipynb |
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| 212 1.02.Multiple-linear-regression.csv |
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| 212 Multiple Linear Regression with sklearn.en.srt |
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| 212 Multiple Linear Regression with sklearn.mp4 |
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| 212 sklearn-Multiple-Linear-Regression.ipynb |
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| 212 sklearn-Multiple-Linear-Regression-with-comments.ipynb |
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| 213 1.02.Multiple-linear-regression.csv |
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| 213 Calculating the Adjusted R-Squared in sklearn.en.srt |
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| 213 Calculating the Adjusted R-Squared in sklearn.mp4 |
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| 213 sklearn-Multiple-Linear-Regression-and-Adjusted-R-squared.ipynb |
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| 213 sklearn-Multiple-Linear-Regression-and-Adjusted-R-squared-with-comments.ipynb |
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| 214 1.02.Multiple-linear-regression.csv |
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| 214 Calculating the Adjusted R-Squared in sklearn - Exercise.html |
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| 214 sklearn-Multiple-Linear-Regression-and-Adjusted-R-squared-Exercise.ipynb |
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| 214 sklearn-Multiple-Linear-Regression-and-Adjusted-R-squared-Exercise-Solution.ipynb |
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| 215 Feature Selection (F-regression).mp4 |
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| 215 sklearn-Feature-Selection-with-F-regression.ipynb |
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| 215 sklearn-Feature-Selection-with-F-regression-with-comments.ipynb |
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| 216 1.02.Multiple-linear-regression.csv |
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| 216 A Note on Calculation of P-values with sklearn.html |
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| 217 1.02.Multiple-linear-regression.csv |
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| 217 Creating a Summary Table with P-values.en.srt |
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| 218 Multiple Linear Regression - Exercise.html |
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| 223 Underfitting and Overfitting.en.srt |
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| 224 sklearn-Train-Test-Split.ipynb |
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| 224 Train - Test Split Explained.en.srt |
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| 225 1.04.Real-life-example.csv |
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| 225 Practical Example_ Linear Regression (Part 1).en.srt |
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| 225 Practical Example_ Linear Regression (Part 1).mp4 |
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| 225 sklearn-Linear-Regression-Practical-Example-Part-1.ipynb |
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| 226 Practical Example_ Linear Regression (Part 2).en.srt |
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| 226 sklearn-Linear-Regression-Practical-Example-Part-2.ipynb |
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| 227 A Note on Multicollinearity.html |
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| 228 Practical Example_ Linear Regression (Part 3).en.srt |
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| 228 sklearn-Linear-Regression-Practical-Example-Part-3.ipynb |
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| 229 1.04.Real-life-example.csv |
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| 229 Dummies and Variance Inflation Factor - Exercise.html |
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| 229 sklearn-Dummies-and-VIF-Exercise-Solution.ipynb |
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| 230 Practical Example_ Linear Regression (Part 4).en.srt |
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| 230 sklearn-Linear-Regression-Practical-Example-Part-4.ipynb |
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| 231 Dummy Variables - Exercise.html |
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| 232 Practical Example_ Linear Regression (Part 5).en.srt |
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| 232 sklearn-Linear-Regression-Practical-Example-Part-5.ipynb |
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| 232 sklearn-Linear-Regression-Practical-Example-Part-5-with-comments.ipynb |
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| 233 Linear Regression - Exercise.html |
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| 234 Course-Notes-Logistic-Regression.pdf |
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| 234 Introduction to Logistic Regression.en.srt |
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| 234 Introduction to Logistic Regression.mp4 |
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| 235 2.01.Admittance.csv |
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| 235 Admittance.ipynb |
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| 235 Admittance-with-comments.ipynb |
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| 235 A Simple Example in Python.en.srt |
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| 235 Course-Notes-Logistic-Regression.pdf |
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| 236 Logistic vs Logit Function.en.srt |
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| 236 Logistic vs Logit Function.mp4 |
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| 237 Admittance-regression.ipynb |
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| 237 Admittance-regression-summary-error.ipynb |
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| 237 Admittance-regression-tables-fixed-error.ipynb |
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| 237 Building a Logistic Regression.en.srt |
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| 237 Building a Logistic Regression.mp4 |
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| 238 Building a Logistic Regression - Exercise.html |
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| 238 Building-a-Logistic-Regression-Exercise.ipynb |
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| 238 Building-a-Logistic-Regression-Solution.ipynb |
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| 238 Example-bank-data.csv |
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| 239 An Invaluable Coding Tip.en.srt |
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| 239 An Invaluable Coding Tip.mp4 |
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| 240 Understanding Logistic Regression Tables.en.srt |
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| 241 Bank-data.csv |
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| 241 Understanding Logistic Regression Tables - Exercise.html |
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| 241 Understanding-Logistic-Regression-Tables-Exercise.ipynb |
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| 241 Understanding-Logistic-Regression-Tables-Solution.ipynb |
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| 242 What do the Odds Actually Mean.en.srt |
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| 243 2.02.Binary-predictors.csv |
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| 243 Binary Predictors in a Logistic Regression.en.srt |
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| 244 Binary Predictors in a Logistic Regression - Exercise.html |
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| 245 Accuracy.ipynb |
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| 245 Calculating the Accuracy of the Model.en.srt |
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| 246 Bank-data.csv |
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| 246 Calculating the Accuracy of the Model.html |
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| 246 Calculating-the-Accuracy-of-the-Model-Exercise.ipynb |
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| 247 Underfitting and Overfitting.en.srt |
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| 248 2.03.Test-dataset.csv |
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| 248 Testing the Model.en.srt |
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| 249 Testing the Model - Exercise.html |
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| 249 Testing-the-Model-Solution.ipynb |
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| 250 Course-Notes-Cluster-Analysis.pdf |
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| 250 Introduction to Cluster Analysis.en.srt |
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| 250 Introduction to Cluster Analysis.mp4 |
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| 251 Course-Notes-Cluster-Analysis.pdf |
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| 251 Some Examples of Clusters.en.srt |
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| 251 Some Examples of Clusters.mp4 |
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| 252 Difference between Classification and Clustering.en.srt |
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| 252 Difference between Classification and Clustering.mp4 |
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| 253 Math Prerequisites.en.srt |
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| 254 K-Means Clustering.en.srt |
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| 254 K-Means Clustering.mp4 |
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| 255 3.01.Country-clusters.csv |
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| 255 A Simple Example of Clustering.en.srt |
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| 255 A Simple Example of Clustering.mp4 |
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| 255 Country-clusters.ipynb |
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| 256 A Simple Example of Clustering - Exercise.html |
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| 257 Categorical-data.ipynb |
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| 257 Clustering Categorical Data.en.srt |
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| 257 Clustering Categorical Data.mp4 |
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| 258 Categorical.csv |
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| 258 Clustering Categorical Data - Exercise.html |
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| 258 Clustering-Categorical-Data-Solution.ipynb |
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| 259 How to Choose the Number of Clusters.en.srt |
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| 259 How to Choose the Number of Clusters.mp4 |
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| 259 Selecting-the-number-of-clusters.ipynb |
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| 260 How to Choose the Number of Clusters - Exercise.html |
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| 260 How-to-Choose-the-Number-of-Clusters-Exercise.ipynb |
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| 260 How-to-Choose-the-Number-of-Clusters-Solution.ipynb |
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| 261 Pros and Cons of K-Means Clustering.en.srt |
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| 261 Pros and Cons of K-Means Clustering.mp4 |
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| 262 To Standardize or not to Standardize.en.srt |
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| 263 Relationship between Clustering and Regression.en.srt |
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| 263 Relationship between Clustering and Regression.mp4 |
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| 264 3.12.Example.csv |
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| 264 Market-segmentation-example.ipynb |
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| 264 Market-segmentation-example-with-comments.ipynb |
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| 264 Market Segmentation with Cluster Analysis (Part 1).en.srt |
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| 264 Market Segmentation with Cluster Analysis (Part 1).mp4 |
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| 265 Market-segmentation-example-Part2.ipynb |
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| 265 Market Segmentation with Cluster Analysis (Part 2).en.srt |
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| 265 Market Segmentation with Cluster Analysis (Part 2).mp4 |
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| 266 How is Clustering Useful_.en.srt |
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| 266 How is Clustering Useful_.mp4 |
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| 267 EXERCISE_ Species Segmentation with Cluster Analysis (Part 1).html |
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| 267 Species-Segmentation-with-Cluster-Analysis-Part-1-Exercise.ipynb |
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| 268 EXERCISE_ Species Segmentation with Cluster Analysis (Part 2).html |
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| 268 Species-Segmentation-with-Cluster-Analysis-Part-2-Exercise.ipynb |
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| 268 Species-Segmentation-with-Cluster-Analysis-Part-2-Solution.ipynb |
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| 269 Types of Clustering.en.srt |
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| 269 Types of Clustering.mp4 |
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| 270 Dendrogram.en.srt |
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| 271 Country-clusters-standardized.csv |
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| 271 Heatmaps.en.srt |
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| 272 What is a Matrix_.en.srt |
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| 272 What is a Matrix_.mp4 |
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| 273 Scalars and Vectors.en.srt |
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| 274 Linear Algebra and Geometry.en.srt |
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| 275 Arrays in Python - A Convenient Way To Represent Matrices.en.srt |
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| 275 Scalars-Vectors-and-Matrices.ipynb |
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| 276 Tensors.ipynb |
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| 276 What is a Tensor_.en.srt |
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| 277 Adding-and-subtracting-matrices.ipynb |
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| 278 Errors when Adding Matrices.en.srt |
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| 278 Errors-when-adding-scalars-vectors-and-matrices-in-Python.ipynb |
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| 280 Dot Product.en.srt |
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| 281 Dot Product of Matrices.en.srt |
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| 281 Dot-product-Part-2.ipynb |
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| 282 Why is Linear Algebra Useful_.en.srt |
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| 282 Why is Linear Algebra Useful_.mp4 |
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| 283 What to Expect from this Part_.en.srt |
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| 283 What to Expect from this Part_.mp4 |
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| 284 Course-Notes-Section-2.pdf |
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| 284 Introduction to Neural Networks.en.srt |
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| 284 Introduction to Neural Networks.mp4 |
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| 285 Course-Notes-Section-2.pdf |
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| 285 Training the Model.en.srt |
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| 286 Types of Machine Learning.en.srt |
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| 286 Types of Machine Learning.mp4 |
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| 287 The Linear Model (Linear Algebraic Version).en.srt |
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| 288 The Linear Model with Multiple Inputs.en.srt |
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| 289 The Linear model with Multiple Inputs and Multiple Outputs.en.srt |
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| 290 Graphical Representation of Simple Neural Networks.en.srt |
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| 290 Graphical Representation of Simple Neural Networks.mp4 |
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| 291 What is the Objective Function_.en.srt |
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| 291 What is the Objective Function_.mp4 |
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| 292 Common Objective Functions_ L2-norm Loss.en.srt |
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| 293 Common Objective Functions_ Cross-Entropy Loss.en.srt |
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| 294 GD-function-example.xlsx |
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| 294 Optimization Algorithm_ 1-Parameter Gradient Descent.en.srt |
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| 295 Optimization Algorithm_ n-Parameter Gradient Descent.en.srt |
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| 296 Basic NN Example (Part 1).en.srt |
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| 296 Minimal-example-Part-1.ipynb |
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| 297 Basic NN Example (Part 2).en.srt |
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| 299 Minimal-example-Part-4-Complete.ipynb |
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| 300 Basic NN Example Exercises.html |
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| 300 Minimal-example-Exercise-1-Solution.ipynb |
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| 301 How to Install TensorFlow 2.0.en.srt |
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| 301 Shortcuts-for-Jupyter.pdf |
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| 302 TensorFlow Outline and Comparison with Other Libraries.en.srt |
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| 303 TensorFlow 1 vs TensorFlow 2.en.srt |
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| 304 A Note on TensorFlow 2 Syntax.en.srt |
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| 305 TensorFlow-Minimal-example-Part1.ipynb |
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| 306 Outlining the Model with TensorFlow 2.en.srt |
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| 306 TensorFlow-Minimal-example-Part2.ipynb |
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| 307 Interpreting the Result and Extracting the Weights and Bias.en.srt |
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| 307 TensorFlow-Minimal-example-Part3.ipynb |
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| 308 Customizing a TensorFlow 2 Model.en.srt |
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| 308 TensorFlow-Minimal-example-complete.ipynb |
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| 309 Basic NN with TensorFlow_ Exercises.html |
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| 309 TensorFlow-Minimal-example-All-exercises.ipynb |
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| 309 TensorFlow-Minimal-Example-Exercise-3-Solution.ipynb |
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| 310 Course-Notes-Section-6.pdf |
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| 310 What is a Layer_.en.srt |
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| 311 Course-Notes-Section-6.pdf |
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| 311 What is a Deep Net_.en.srt |
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| 312 Digging into a Deep Net.en.srt |
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| 313 Non-Linearities and their Purpose.en.srt |
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| 314 Activation Functions.en.srt |
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| 315 Activation Functions_ Softmax Activation.en.srt |
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| 316 Backpropagation.en.srt |
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| 317 Backpropagation Picture.en.srt |
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| 318 Backpropagation - A Peek into the Mathematics of Optimization.html |
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| 318 Backpropagation-a-peek-into-the-Mathematics-of-Optimization.pdf |
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| 319 What is Overfitting_.en.srt |
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| 320 Underfitting and Overfitting for Classification.en.srt |
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| 321 What is Validation_.en.srt |
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| 322 Training, Validation, and Test Datasets.en.srt |
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| 323 N-Fold Cross Validation.en.srt |
4.33KB |
| 323 N-Fold Cross Validation.mp4 |
20.70MB |
| 324 Early Stopping or When to Stop Training.en.srt |
7.12KB |
| 324 Early Stopping or When to Stop Training.mp4 |
24.17MB |
| 325 What is Initialization_.en.srt |
3.64KB |
| 325 What is Initialization_.mp4 |
21.76MB |
| 326 Types of Simple Initializations.en.srt |
3.81KB |
| 326 Types of Simple Initializations.mp4 |
14.31MB |
| 327 State-of-the-Art Method - (Xavier) Glorot Initialization.en.srt |
3.85KB |
| 327 State-of-the-Art Method - (Xavier) Glorot Initialization.mp4 |
17.14MB |
| 328 Stochastic Gradient Descent.en.srt |
5.01KB |
| 328 Stochastic Gradient Descent.mp4 |
28.68MB |
| 329 Problems with Gradient Descent.en.srt |
2.93KB |
| 329 Problems with Gradient Descent.mp4 |
11.01MB |
| 330 Momentum.en.srt |
3.57KB |
| 330 Momentum.mp4 |
16.43MB |
| 331 Learning Rate Schedules, or How to Choose the Optimal Learning Rate.en.srt |
6.16KB |
| 331 Learning Rate Schedules, or How to Choose the Optimal Learning Rate.mp4 |
29.08MB |
| 332 Learning Rate Schedules Visualized.en.srt |
2.25KB |
| 332 Learning Rate Schedules Visualized.mp4 |
9.11MB |
| 333 Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).en.srt |
5.41KB |
| 333 Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).mp4 |
26.35MB |
| 334 Adam (Adaptive Moment Estimation).en.srt |
3.46KB |
| 334 Adam (Adaptive Moment Estimation).mp4 |
22.35MB |
| 335 Preprocessing Introduction.en.srt |
4.02KB |
| 335 Preprocessing Introduction.mp4 |
27.78MB |
| 336 Types of Basic Preprocessing.en.srt |
1.69KB |
| 336 Types of Basic Preprocessing.mp4 |
11.84MB |
| 337 Standardization.en.srt |
6.18KB |
| 337 Standardization.mp4 |
50.98MB |
| 338 Preprocessing Categorical Data.en.srt |
2.87KB |
| 338 Preprocessing Categorical Data.mp4 |
18.60MB |
| 339 Binary and One-Hot Encoding.en.srt |
5.01KB |
| 339 Binary and One-Hot Encoding.mp4 |
28.94MB |
| 340 MNIST_ The Dataset.en.srt |
3.72KB |
| 340 MNIST_ The Dataset.mp4 |
13.38MB |
| 341 MNIST_ How to Tackle the MNIST.en.srt |
3.64KB |
| 341 MNIST_ How to Tackle the MNIST.mp4 |
18.66MB |
| 342 MNIST_ Importing the Relevant Packages and Loading the Data.en.srt |
3.20KB |
| 342 MNIST_ Importing the Relevant Packages and Loading the Data.mp4 |
16.32MB |
| 342 TensorFlow-MNIST-Part1-with-comments.ipynb |
3.97KB |
| 343 MNIST_ Preprocess the Data - Create a Validation Set and Scale It.en.srt |
6.50KB |
| 343 MNIST_ Preprocess the Data - Create a Validation Set and Scale It.mp4 |
29.04MB |
| 344 MNIST_ Preprocess the Data - Scale the Test Data - Exercise.html |
1013B |
| 344 TensorFlow-MNIST-Part2-with-comments.ipynb |
6.39KB |
| 345 MNIST_ Preprocess the Data - Shuffle and Batch.en.srt |
9.61KB |
| 345 MNIST_ Preprocess the Data - Shuffle and Batch.mp4 |
41.52MB |
| 346 MNIST_ Preprocess the Data - Shuffle and Batch - Exercise.html |
1011B |
| 346 TensorFlow-MNIST-Part3-with-comments.ipynb |
8.61KB |
| 347 MNIST_ Outline the Model.en.srt |
7.49KB |
| 347 MNIST_ Outline the Model.mp4 |
28.23MB |
| 347 TensorFlow-MNIST-Part4-with-comments.ipynb |
10.49KB |
| 348 MNIST_ Select the Loss and the Optimizer.en.srt |
3.14KB |
| 348 MNIST_ Select the Loss and the Optimizer.mp4 |
13.90MB |
| 348 TensorFlow-MNIST-Part5-with-comments.ipynb |
10.99KB |
| 349 MNIST_ Learning.en.srt |
8.27KB |
| 349 MNIST_ Learning.mp4 |
40.96MB |
| 349 TensorFlow-MNIST-Part6-with-comments.ipynb |
12.54KB |
| 350 1.TensorFlow-MNIST-Width-Solution.ipynb |
14.84KB |
| 350 2.TensorFlow-MNIST-Depth-Solution.ipynb |
15.31KB |
| 350 3.TensorFlow-MNIST-Width-and-Depth-Solution.ipynb |
15.30KB |
| 350 4.TensorFlow-MNIST-Activation-functions-Part-1-Solution.ipynb |
15.11KB |
| 350 5.TensorFlow-MNIST-Activation-functions-Part-2-Solution.ipynb |
14.74KB |
| 350 6.TensorFlow-MNIST-Batch-size-Part-1-Solution.ipynb |
15.12KB |
| 350 7.TensorFlow-MNIST-Batch-size-Part-2-Solution.ipynb |
15.18KB |
| 350 8.TensorFlow-MNIST-Learning-rate-Part-1-Solution.ipynb |
20.58KB |
| 350 9.TensorFlow-MNIST-Learning-rate-Part-2-Solution.ipynb |
15.80KB |
| 350 MNIST - Exercises.html |
2.85KB |
| 350 TensorFlow-MNIST-All-Exercises.ipynb |
16.65KB |
| 350 TensorFlow-MNIST-around-98-percent-accuracy.ipynb |
15.02KB |
| 351 MNIST_ Testing the Model.en.srt |
6.26KB |
| 351 MNIST_ Testing the Model.mp4 |
29.52MB |
| 351 TensorFlow-MNIST-complete.ipynb |
6.78KB |
| 351 TensorFlow-MNIST-complete-with-comments.ipynb |
14.51KB |
| 352 Audiobooks-data.csv |
710.77KB |
| 352 Business Case_ Exploring the Dataset and Identifying Predictors.en.srt |
11.08KB |
| 352 Business Case_ Exploring the Dataset and Identifying Predictors.mp4 |
66.27MB |
| 353 Business Case_ Outlining the Solution.en.srt |
2.08KB |
| 353 Business Case_ Outlining the Solution.mp4 |
7.30MB |
| 354 Business Case_ Balancing the Dataset.en.srt |
4.67KB |
| 354 Business Case_ Balancing the Dataset.mp4 |
30.43MB |
| 355 Business Case_ Preprocessing the Data.en.srt |
12.71KB |
| 355 Business Case_ Preprocessing the Data.mp4 |
84.33MB |
| 355 TensorFlow-Audiobooks-Preprocessing.ipynb |
5.58KB |
| 355 TensorFlow-Audiobooks-Preprocessing-with-comments.ipynb |
11.19KB |
| 356 Business Case_ Preprocessing the Data - Exercise.html |
1.26KB |
| 356 TensorFlow-Audiobooks-Preprocessing-Exercise.ipynb |
8.60KB |
| 356 TensorFlow-Audiobooks-Preprocessing-Exercise-Solution.ipynb |
10.04KB |
| 357 Business Case_ Load the Preprocessed Data.en.srt |
4.89KB |
| 357 Business Case_ Load the Preprocessed Data.mp4 |
17.57MB |
| 358 Business Case_ Load the Preprocessed Data - Exercise.html |
1006B |
| 358 TensorFlow-Audiobooks-Machine-Learning-Part1-with-comments.ipynb |
4.61KB |
| 359 Business Case_ Learning and Interpreting the Result.en.srt |
6.53KB |
| 359 Business Case_ Learning and Interpreting the Result.mp4 |
31.18MB |
| 359 TensorFlow-Audiobooks-Machine-Learning-Part2-with-comments.ipynb |
19.69KB |
| 360 Business Case_ Setting an Early Stopping Mechanism.en.srt |
8.11KB |
| 360 Business Case_ Setting an Early Stopping Mechanism.mp4 |
49.81MB |
| 360 TensorFlow-Audiobooks-Machine-Learning-Part3-with-comments.ipynb |
10.06KB |
| 361 Setting an Early Stopping Mechanism - Exercise.html |
1.09KB |
| 362 Business Case_ Testing the Model.en.srt |
2.12KB |
| 362 Business Case_ Testing the Model.mp4 |
10.79MB |
| 362 TensorFlow-Audiobooks-Machine-Learning-with-comments.ipynb |
11.95KB |
| 363 Business Case_ Final Exercise.html |
1.31KB |
| 363 TensorFlow-Audiobooks-Machine-Learning-with-comments.ipynb |
11.95KB |
| 364 Summary on What You've Learned.en.srt |
5.41KB |
| 364 Summary on What You've Learned.mp4 |
39.75MB |
| 365 What's Further out there in terms of Machine Learning.en.srt |
2.64KB |
| 365 What's Further out there in terms of Machine Learning.mp4 |
20.12MB |
| 366 DeepMind and Deep Learning.html |
1.93KB |
| 367 An overview of CNNs.en.srt |
6.68KB |
| 367 An overview of CNNs.mp4 |
58.79MB |
| 368 An Overview of RNNs.en.srt |
3.83KB |
| 368 An Overview of RNNs.mp4 |
25.26MB |
| 369 An Overview of non-NN Approaches.en.srt |
5.29KB |
| 369 An Overview of non-NN Approaches.mp4 |
44.77MB |
| 370 READ ME!!!!.html |
1.42KB |
| 371 How to Install TensorFlow 1.en.srt |
3.56KB |
| 371 How to Install TensorFlow 1.mp4 |
11.35MB |
| 372 A Note on Installing Packages in Anaconda.html |
3.35KB |
| 373 TensorFlow Intro.en.srt |
5.40KB |
| 373 TensorFlow Intro.mp4 |
47.69MB |
| 374 Actual Introduction to TensorFlow.en.srt |
2.25KB |
| 374 Actual Introduction to TensorFlow.mp4 |
17.41MB |
| 374 Shortcuts-for-Jupyter.pdf |
619.17KB |
| 375 5.3.TensorFlow-Minimal-example-Part-1.ipynb |
3.36KB |
| 375 Types of File Formats, supporting Tensors.en.srt |
3.59KB |
| 375 Types of File Formats, supporting Tensors.mp4 |
20.34MB |
| 376 5.4.TensorFlow-Minimal-example-Part-2.ipynb |
6.17KB |
| 376 Basic NN Example with TF_ Inputs, Outputs, Targets, Weights, Biases.en.srt |
7.63KB |
| 376 Basic NN Example with TF_ Inputs, Outputs, Targets, Weights, Biases.mp4 |
38.49MB |
| 377 5.5.TensorFlow-Minimal-example-Part-3.ipynb |
8.65KB |
| 377 Basic NN Example with TF_ Loss Function and Gradient Descent.en.srt |
5.02KB |
| 377 Basic NN Example with TF_ Loss Function and Gradient Descent.mp4 |
32.51MB |
| 378 5.6.TensorFlow-Minimal-example-complete.ipynb |
12.15KB |
| 378 Basic NN Example with TF_ Model Output.en.srt |
8.26KB |
| 378 Basic NN Example with TF_ Model Output.mp4 |
37.39MB |
| 379 Basic NN Example with TF Exercises.html |
2.47KB |
| 379 TensorFlow-Minimal-Example-All-Exercises.ipynb |
13.97KB |
| 379 TensorFlow-Minimal-Example-Exercise-1-Solution.ipynb |
23.63KB |
| 379 TensorFlow-Minimal-Example-Exercise-2-1-Solution.ipynb |
25.54KB |
| 379 TensorFlow-Minimal-Example-Exercise-2-2-Solution.ipynb |
25.51KB |
| 379 TensorFlow-Minimal-Example-Exercise-2-3-Solution.ipynb |
49.96KB |
| 379 TensorFlow-Minimal-Example-Exercise-2-4-Solution.ipynb |
21.75KB |
| 379 TensorFlow-Minimal-Example-Exercise-3-Solution.ipynb |
26.71KB |
| 379 TensorFlow-Minimal-Example-Exercise-4-Solution.ipynb |
26.98KB |
| 380 MNIST_ What is the MNIST Dataset_.en.srt |
3.63KB |
| 380 MNIST_ What is the MNIST Dataset_.mp4 |
17.82MB |
| 381 MNIST_ How to Tackle the MNIST.en.srt |
3.76KB |
| 381 MNIST_ How to Tackle the MNIST.mp4 |
22.58MB |
| 382 12.3.TensorFlow-MNIST-with-comments-Part-1.ipynb |
3.89KB |
| 382 MNIST_ Relevant Packages.en.srt |
2.19KB |
| 382 MNIST_ Relevant Packages.mp4 |
18.90MB |
| 383 12.4.TensorFlow-MNIST-with-comments-Part-2.ipynb |
6.10KB |
| 383 MNIST_ Model Outline.en.srt |
9.42KB |
| 383 MNIST_ Model Outline.mp4 |
56.38MB |
| 384 12.5.TensorFlow-MNIST-with-comments-Part-3.ipynb |
7.31KB |
| 384 MNIST_ Loss and Optimization Algorithm.en.srt |
3.67KB |
| 384 MNIST_ Loss and Optimization Algorithm.mp4 |
25.86MB |
| 385 12.6.TensorFlow-MNIST-with-comments-Part-4.ipynb |
7.90KB |
| 385 Calculating the Accuracy of the Model.en.srt |
5.40KB |
| 385 Calculating the Accuracy of the Model.mp4 |
43.90MB |
| 386 12.7.TensorFlow-MNIST-with-comments-Part-5.ipynb |
8.53KB |
| 386 MNIST_ Batching and Early Stopping.en.srt |
3.04KB |
| 386 MNIST_ Batching and Early Stopping.mp4 |
12.85MB |
| 387 12.8.TensorFlow-MNIST-with-comments-Part-6.ipynb |
11.50KB |
| 387 MNIST_ Learning.en.srt |
10.60KB |
| 387 MNIST_ Learning.mp4 |
46.68MB |
| 388 12.9.TensorFlow-MNIST-with-comments.ipynb |
13.03KB |
| 388 MNIST_ Results and Testing.en.srt |
8.49KB |
| 388 MNIST_ Results and Testing.mp4 |
62.77MB |
| 389 MNIST_ Exercises.html |
3.00KB |
| 389 TensorFlow-MNIST-Exercises-All.ipynb |
15.47KB |
| 390 0.TensorFlow-MNIST-take-note-of-time-Solution.ipynb |
14.00KB |
| 390 1.TensorFlow-MNIST-Width-Solution.ipynb |
14.01KB |
| 390 2.TensorFlow-MNIST-Depth-Solution.ipynb |
14.87KB |
| 390 3.TensorFlow-MNIST-Width-and-Depth-Solution.ipynb |
16.81KB |
| 390 4.TensorFlow-MNIST-Activation-functions-Part-1-Solution.ipynb |
14.35KB |
| 390 5.TensorFlow-MNIST-Activation-functions-Part-2-Solution.ipynb |
13.93KB |
| 390 6.TensorFlow-MNIST-Batch-size-Part-1-Solution.ipynb |
14.26KB |
| 390 7.TensorFlow-MNIST-Batch-size-Part-2-Solution.ipynb |
14.16KB |
| 390 8.TensorFlow-MNIST-Learning-rate-Part-1-Solution.ipynb |
14.07KB |
| 390 9.TensorFlow-MNIST-Learning-rate-Part-2-Solution.ipynb |
15.21KB |
| 390 MNIST_ Solutions.html |
3.06KB |
| 390 TensorFlow-MNIST-around-98-percent-accuracy.ipynb |
17.66KB |
| 391 Audiobooks-data.csv |
710.77KB |
| 391 Business Case_ Getting Acquainted with the Dataset.en.srt |
11.22KB |
| 391 Business Case_ Getting Acquainted with the Dataset.mp4 |
87.65MB |
| 392 Business Case_ Outlining the Solution.en.srt |
2.62KB |
| 392 Business Case_ Outlining the Solution.mp4 |
12.21MB |
| 393 Audiobooks-data.csv |
710.77KB |
| 393 The Importance of Working with a Balanced Dataset.en.srt |
4.66KB |
| 393 The Importance of Working with a Balanced Dataset.mp4 |
39.41MB |
| 394 Audiobooks-data.csv |
710.77KB |
| 394 Business Case_ Preprocessing.en.srt |
13.98KB |
| 394 Business Case_ Preprocessing.mp4 |
103.41MB |
| 394 TensorFlow-Audiobooks-Preprocessing.ipynb |
5.58KB |
| 394 TensorFlow-Audiobooks-Preprocessing-with-comments.ipynb |
11.19KB |
| 395 Audiobooks-data.csv |
710.77KB |
| 395 Business Case_ Preprocessing Exercise.html |
1.26KB |
| 395 TensorFlow-Audiobooks-Preprocessing-Exercise.ipynb |
8.60KB |
| 395 TensorFlow-Audiobooks-Preprocessing-Exercise-Solution.ipynb |
10.03KB |
| 396 Creating a Data Provider.en.srt |
8.05KB |
| 396 Creating a Data Provider.mp4 |
76.34MB |
| 397 Business Case_ Model Outline.en.srt |
7.21KB |
| 397 Business Case_ Model Outline.mp4 |
53.12MB |
| 397 TensorFlow-Audiobooks-Outlining-the-model.ipynb |
9.36KB |
| 397 TensorFlow-Audiobooks-Outlining-the-model-with-comments.ipynb |
10.34KB |
| 398 Business Case_ Optimization.en.srt |
6.86KB |
| 398 Business Case_ Optimization.mp4 |
41.52MB |
| 398 TensorFlow-Audiobooks-optimizing-the-algorithm.ipynb |
10.64KB |
| 398 TensorFlow-Audiobooks-optimizing-the-algorithm-with-comments.ipynb |
12.73KB |
| 399 Business Case_ Interpretation.en.srt |
3.05KB |
| 399 Business Case_ Interpretation.mp4 |
25.74MB |
| 399 TensorFlow-Audiobooks-optimizing-the-algorithm.ipynb |
10.64KB |
| 399 TensorFlow-Audiobooks-optimizing-the-algorithm-with-comments.ipynb |
12.73KB |
| 400 Business Case_ Testing the Model.en.srt |
2.82KB |
| 400 Business Case_ Testing the Model.mp4 |
11.20MB |
| 401 Audiobooks-data.csv |
710.77KB |
| 401 Business Case_ A Comment on the Homework.en.srt |
5.50KB |
| 401 Business Case_ A Comment on the Homework.mp4 |
36.38MB |
| 401 TensorFlow-Audiobooks-Machine-learning-Homework.ipynb |
14.40KB |
| 401 TensorFlow-Audiobooks-Preprocessing-with-comments.ipynb |
11.19KB |
| 402 Audiobooks-data.csv |
710.77KB |
| 402 Business Case_ Final Exercise.html |
1.31KB |
| 402 TensorFlow-Audiobooks-Machine-learning-Homework.ipynb |
14.40KB |
| 402 TensorFlow-Audiobooks-Preprocessing-with-comments.ipynb |
11.19KB |
| 403 What are Data, Servers, Clients, Requests, and Responses.en.srt |
6.16KB |
| 403 What are Data, Servers, Clients, Requests, and Responses.mp4 |
69.03MB |
| 404 What are Data Connectivity, APIs, and Endpoints_.en.srt |
8.85KB |
| 404 What are Data Connectivity, APIs, and Endpoints_.mp4 |
104.08MB |
| 405 Taking a Closer Look at APIs.en.srt |
10.77KB |
| 405 Taking a Closer Look at APIs.mp4 |
115.59MB |
| 406 Communication between Software Products through Text Files.en.srt |
5.68KB |
| 406 Communication between Software Products through Text Files.mp4 |
60.34MB |
| 407 Software Integration - Explained.en.srt |
6.95KB |
| 407 Software Integration - Explained.mp4 |
63.69MB |
| 408 Game Plan for this Python, SQL, and Tableau Business Exercise.en.srt |
5.66KB |
| 408 Game Plan for this Python, SQL, and Tableau Business Exercise.mp4 |
52.30MB |
| 409 The Business Task.en.srt |
3.88KB |
| 409 The Business Task.mp4 |
39.15MB |
| 410 Introducing the Data Set.en.srt |
4.30KB |
| 410 Introducing the Data Set.mp4 |
40.86MB |
| 411 Absenteeism-data.csv |
32.05KB |
| 411 data-preprocessing-homework.pdf |
134.47KB |
| 411 df-preprocessed.csv |
29.11KB |
| 411 What to Expect from the Following Sections_.html |
3.38KB |
| 412 Importing the Absenteeism Data in Python.en.srt |
4.15KB |
| 412 Importing the Absenteeism Data in Python.mp4 |
23.15MB |
| 413 Checking the Content of the Data Set.en.srt |
7.32KB |
| 413 Checking the Content of the Data Set.mp4 |
61.90MB |
| 414 Introduction to Terms with Multiple Meanings.en.srt |
4.20KB |
| 414 Introduction to Terms with Multiple Meanings.mp4 |
27.85MB |
| 415 What's Regression Analysis - a Quick Refresher.html |
3.74KB |
| 416 Using a Statistical Approach towards the Solution to the Exercise.en.srt |
2.91KB |
| 416 Using a Statistical Approach towards the Solution to the Exercise.mp4 |
20.18MB |
| 417 Dropping a Column from a DataFrame in Python.en.srt |
8.12KB |
| 417 Dropping a Column from a DataFrame in Python.mp4 |
61.76MB |
| 418 EXERCISE - Dropping a Column from a DataFrame in Python.html |
1.75KB |
| 419 SOLUTION - Dropping a Column from a DataFrame in Python.html |
1.02KB |
| 420 Analyzing the Reasons for Absence.en.srt |
6.08KB |
| 420 Analyzing the Reasons for Absence.mp4 |
40.57MB |
| 421 Obtaining Dummies from a Single Feature.en.srt |
10.59KB |
| 421 Obtaining Dummies from a Single Feature.mp4 |
81.11MB |
| 422 EXERCISE - Obtaining Dummies from a Single Feature.html |
1.03KB |
| 423 SOLUTION - Obtaining Dummies from a Single Feature.html |
1.02KB |
| 424 Dropping a Dummy Variable from the Data Set.html |
3.23KB |
| 425 More on Dummy Variables_ A Statistical Perspective.en.srt |
1.76KB |
| 425 More on Dummy Variables_ A Statistical Perspective.mp4 |
13.74MB |
| 426 Classifying the Various Reasons for Absence.en.srt |
10.41KB |
| 426 Classifying the Various Reasons for Absence.mp4 |
74.60MB |
| 427 Using .concat() in Python.en.srt |
5.27KB |
| 427 Using .concat() in Python.mp4 |
38.73MB |
| 428 EXERCISE - Using .concat() in Python.html |
1.07KB |
| 429 SOLUTION - Using .concat() in Python.html |
1.03KB |
| 430 Reordering Columns in a Pandas DataFrame in Python.en.srt |
1.89KB |
| 430 Reordering Columns in a Pandas DataFrame in Python.mp4 |
14.01MB |
| 431 EXERCISE - Reordering Columns in a Pandas DataFrame in Python.html |
1.08KB |
| 432 SOLUTION - Reordering Columns in a Pandas DataFrame in Python.html |
1.37KB |
| 433 Absenteeism-Exercise-Preprocessing-df-reason-mod.ipynb |
4.82KB |
| 433 Creating Checkpoints while Coding in Jupyter.en.srt |
3.77KB |
| 433 Creating Checkpoints while Coding in Jupyter.mp4 |
25.67MB |
| 434 EXERCISE - Creating Checkpoints while Coding in Jupyter.html |
1.04KB |
| 435 SOLUTION - Creating Checkpoints while Coding in Jupyter.html |
1.02KB |
| 436 Analyzing the Dates from the Initial Data Set.en.srt |
8.73KB |
| 436 Analyzing the Dates from the Initial Data Set.mp4 |
57.28MB |
| 437 Extracting the Month Value from the _Date_ Column.en.srt |
8.29KB |
| 437 Extracting the Month Value from the _Date_ Column.mp4 |
47.79MB |
| 438 Extracting the Day of the Week from the _Date_ Column.en.srt |
4.65KB |
| 438 Extracting the Day of the Week from the _Date_ Column.mp4 |
27.96MB |
| 439 Absenteeism-Exercise-Preprocessing-ChP-df-date-reason-mod.ipynb |
7.33KB |
| 439 Absenteeism-Exercise-Preprocessing-LECTURES.ipynb |
7.60MB |
| 439 Absenteeism-Exercise-Removing-the-Date-Column-SOLUTION.ipynb |
8.33KB |
| 439 EXERCISE - Removing the _Date_ Column.html |
2.10KB |
| 440 Analyzing Several _Straightforward_ Columns for this Exercise.en.srt |
4.50KB |
| 440 Analyzing Several _Straightforward_ Columns for this Exercise.mp4 |
29.51MB |
| 441 Working on _Education_, _Children_, and _Pets_.en.srt |
5.89KB |
| 441 Working on _Education_, _Children_, and _Pets_.mp4 |
39.59MB |
| 442 Absenteeism-Exercise-EXERCISES-and-SOLUTIONS.ipynb |
4.13KB |
| 442 Absenteeism-Exercise-Preprocessing-df-preprocessed.ipynb |
8.51KB |
| 442 Final Remarks of this Section.en.srt |
2.56KB |
| 442 Final Remarks of this Section.mp4 |
21.63MB |
| 443 A Note on Exporting Your Data as a _.csv File.html |
1.76KB |
| 444 Absenteeism-preprocessed.csv |
29.13KB |
| 444 Exploring the Problem with a Machine Learning Mindset.en.srt |
4.76KB |
| 444 Exploring the Problem with a Machine Learning Mindset.mp4 |
27.54MB |
| 445 Creating the Targets for the Logistic Regression.en.srt |
8.71KB |
| 445 Creating the Targets for the Logistic Regression.mp4 |
45.79MB |
| 446 Selecting the Inputs for the Logistic Regression.en.srt |
3.82KB |
| 446 Selecting the Inputs for the Logistic Regression.mp4 |
16.75MB |
| 447 Standardizing the Data.en.srt |
4.36KB |
| 447 Standardizing the Data.mp4 |
20.59MB |
| 448 Splitting the Data for Training and Testing.en.srt |
8.42KB |
| 448 Splitting the Data for Training and Testing.mp4 |
52.76MB |
| 449 Fitting the Model and Assessing its Accuracy.en.srt |
7.68KB |
| 449 Fitting the Model and Assessing its Accuracy.mp4 |
41.62MB |
| 450 Creating a Summary Table with the Coefficients and Intercept.en.srt |
6.88KB |
| 450 Creating a Summary Table with the Coefficients and Intercept.mp4 |
38.87MB |
| 451 Interpreting the Coefficients for Our Problem.en.srt |
8.19KB |
| 451 Interpreting the Coefficients for Our Problem.mp4 |
52.37MB |
| 452 Standardizing only the Numerical Variables (Creating a Custom Scaler).en.srt |
5.22KB |
| 452 Standardizing only the Numerical Variables (Creating a Custom Scaler).mp4 |
41.19MB |
| 453 Interpreting the Coefficients of the Logistic Regression.en.srt |
7.53KB |
| 453 Interpreting the Coefficients of the Logistic Regression.mp4 |
40.40MB |
| 454 Backward Elimination or How to Simplify Your Model.en.srt |
5.45KB |
| 454 Backward Elimination or How to Simplify Your Model.mp4 |
39.56MB |
| 455 Testing the Model We Created.en.srt |
6.76KB |
| 455 Testing the Model We Created.mp4 |
49.06MB |
| 456 Saving the Model and Preparing it for Deployment.en.srt |
5.79KB |
| 456 Saving the Model and Preparing it for Deployment.mp4 |
37.45MB |
| 457 ARTICLE - A Note on 'pickling'.html |
3.03KB |
| 458 EXERCISE - Saving the Model (and Scaler).html |
1.17KB |
| 459 Preparing the Deployment of the Model through a Module.en.srt |
5.84KB |
| 459 Preparing the Deployment of the Model through a Module.mp4 |
44.48MB |
| 460 Absenteeism-Exercise-Integration.ipynb |
62.35KB |
| 460 absenteeism-module.py |
6.62KB |
| 460 Absenteeism-new-data.csv |
1.87KB |
| 460 Are You Sure You're All Set_.html |
1.39KB |
| 460 model |
1.01KB |
| 460 scaler |
1.86KB |
| 461 Deploying the 'absenteeism_module' - Part I.en.srt |
4.94KB |
| 461 Deploying the 'absenteeism_module' - Part I.mp4 |
25.48MB |
| 462 Deploying the 'absenteeism_module' - Part II.en.srt |
7.81KB |
| 462 Deploying the 'absenteeism_module' - Part II.mp4 |
54.25MB |
| 463 Absenteeism-Exercise-Deploying-the-absenteeism-module.ipynb |
973B |
| 463 Exporting the Obtained Data Set as a _.csv.html |
1.87KB |
| 464 Absenteeism-predictions.csv |
2.10KB |
| 464 EXERCISE - Age vs Probability.html |
1.26KB |
| 465 Absenteeism-predictions.csv |
2.10KB |
| 465 Analyzing Age vs Probability in Tableau.en.srt |
10.41KB |
| 465 Analyzing Age vs Probability in Tableau.mp4 |
56.55MB |
| 466 EXERCISE - Reasons vs Probability.html |
1.27KB |
| 467 Analyzing Reasons vs Probability in Tableau.en.srt |
9.91KB |
| 467 Analyzing Reasons vs Probability in Tableau.mp4 |
59.33MB |
| 468 EXERCISE - Transportation Expense vs Probability.html |
1.44KB |
| 469 Analyzing Transportation Expense vs Probability in Tableau.en.srt |
7.49KB |
| 469 Analyzing Transportation Expense vs Probability in Tableau.mp4 |
40.63MB |
| 470 Additional-Python-Tools-Exercises.ipynb |
11.37KB |
| 470 Additional-Python-Tools-Lectures.ipynb |
13.47KB |
| 470 Additional-Python-Tools-Solutions.ipynb |
25.49KB |
| 470 Using the .format() Method.en.srt |
12.81KB |
| 470 Using the .format() Method.mp4 |
47.63MB |
| 471 Iterating Over Range Objects.en.srt |
6.26KB |
| 471 Iterating Over Range Objects.mp4 |
22.48MB |
| 472 Introduction to Nested For Loops.en.srt |
8.61KB |
| 472 Introduction to Nested For Loops.mp4 |
29.46MB |
| 473 Triple Nested For Loops.en.srt |
8.30KB |
| 473 Triple Nested For Loops.mp4 |
46.59MB |
| 474 List Comprehensions.en.srt |
12.81KB |
| 474 List Comprehensions.mp4 |
55.45MB |
| 475 Additional-Python-Tools-Exercises.ipynb |
11.37KB |
| 475 Additional-Python-Tools-Lectures.ipynb |
13.47KB |
| 475 Additional-Python-Tools-Solutions.ipynb |
25.49KB |
| 475 Anonymous (Lambda) Functions.en.srt |
10.24KB |
| 475 Anonymous (Lambda) Functions.mp4 |
38.53MB |
| 476 Bonus Lecture_ Next Steps.html |
3.41KB |
| external-assets-links.txt |
105B |
| external-assets-links.txt |
134B |
| external-assets-links.txt |
790B |