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Please note that this page does not hosts or makes available any of the listed filenames. You
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| 001 Applying Traditional Data, Big Data, BI, Traditional Data Science and ML__en.srt |
9.14KB |
| 001 Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.mp4 |
21.73MB |
| 001 A Practical Example_ What You Will Learn in This Course__en.srt |
6.41KB |
| 001 A Practical Example_ What You Will Learn in This Course.mp4 |
13.08MB |
| 001 Are You Sure You're All Set_.html |
513B |
| 001 Basic NN Example (Part 1)__en.srt |
4.52KB |
| 001 Basic NN Example (Part 1).mp4 |
5.14MB |
| 001 Bonus Lecture_ Next Steps.html |
2.84KB |
| 001 Business Case_ Exploring the Dataset and Identifying Predictors__en.srt |
10.50KB |
| 001 Business Case_ Exploring the Dataset and Identifying Predictors.mp4 |
51.38MB |
| 001 Business Case_ Getting Acquainted with the Dataset__en.srt |
10.62KB |
| 001 Business Case_ Getting Acquainted with the Dataset.mp4 |
60.26MB |
| 001 Comparison Operators__en.srt |
2.50KB |
| 001 Comparison Operators.mp4 |
3.12MB |
| 001 Data Science and Business Buzzwords_ Why are there so Many___en.srt |
6.77KB |
| 001 Data Science and Business Buzzwords_ Why are there so Many_.mp4 |
54.72MB |
| 001 Debunking Common Misconceptions__en.srt |
5.43KB |
| 001 Debunking Common Misconceptions.mp4 |
16.43MB |
| 001 Defining a Function in Python__en.srt |
2.43KB |
| 001 Defining a Function in Python.mp4 |
3.23MB |
| 001 EXERCISE - Age vs Probability.html |
367B |
| 001 Exploring the Problem with a Machine Learning Mindset__en.srt |
4.63KB |
| 001 Exploring the Problem with a Machine Learning Mindset.mp4 |
11.08MB |
| 001 Finding the Job - What to Expect and What to Look for__en.srt |
4.34KB |
| 001 Finding the Job - What to Expect and What to Look for.mp4 |
9.48MB |
| 001 For Loops__en.srt |
6.69KB |
| 001 For Loops.mp4 |
23.58MB |
| 001 Fundamentals of Combinatorics__en.srt |
1.35KB |
| 001 Fundamentals of Combinatorics.mp4 |
3.21MB |
| 001 Fundamentals of Probability Distributions__en.srt |
7.92KB |
| 001 Fundamentals of Probability Distributions.mp4 |
19.28MB |
| 001 Game Plan for this Python, SQL, and Tableau Business Exercise__en.srt |
5.54KB |
| 001 Game Plan for this Python, SQL, and Tableau Business Exercise.mp4 |
15.80MB |
| 001 How to Install TensorFlow 2.0__en.srt |
6.52KB |
| 001 How to Install TensorFlow 2.0.mp4 |
27.34MB |
| 001 Introduction__en.srt |
1.63KB |
| 001 Introduction.mp4 |
2.93MB |
| 001 Introduction to Cluster Analysis__en.srt |
4.77KB |
| 001 Introduction to Cluster Analysis.mp4 |
10.66MB |
| 001 Introduction to Logistic Regression__en.srt |
1.71KB |
| 001 Introduction to Logistic Regression.mp4 |
4.41MB |
| 001 Introduction to Neural Networks__en.srt |
6.09KB |
| 001 Introduction to Neural Networks.mp4 |
10.37MB |
| 001 Introduction to pandas Series__en.srt |
10.67KB |
| 001 Introduction to pandas Series.mp4 |
22.22MB |
| 001 Introduction to Programming__en.srt |
6.93KB |
| 001 Introduction to Programming.mp4 |
14.33MB |
| 001 Introduction to Regression Analysis__en.srt |
2.22KB |
| 001 Introduction to Regression Analysis.mp4 |
2.92MB |
| 001 K-Means Clustering__en.srt |
6.48KB |
| 001 K-Means Clustering.mp4 |
10.53MB |
| 001 Lists__en.srt |
10.05KB |
| 001 Lists.mp4 |
20.50MB |
| 001 MNIST_ The Dataset__en.srt |
3.48KB |
| 001 MNIST_ The Dataset.mp4 |
4.06MB |
| 001 MNIST_ What is the MNIST Dataset___en.srt |
3.52KB |
| 001 MNIST_ What is the MNIST Dataset_.mp4 |
4.23MB |
| 001 Multiple Linear Regression__en.srt |
3.32KB |
| 001 Multiple Linear Regression.mp4 |
5.54MB |
| 001 Necessary Programming Languages and Software Used in Data Science__en.srt |
7.35KB |
| 001 Necessary Programming Languages and Software Used in Data Science.mp4 |
19.54MB |
| 001 Null vs Alternative Hypothesis__en.srt |
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| 001 Null vs Alternative Hypothesis.mp4 |
80.83MB |
| 001 Object Oriented Programming__en.srt |
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| 001 Object Oriented Programming.mp4 |
8.42MB |
| 001 Population and Sample__en.srt |
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| 001 Population and Sample.mp4 |
10.89MB |
| 001 Practical Example_ Descriptive Statistics__en.srt |
20.91KB |
| 001 Practical Example_ Descriptive Statistics.mp4 |
37.17MB |
| 001 Practical Example_ Hypothesis Testing__en.srt |
8.71KB |
| 001 Practical Example_ Hypothesis Testing.mp4 |
16.30MB |
| 001 Practical Example_ Inferential Statistics__en.srt |
13.73KB |
| 001 Practical Example_ Inferential Statistics.mp4 |
22.10MB |
| 001 Practical Example_ Linear Regression (Part 1)__en.srt |
14.94KB |
| 001 Practical Example_ Linear Regression (Part 1).mp4 |
84.84MB |
| 001 Preprocessing Introduction__en.srt |
3.81KB |
| 001 Preprocessing Introduction.mp4 |
8.98MB |
| 001 Probability in Finance__en.srt |
9.80KB |
| 001 Probability in Finance.mp4 |
39.66MB |
| 001 READ ME____.html |
564B |
| 001 Sets and Events__en.srt |
5.37KB |
| 001 Sets and Events.mp4 |
17.44MB |
| 001 Stochastic Gradient Descent__en.srt |
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| 001 Stochastic Gradient Descent.mp4 |
7.62MB |
| 001 Summary on What You've Learned__en.srt |
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| 001 Summary on What You've Learned.mp4 |
9.66MB |
| 001 Techniques for Working with Traditional Data__en.srt |
10.70KB |
| 001 Techniques for Working with Traditional Data.mp4 |
105.52MB |
| 001 The Basic Probability Formula__en.srt |
9.04KB |
| 001 The Basic Probability Formula.mp4 |
29.13MB |
| 001 The IF Statement__en.srt |
3.53KB |
| 001 The IF Statement.mp4 |
5.33MB |
| 001 The Linear Regression Model__en.srt |
6.83KB |
| 001 The Linear Regression Model.mp4 |
13.16MB |
| 001 The Reason Behind These Disciplines__en.srt |
6.56KB |
| 001 The Reason Behind These Disciplines.mp4 |
12.41MB |
| 001 Types of Clustering__en.srt |
4.74KB |
| 001 Types of Clustering.mp4 |
7.57MB |
| 001 Types of Data__en.srt |
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| 001 Types of Data.mp4 |
42.47MB |
| 001 Using Arithmetic Operators in Python__en.srt |
4.28KB |
| 001 Using Arithmetic Operators in Python.mp4 |
7.28MB |
| 001 Using the .format() Method__en.srt |
12.34KB |
| 001 Using the .format() Method.mp4 |
21.67MB |
| 001 Variables__en.srt |
4.53KB |
| 001 Variables.mp4 |
8.93MB |
| 001 What are Confidence Intervals___en.srt |
3.29KB |
| 001 What are Confidence Intervals_.mp4 |
28.38MB |
| 001 What are Data, Servers, Clients, Requests, and Responses__en.srt |
5.92KB |
| 001 What are Data, Servers, Clients, Requests, and Responses.mp4 |
19.17MB |
| 001 What is a Layer___en.srt |
2.44KB |
| 001 What is a Layer_.mp4 |
3.47MB |
| 001 What is a Matrix___en.srt |
4.42KB |
| 001 What is a Matrix_.mp4 |
11.70MB |
| 001 What is Initialization___en.srt |
3.60KB |
| 001 What is Initialization_.mp4 |
17.42MB |
| 001 What is Overfitting___en.srt |
5.73KB |
| 001 What is Overfitting_.mp4 |
10.50MB |
| 001 What is sklearn and How is it Different from Other Packages__en.srt |
3.38KB |
| 001 What is sklearn and How is it Different from Other Packages.mp4 |
6.24MB |
| 001 What to Expect from the Following Sections_.html |
2.43KB |
| 001 What to Expect from this Part___en.srt |
4.61KB |
| 001 What to Expect from this Part_.mp4 |
7.56MB |
| 002 Adjusted R-Squared__en.srt |
7.57KB |
| 002 Adjusted R-Squared.mp4 |
34.22MB |
| 002 Analyzing Age vs Probability in Tableau__en.srt |
10.22KB |
| 002 Analyzing Age vs Probability in Tableau.mp4 |
38.69MB |
| 002 A Simple Example in Python__en.srt |
5.91KB |
| 002 A Simple Example in Python.mp4 |
21.91MB |
| 002 A Simple Example of Clustering__en.srt |
902B |
| 002 A Simple Example of Clustering_en.vtt |
8.25KB |
| 002 A Simple Example of Clustering.mp4 |
26.08MB |
| 002 Basic NN Example (Part 2)__en.srt |
6.90KB |
| 002 Basic NN Example (Part 2).mp4 |
15.23MB |
| 002 Business Case_ Outlining the Solution__en.srt |
1.93KB |
| 002 Business Case_ Outlining the Solution__en.srt |
2.50KB |
| 002 Business Case_ Outlining the Solution.mp4 |
2.21MB |
| 002 Business Case_ Outlining the Solution.mp4 |
2.89MB |
| 002 Computing Expected Values__en.srt |
6.71KB |
| 002 Computing Expected Values.mp4 |
29.24MB |
| 002 Confidence Intervals; Population Variance Known; Z-score__en.srt |
10.26KB |
| 002 Confidence Intervals; Population Variance Known; Z-score.mp4 |
52.21MB |
| 002 Correlation vs Regression__en.srt |
2.03KB |
| 002 Correlation vs Regression.mp4 |
3.75MB |
| 002 Creating the Targets for the Logistic Regression__en.srt |
8.56KB |
| 002 Creating the Targets for the Logistic Regression.mp4 |
32.50MB |
| 002 Dendrogram__en.srt |
7.28KB |
| 002 Dendrogram.mp4 |
17.34MB |
| 002 Deploying the 'absenteeism_module' - Part I__en.srt |
4.86KB |
| 002 Deploying the 'absenteeism_module' - Part I.mp4 |
8.38MB |
| 002 Further Reading on Null and Alternative Hypothesis.html |
2.23KB |
| 002 How are we Going to Approach this Section___en.srt |
1.56KB |
| 002 How are we Going to Approach this Section__en.vtt |
2.56KB |
| 002 How are we Going to Approach this Section_.mp4 |
4.03MB |
| 002 How to Create a Function with a Parameter__en.srt |
4.30KB |
| 002 How to Create a Function with a Parameter.mp4 |
8.29MB |
| 002 How to Install TensorFlow 1__en.srt |
2.70KB |
| 002 How to Install TensorFlow 1_en.vtt |
2.91KB |
| 002 How to Install TensorFlow 1.mp4 |
3.71MB |
| 002 Importing the Absenteeism Data in Python__en.srt |
3.89KB |
| 002 Importing the Absenteeism Data in Python.mp4 |
18.03MB |
| 002 Iterating Over Range Objects__en.srt |
6.04KB |
| 002 Iterating Over Range Objects.mp4 |
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| 002 Levels of Measurement__en.srt |
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| 002 Levels of Measurement.mp4 |
31.44MB |
| 002 Logical and Identity Operators__en.srt |
5.94KB |
| 002 Logical and Identity Operators.mp4 |
19.00MB |
| 002 MNIST_ How to Tackle the MNIST__en.srt |
3.78KB |
| 002 MNIST_ How to Tackle the MNIST__en.srt |
3.72KB |
| 002 MNIST_ How to Tackle the MNIST.mp4 |
7.66MB |
| 002 MNIST_ How to Tackle the MNIST.mp4 |
7.68MB |
| 002 Modules and Packages__en.srt |
1.34KB |
| 002 Modules and Packages.mp4 |
1.71MB |
| 002 Numbers and Boolean Values in Python__en.srt |
3.62KB |
| 002 Numbers and Boolean Values in Python.mp4 |
4.61MB |
| 002 Permutations and How to Use Them__en.srt |
4.24KB |
| 002 Permutations and How to Use Them.mp4 |
13.97MB |
| 002 Practical Example_ Descriptive Statistics Exercise.html |
81B |
| 002 Practical Example_ Hypothesis Testing Exercise.html |
81B |
| 002 Practical Example_ Inferential Statistics Exercise.html |
81B |
| 002 Practical Example_ Linear Regression (Part 2)__en.srt |
5.00KB |
| 002 Practical Example_ Linear Regression (Part 2)_en.vtt |
7.11KB |
| 002 Practical Example_ Linear Regression (Part 2).mp4 |
31.90MB |
| 002 Probability in Statistics__en.srt |
8.62KB |
| 002 Probability in Statistics.mp4 |
14.26MB |
| 002 Problems with Gradient Descent__en.srt |
2.92KB |
| 002 Problems with Gradient Descent.mp4 |
3.51MB |
| 002 Real Life Examples of Traditional Data__en.srt |
2.22KB |
| 002 Real Life Examples of Traditional Data.mp4 |
13.92MB |
| 002 Scalars and Vectors__en.srt |
3.83KB |
| 002 Scalars and Vectors.mp4 |
8.39MB |
| 002 Some Examples of Clusters__en.srt |
6.20KB |
| 002 Some Examples of Clusters.mp4 |
35.12MB |
| 002 TensorFlow Outline and Comparison with Other Libraries__en.srt |
1.50KB |
| 002 TensorFlow Outline and Comparison with Other Libraries_en.vtt |
4.74KB |
| 002 TensorFlow Outline and Comparison with Other Libraries.mp4 |
14.94MB |
| 002 The Business Task__en.srt |
3.72KB |
| 002 The Business Task.mp4 |
6.80MB |
| 002 The Double Equality Sign__en.srt |
1.77KB |
| 002 The Double Equality Sign.mp4 |
2.72MB |
| 002 The ELSE Statement__en.srt |
3.11KB |
| 002 The ELSE Statement.mp4 |
5.25MB |
| 002 Training the Model__en.srt |
4.42KB |
| 002 Training the Model.mp4 |
7.57MB |
| 002 Types of Basic Preprocessing__en.srt |
1.72KB |
| 002 Types of Basic Preprocessing.mp4 |
2.40MB |
| 002 Types of Probability Distributions__en.srt |
9.72KB |
| 002 Types of Probability Distributions.mp4 |
28.69MB |
| 002 Types of Simple Initializations__en.srt |
3.82KB |
| 002 Types of Simple Initializations.mp4 |
5.73MB |
| 002 Underfitting and Overfitting for Classification__en.srt |
2.69KB |
| 002 Underfitting and Overfitting for Classification.mp4 |
13.53MB |
| 002 Using Methods__en.srt |
8.35KB |
| 002 Using Methods.mp4 |
23.42MB |
| 002 Ways Sets Can Interact__en.srt |
4.40KB |
| 002 Ways Sets Can Interact.mp4 |
19.02MB |
| 002 What's Further out there in terms of Machine Learning__en.srt |
2.64KB |
| 002 What's Further out there in terms of Machine Learning.mp4 |
3.71MB |
| 002 What are Data Connectivity, APIs, and Endpoints___en.srt |
8.51KB |
| 002 What are Data Connectivity, APIs, and Endpoints_.mp4 |
58.83MB |
| 002 What Does the Course Cover__en.srt |
5.10KB |
| 002 What Does the Course Cover.mp4 |
49.69MB |
| 002 What is a Deep Net___en.srt |
3.30KB |
| 002 What is a Deep Net_.mp4 |
11.06MB |
| 002 What is a Distribution__en.srt |
6.13KB |
| 002 What is a Distribution.mp4 |
16.90MB |
| 002 What is the difference between Analysis and Analytics__en.srt |
5.03KB |
| 002 What is the difference between Analysis and Analytics.mp4 |
8.01MB |
| 002 While Loops and Incrementing__en.srt |
6.18KB |
| 002 While Loops and Incrementing.mp4 |
20.20MB |
| 002 Why Python___en.srt |
6.85KB |
| 002 Why Python_.mp4 |
11.77MB |
| 002 Working with Methods in Python - Part I__en.srt |
6.92KB |
| 002 Working with Methods in Python - Part I.mp4 |
16.80MB |
| 003 A Note on Installing Packages in Anaconda.html |
2.28KB |
| 003 A Note on Multicollinearity.html |
849B |
| 003 A Simple Example of Clustering - Exercise.html |
87B |
| 003 Basic NN Example (Part 3)__en.srt |
4.39KB |
| 003 Basic NN Example (Part 3).mp4 |
15.68MB |
| 003 Business Analytics, Data Analytics, and Data Science_ An Introduction__en.srt |
11.00KB |
| 003 Business Analytics, Data Analytics, and Data Science_ An Introduction.mp4 |
49.96MB |
| 003 Business Case_ Balancing the Dataset__en.srt |
4.72KB |
| 003 Business Case_ Balancing the Dataset.mp4 |
26.19MB |
| 003 Categorical Variables - Visualization Techniques__en.srt |
6.44KB |
| 003 Categorical Variables - Visualization Techniques.mp4 |
36.65MB |
| 003 Characteristics of Discrete Distributions__en.srt |
2.51KB |
| 003 Characteristics of Discrete Distributions.mp4 |
9.25MB |
| 003 Checking the Content of the Data Set__en.srt |
7.12KB |
| 003 Checking the Content of the Data Set.mp4 |
54.27MB |
| 003 Confidence Intervals; Population Variance Known; Z-score; Exercise.html |
81B |
| 003 DeepMind and Deep Learning.html |
1.04KB |
| 003 Defining a Function in Python - Part II__en.srt |
2.88KB |
| 003 Defining a Function in Python - Part II.mp4 |
6.45MB |
| 003 Deploying the 'absenteeism_module' - Part II__en.srt |
429B |
| 003 Deploying the 'absenteeism_module' - Part II_en.vtt |
6.76KB |
| 003 Deploying the 'absenteeism_module' - Part II.mp4 |
25.99MB |
| 003 Difference between Classification and Clustering__en.srt |
3.34KB |
| 003 Difference between Classification and Clustering.mp4 |
9.53MB |
| 003 Digging into a Deep Net__en.srt |
6.73KB |
| 003 Digging into a Deep Net.mp4 |
19.14MB |
| 003 Download All Resources and Important FAQ.html |
21.36KB |
| 003 EXERCISE - Reasons vs Probability.html |
385B |
| 003 Frequency__en.srt |
6.28KB |
| 003 Frequency.mp4 |
36.39MB |
| 003 Geometrical Representation of the Linear Regression Model__en.srt |
1.66KB |
| 003 Geometrical Representation of the Linear Regression Model.mp4 |
1.75MB |
| 003 Heatmaps__en.srt |
6.24KB |
| 003 Heatmaps.mp4 |
25.71MB |
| 003 How to Reassign Values__en.srt |
1.39KB |
| 003 How to Reassign Values.mp4 |
1.86MB |
| 003 Intersection of Sets__en.srt |
2.49KB |
| 003 Intersection of Sets.mp4 |
8.78MB |
| 003 Introducing the Data Set__en.srt |
4.13KB |
| 003 Introducing the Data Set.mp4 |
15.29MB |
| 003 Introduction to Nested For Loops__en.srt |
8.30KB |
| 003 Introduction to Nested For Loops.mp4 |
12.26MB |
| 003 Linear Algebra and Geometry__en.srt |
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| 003 Linear Algebra and Geometry.mp4 |
13.56MB |
| 003 List Slicing__en.srt |
5.22KB |
| 003 List Slicing.mp4 |
19.17MB |
| 003 Lists with the range() Function__en.srt |
8.03KB |
| 003 Lists with the range() Function.mp4 |
14.50MB |
| 003 Logistic vs Logit Function__en.srt |
4.89KB |
| 003 Logistic vs Logit Function.mp4 |
43.96MB |
| 003 MNIST_ Importing the Relevant Packages and Loading the Data__en.srt |
3.01KB |
| 003 MNIST_ Importing the Relevant Packages and Loading the Data.mp4 |
12.24MB |
| 003 MNIST_ Relevant Packages__en.srt |
2.25KB |
| 003 MNIST_ Relevant Packages.mp4 |
7.88MB |
| 003 Momentum__en.srt |
3.59KB |
| 003 Momentum.mp4 |
5.01MB |
| 003 Multiple Linear Regression Exercise.html |
76B |
| 003 Probability in Data Science__en.srt |
6.64KB |
| 003 Probability in Data Science.mp4 |
23.94MB |
| 003 Python Strings__en.srt |
7.10KB |
| 003 Python Strings.mp4 |
19.74MB |
| 003 Rejection Region and Significance Level__en.srt |
9.09KB |
| 003 Rejection Region and Significance Level.mp4 |
38.20MB |
| 003 Selecting the Inputs for the Logistic Regression__en.srt |
3.42KB |
| 003 Selecting the Inputs for the Logistic Regression.mp4 |
4.64MB |
| 003 Simple Linear Regression with sklearn__en.srt |
1.06KB |
| 003 Simple Linear Regression with sklearn_en.vtt |
6.71KB |
| 003 Simple Linear Regression with sklearn.mp4 |
31.65MB |
| 003 Simple Operations with Factorials__en.srt |
3.56KB |
| 003 Simple Operations with Factorials.mp4 |
13.98MB |
| 003 Standardization__en.srt |
6.20KB |
| 003 Standardization.mp4 |
11.95MB |
| 003 State-of-the-Art Method - (Xavier) Glorot Initialization__en.srt |
3.67KB |
| 003 State-of-the-Art Method - (Xavier) Glorot Initialization.mp4 |
4.18MB |
| 003 Taking a Closer Look at APIs__en.srt |
10.60KB |
| 003 Taking a Closer Look at APIs.mp4 |
65.29MB |
| 003 Techniques for Working with Big Data__en.srt |
5.74KB |
| 003 Techniques for Working with Big Data.mp4 |
60.48MB |
| 003 TensorFlow 1 vs TensorFlow 2__en.srt |
3.76KB |
| 003 TensorFlow 1 vs TensorFlow 2.mp4 |
14.95MB |
| 003 The ELIF Statement__en.srt |
6.60KB |
| 003 The ELIF Statement.mp4 |
14.25MB |
| 003 The Importance of Working with a Balanced Dataset__en.srt |
4.72KB |
| 003 The Importance of Working with a Balanced Dataset.mp4 |
21.60MB |
| 003 The Normal Distribution__en.srt |
5.01KB |
| 003 The Normal Distribution.mp4 |
16.16MB |
| 003 Types of Machine Learning__en.srt |
5.31KB |
| 003 Types of Machine Learning.mp4 |
9.81MB |
| 003 What is the Standard Library___en.srt |
3.66KB |
| 003 What is the Standard Library_.mp4 |
4.87MB |
| 003 What is Validation___en.srt |
4.90KB |
| 003 What is Validation_.mp4 |
8.14MB |
| 003 Why Jupyter___en.srt |
4.63KB |
| 003 Why Jupyter_.mp4 |
7.96MB |
| 003 Working with Methods in Python - Part II__en.srt |
3.60KB |
| 003 Working with Methods in Python - Part II.mp4 |
5.77MB |
| 004 Add Comments__en.srt |
1.80KB |
| 004 Add Comments.mp4 |
2.41MB |
| 004 Analyzing Reasons vs Probability in Tableau__en.srt |
9.68KB |
| 004 Analyzing Reasons vs Probability in Tableau.mp4 |
40.24MB |
| 004 A Note on Boolean Values__en.srt |
2.91KB |
| 004 A Note on Boolean Values.mp4 |
3.26MB |
| 004 A Note on TensorFlow 2 Syntax__en.srt |
1.41KB |
| 004 A Note on TensorFlow 2 Syntax.mp4 |
2.34MB |
| 004 An overview of CNNs__en.srt |
6.71KB |
| 004 An overview of CNNs.mp4 |
30.47MB |
| 004 Arrays in Python - A Convenient Way To Represent Matrices__en.srt |
6.03KB |
| 004 Arrays in Python - A Convenient Way To Represent Matrices.mp4 |
19.01MB |
| 004 Basic NN Example (Part 4)__en.srt |
10.56KB |
| 004 Basic NN Example (Part 4).mp4 |
30.06MB |
| 004 Building a Logistic Regression__en.srt |
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| 004 Building a Logistic Regression.mp4 |
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| 006 Practical Example_ Linear Regression (Part 4)__en.srt |
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| 35215106-365-Data-Science-Data-Science-Interview-Questions-Guide.pdf |
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