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| 001 What is Machine Learning_.en_US.srt |
6.41Кб |
| 001 What is Machine Learning_.mp4 |
32.37Мб |
| 002 What is Data Science_.en_US.srt |
5.33Кб |
| 002 What is Data Science_.mp4 |
71.61Мб |
| 003 Download the Syllabus.html |
2.14Кб |
| 003 ML-Data-Science-Syllabus.pdf |
103.97Кб |
| 004 App-Brewery-Cornell-Notes-Template.txt |
81б |
| 004 Top Tips for Succeeding on this Course.html |
2.72Кб |
| 005 Course Resources List.html |
1.76Кб |
| 006 Course-Resources.txt |
62б |
| 006 Introduction to Linear Regression & Specifying the Problem.en_US.srt |
8.08Кб |
| 006 Introduction to Linear Regression & Specifying the Problem.mp4 |
38.77Мб |
| 007 cost-revenue-dirty.csv |
374.68Кб |
| 007 Gather & Clean the Data.en_US.srt |
12.93Кб |
| 007 Gather & Clean the Data.mp4 |
69.03Мб |
| 007 The-Numbers-Movie-Budgets.txt |
42б |
| 008 cost-revenue-clean.csv |
90.82Кб |
| 008 Explore & Visualise the Data with Python.en_US.srt |
28.64Кб |
| 008 Explore & Visualise the Data with Python.mp4 |
188.77Мб |
| 008 Try-Jupyter-in-your-Browser.txt |
25б |
| 009 01-Linear-Regression-checkpoint.ipynb.zip |
37.64Кб |
| 009 The Intuition behind the Linear Regression Model.en_US.srt |
10.05Кб |
| 009 The Intuition behind the Linear Regression Model.mp4 |
19.46Мб |
| 010 Analyse and Evaluate the Results.en_US.srt |
20.75Кб |
| 010 Analyse and Evaluate the Results.mp4 |
142.22Мб |
| 011 01-Linear-Regression-complete.ipynb.zip |
75.28Кб |
| 011 Download the Complete Notebook Here.html |
727б |
| 012 Join the Student Community.html |
1.34Кб |
| 013 Any Feedback on this Section_.html |
997б |
| 014 Course-Resources.txt |
62б |
| 014 Windows Users - Install Anaconda.en_US.srt |
8.17Кб |
| 014 Windows Users - Install Anaconda.mp4 |
58.18Мб |
| 015 Course-Resources.txt |
62б |
| 015 Mac Users - Install Anaconda.en_US.srt |
7.49Кб |
| 015 Mac Users - Install Anaconda.mp4 |
81.31Мб |
| 016 Does LSD Make You Better at Maths_.en_US.srt |
6.82Кб |
| 016 Does LSD Make You Better at Maths_.mp4 |
66.90Мб |
| 017 12-Rules-to-Learn-to-Code.pdf |
2.25Мб |
| 017 Download the 12 Rules to Learn to Code.html |
1.77Кб |
| 018 [Python] - Variables and Types.en_US.srt |
15.36Кб |
| 018 [Python] - Variables and Types.mp4 |
84.28Мб |
| 019 [exercise_info] Python Variable Coding Exercise.html |
1.85Кб |
| 019 [exercise_solution] Python Variable Coding Exercise.zip |
173б |
| 019 [exercise] Python Variable Coding Exercise.zip |
241б |
| 019 [Python] - Lists and Arrays.en_US.srt |
11.23Кб |
| 019 [Python] - Lists and Arrays.mp4 |
60.90Мб |
| 020 [exercise_info] Python Lists Coding Exercise.html |
1.92Кб |
| 020 [exercise_solution] Python Lists Coding Exercise.zip |
228б |
| 020 [exercise] Python Lists Coding Exercise.zip |
246б |
| 020 [Python & Pandas] - Dataframes and Series.en_US.srt |
25.94Кб |
| 020 [Python & Pandas] - Dataframes and Series.mp4 |
203.61Мб |
| 020 lsd-math-score-data.csv |
155б |
| 021 [Python] - Module Imports.en_US.srt |
33.36Кб |
| 021 [Python] - Module Imports.mp4 |
349.51Мб |
| 022 [Python] - Functions - Part 1_ Defining and Calling Functions.en_US.srt |
9.72Кб |
| 022 [Python] - Functions - Part 1_ Defining and Calling Functions.mp4 |
46.99Мб |
| 023 [exercise_info] Python Functions Coding Exercise - Part 1.html |
1.64Кб |
| 023 [exercise_solution] Python Functions Coding Exercise - Part 1.zip |
269б |
| 023 [exercise] Python Functions Coding Exercise - Part 1.zip |
285б |
| 023 [Python] - Functions - Part 2_ Arguments & Parameters.en_US.srt |
19.25Кб |
| 023 [Python] - Functions - Part 2_ Arguments & Parameters.mp4 |
189.68Мб |
| 024 [exercise_info] Python Functions Coding Exercise - Part 2.html |
1.36Кб |
| 024 [exercise_solution] Python Functions Coding Exercise - Part 2.zip |
253б |
| 024 [exercise] Python Functions Coding Exercise - Part 2.zip |
276б |
| 024 [Python] - Functions - Part 3_ Results & Return Values.en_US.srt |
15.34Кб |
| 024 [Python] - Functions - Part 3_ Results & Return Values.mp4 |
94.36Мб |
| 025 [exercise_info] Python Functions Coding Exercise - Part 3.html |
1.42Кб |
| 025 [exercise_solution] Python Functions Coding Exercise - Part 3.zip |
190б |
| 025 [exercise] Python Functions Coding Exercise - Part 3.zip |
210б |
| 025 [Python] - Objects - Understanding Attributes and Methods.en_US.srt |
27.54Кб |
| 025 [Python] - Objects - Understanding Attributes and Methods.mp4 |
234.49Мб |
| 026 How to Make Sense of Python Documentation for Data Visualisation.en_US.srt |
24.48Кб |
| 026 How to Make Sense of Python Documentation for Data Visualisation.mp4 |
265.05Мб |
| 027 Working with Python Objects to Analyse Data.en_US.srt |
25.09Кб |
| 027 Working with Python Objects to Analyse Data.mp4 |
258.83Мб |
| 028 [Python] - Tips, Code Style and Naming Conventions.en_US.srt |
15.53Кб |
| 028 [Python] - Tips, Code Style and Naming Conventions.mp4 |
130.73Мб |
| 029 02-Python-Intro.ipynb.zip |
36.44Кб |
| 029 Download the Complete Notebook Here.html |
727б |
| 030 Any Feedback on this Section_.html |
998б |
| 031 Course-Resources.txt |
62б |
| 031 What's Coming Up_.en_US.srt |
3.56Кб |
| 031 What's Coming Up_.mp4 |
24.59Мб |
| 032 How a Machine Learns.en_US.srt |
6.71Кб |
| 032 How a Machine Learns.mp4 |
16.56Мб |
| 033 Introduction to Cost Functions.en_US.srt |
8.75Кб |
| 033 Introduction to Cost Functions.mp4 |
76.86Мб |
| 034 LaTeX Markdown and Generating Data with Numpy.en_US.srt |
15.85Кб |
| 034 LaTeX Markdown and Generating Data with Numpy.mp4 |
77.24Мб |
| 035 Understanding the Power Rule & Creating Charts with Subplots.en_US.srt |
16.63Кб |
| 035 Understanding the Power Rule & Creating Charts with Subplots.mp4 |
104.46Мб |
| 036 [Python] - Loops and the Gradient Descent Algorithm.en_US.srt |
40.23Кб |
| 036 [Python] - Loops and the Gradient Descent Algorithm.mp4 |
449.20Мб |
| 037 [exercise_info] Python Loops Coding Exercise.html |
15.48Кб |
| 037 [exercise_solution] Python Loops Coding Exercise.zip |
288б |
| 037 [exercise] Python Loops Coding Exercise.zip |
260б |
| 037 [Python] - Advanced Functions and the Pitfalls of Optimisation (Part 1).en_US.srt |
39.58Кб |
| 037 [Python] - Advanced Functions and the Pitfalls of Optimisation (Part 1).mp4 |
446.84Мб |
| 038 [Python] - Tuples and the Pitfalls of Optimisation (Part 2).en_US.srt |
30.87Кб |
| 038 [Python] - Tuples and the Pitfalls of Optimisation (Part 2).mp4 |
302.32Мб |
| 039 Understanding the Learning Rate.en_US.srt |
34.67Кб |
| 039 Understanding the Learning Rate.mp4 |
280.53Мб |
| 040 How to Create 3-Dimensional Charts.en_US.srt |
24.04Кб |
| 040 How to Create 3-Dimensional Charts.mp4 |
294.27Мб |
| 041 Understanding Partial Derivatives and How to use SymPy.en_US.srt |
18.71Кб |
| 041 Understanding Partial Derivatives and How to use SymPy.mp4 |
193.33Мб |
| 042 Implementing Batch Gradient Descent with SymPy.en_US.srt |
11.97Кб |
| 042 Implementing Batch Gradient Descent with SymPy.mp4 |
117.53Мб |
| 043 [Python] - Loops and Performance Considerations.en_US.srt |
16.67Кб |
| 043 [Python] - Loops and Performance Considerations.mp4 |
205.61Мб |
| 044 Reshaping and Slicing N-Dimensional Arrays.en_US.srt |
21.21Кб |
| 044 Reshaping and Slicing N-Dimensional Arrays.mp4 |
168.28Мб |
| 045 Concatenating Numpy Arrays.en_US.srt |
8.28Кб |
| 045 Concatenating Numpy Arrays.mp4 |
85.15Мб |
| 046 Introduction to the Mean Squared Error (MSE).en_US.srt |
11.70Кб |
| 046 Introduction to the Mean Squared Error (MSE).mp4 |
75.17Мб |
| 047 Transposing and Reshaping Arrays.en_US.srt |
12.46Кб |
| 047 Transposing and Reshaping Arrays.mp4 |
101.68Мб |
| 048 Implementing a MSE Cost Function.en_US.srt |
12.51Кб |
| 048 Implementing a MSE Cost Function.mp4 |
97.47Мб |
| 049 Understanding Nested Loops and Plotting the MSE Function (Part 1).en_US.srt |
12.89Кб |
| 049 Understanding Nested Loops and Plotting the MSE Function (Part 1).mp4 |
83.37Мб |
| 050 Plotting the Mean Squared Error (MSE) on a Surface (Part 2).en_US.srt |
16.01Кб |
| 050 Plotting the Mean Squared Error (MSE) on a Surface (Part 2).mp4 |
68.86Мб |
| 051 Running Gradient Descent with a MSE Cost Function.en_US.srt |
20.52Кб |
| 051 Running Gradient Descent with a MSE Cost Function.mp4 |
129.83Мб |
| 052 Visualising the Optimisation on a 3D Surface.en_US.srt |
9.94Кб |
| 052 Visualising the Optimisation on a 3D Surface.mp4 |
88.19Мб |
| 053 03-Gradient-Descent.ipynb.zip |
1.14Мб |
| 053 Download the Complete Notebook Here.html |
727б |
| 054 Any Feedback on this Section_.html |
1005б |
| 055 Course-Resources.txt |
62б |
| 055 Defining the Problem.en_US.srt |
5.98Кб |
| 055 Defining the Problem.mp4 |
57.82Мб |
| 056 Gathering the Boston House Price Data.en_US.srt |
8.03Кб |
| 056 Gathering the Boston House Price Data.mp4 |
91.05Мб |
| 057 Clean and Explore the Data (Part 1)_ Understand the Nature of the Dataset.en_US.srt |
14.47Кб |
| 057 Clean and Explore the Data (Part 1)_ Understand the Nature of the Dataset.mp4 |
99.37Мб |
| 058 Clean and Explore the Data (Part 2)_ Find Missing Values.en_US.srt |
17.24Кб |
| 058 Clean and Explore the Data (Part 2)_ Find Missing Values.mp4 |
207.91Мб |
| 059 Visualising Data (Part 1)_ Historams, Distributions & Outliers.en_US.srt |
13.17Кб |
| 059 Visualising Data (Part 1)_ Historams, Distributions & Outliers.mp4 |
71.94Мб |
| 060 Visualising Data (Part 2)_ Seaborn and Probability Density Functions.en_US.srt |
8.33Кб |
| 060 Visualising Data (Part 2)_ Seaborn and Probability Density Functions.mp4 |
66.18Мб |
| 061 Working with Index Data, Pandas Series, and Dummy Variables.en_US.srt |
19.11Кб |
| 061 Working with Index Data, Pandas Series, and Dummy Variables.mp4 |
195.59Мб |
| 062 Understanding Descriptive Statistics_ the Mean vs the Median.en_US.srt |
11.28Кб |
| 062 Understanding Descriptive Statistics_ the Mean vs the Median.mp4 |
72.50Мб |
| 063 Introduction to Correlation_ Understanding Strength & Direction.en_US.srt |
7.81Кб |
| 063 Introduction to Correlation_ Understanding Strength & Direction.mp4 |
20.40Мб |
| 064 Calculating Correlations and the Problem posed by Multicollinearity.en_US.srt |
16.46Кб |
| 064 Calculating Correlations and the Problem posed by Multicollinearity.mp4 |
154.88Мб |
| 065 Visualising Correlations with a Heatmap.en_US.srt |
22.52Кб |
| 065 Visualising Correlations with a Heatmap.mp4 |
191.50Мб |
| 066 Techniques to Style Scatter Plots.en_US.srt |
19.09Кб |
| 066 Techniques to Style Scatter Plots.mp4 |
149.97Мб |
| 067 A Note for the Next Lesson.html |
961б |
| 068 Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques.en_US.srt |
26.52Кб |
| 068 Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques.mp4 |
334.45Мб |
| 069 Understanding Multivariable Regression.en_US.srt |
6.98Кб |
| 069 Understanding Multivariable Regression.mp4 |
61.60Мб |
| 070 How to Shuffle and Split Training & Testing Data.en_US.srt |
10.65Кб |
| 070 How to Shuffle and Split Training & Testing Data.mp4 |
83.32Мб |
| 071 Running a Multivariable Regression.en_US.srt |
9.06Кб |
| 071 Running a Multivariable Regression.mp4 |
74.05Мб |
| 072 How to Calculate the Model Fit with R-Squared.en_US.srt |
4.09Кб |
| 072 How to Calculate the Model Fit with R-Squared.mp4 |
38.56Мб |
| 073 Introduction to Model Evaluation.en_US.srt |
3.53Кб |
| 073 Introduction to Model Evaluation.mp4 |
12.03Мб |
| 074 Improving the Model by Transforming the Data.en_US.srt |
19.95Кб |
| 074 Improving the Model by Transforming the Data.mp4 |
142.49Мб |
| 075 How to Interpret Coefficients using p-Values and Statistical Significance.en_US.srt |
10.04Кб |
| 075 How to Interpret Coefficients using p-Values and Statistical Significance.mp4 |
89.12Мб |
| 076 Understanding VIF & Testing for Multicollinearity.en_US.srt |
23.67Кб |
| 076 Understanding VIF & Testing for Multicollinearity.mp4 |
164.60Мб |
| 077 Model Simplification & Baysian Information Criterion.en_US.srt |
21.41Кб |
| 077 Model Simplification & Baysian Information Criterion.mp4 |
229.47Мб |
| 078 How to Analyse and Plot Regression Residuals.en_US.srt |
13.66Кб |
| 078 How to Analyse and Plot Regression Residuals.mp4 |
46.63Мб |
| 079 Residual Analysis (Part 1)_ Predicted vs Actual Values.en_US.srt |
16.78Кб |
| 079 Residual Analysis (Part 1)_ Predicted vs Actual Values.mp4 |
146.46Мб |
| 080 Residual Analysis (Part 2)_ Graphing and Comparing Regression Residuals.en_US.srt |
20.96Кб |
| 080 Residual Analysis (Part 2)_ Graphing and Comparing Regression Residuals.mp4 |
178.21Мб |
| 081 Making Predictions (Part 1)_ MSE & R-Squared.en_US.srt |
21.83Кб |
| 081 Making Predictions (Part 1)_ MSE & R-Squared.mp4 |
217.80Мб |
| 082 Making Predictions (Part 2)_ Standard Deviation, RMSE, and Prediction Intervals.en_US.srt |
13.67Кб |
| 082 Making Predictions (Part 2)_ Standard Deviation, RMSE, and Prediction Intervals.mp4 |
116.49Мб |
| 083 Build a Valuation Tool (Part 1)_ Working with Pandas Series & Numpy ndarrays.en_US.srt |
19.09Кб |
| 083 Build a Valuation Tool (Part 1)_ Working with Pandas Series & Numpy ndarrays.mp4 |
198.42Мб |
| 084 [Python] - Conditional Statements - Build a Valuation Tool (Part 2).en_US.srt |
19.66Кб |
| 084 [Python] - Conditional Statements - Build a Valuation Tool (Part 2).mp4 |
159.88Мб |
| 085 [exercise_info] Python Conditional Statement Coding Exercise.html |
2.54Кб |
| 085 [exercise_solution] Python Conditional Statement Coding Exercise.zip |
200б |
| 085 [exercise] Python Conditional Statement Coding Exercise.zip |
167б |
| 085 Build a Valuation Tool (Part 3)_ Docstrings & Creating your own Python Module.en_US.srt |
26.12Кб |
| 085 Build a Valuation Tool (Part 3)_ Docstrings & Creating your own Python Module.mp4 |
167.92Мб |
| 086 04-Multivariable-Regression.ipynb.zip |
3.54Мб |
| 086 04-Valuation-Tool.ipynb.zip |
2.93Кб |
| 086 boston-valuation.py |
3.05Кб |
| 086 Download the Complete Notebook Here.html |
727б |
| 087 Any Feedback on this Section_.html |
997б |
| 088 Course-Resources.txt |
62б |
| 088 How to Translate a Business Problem into a Machine Learning Problem.en_US.srt |
8.99Кб |
| 088 How to Translate a Business Problem into a Machine Learning Problem.mp4 |
57.44Мб |
| 089 Gathering Email Data and Working with Archives & Text Editors.en_US.srt |
13.05Кб |
| 089 Gathering Email Data and Working with Archives & Text Editors.mp4 |
186.87Мб |
| 089 SpamData.zip |
21.28Мб |
| 090 How to Add the Lesson Resources to the Project.en_US.srt |
4.55Кб |
| 090 How to Add the Lesson Resources to the Project.mp4 |
35.07Мб |
| 091 The Naive Bayes Algorithm and the Decision Boundary for a Classifier.en_US.srt |
5.67Кб |
| 091 The Naive Bayes Algorithm and the Decision Boundary for a Classifier.mp4 |
57.15Мб |
| 092 Basic Probability.en_US.srt |
4.90Кб |
| 092 Basic Probability.mp4 |
15.52Мб |
| 093 Joint & Conditional Probability.en_US.srt |
18.34Кб |
| 093 Joint & Conditional Probability.mp4 |
169.32Мб |
| 094 Bayes Theorem.en_US.srt |
14.02Кб |
| 094 Bayes Theorem.mp4 |
94.54Мб |
| 095 Reading Files (Part 1)_ Absolute Paths and Relative Paths.en_US.srt |
10.90Кб |
| 095 Reading Files (Part 1)_ Absolute Paths and Relative Paths.mp4 |
71.15Мб |
| 096 Reading Files (Part 2)_ Stream Objects and Email Structure.en_US.srt |
13.52Кб |
| 096 Reading Files (Part 2)_ Stream Objects and Email Structure.mp4 |
167.33Мб |
| 097 Extracting the Text in the Email Body.en_US.srt |
5.54Кб |
| 097 Extracting the Text in the Email Body.mp4 |
55.23Мб |
| 098 [Python] - Generator Functions & the yield Keyword.en_US.srt |
20.66Кб |
| 098 [Python] - Generator Functions & the yield Keyword.mp4 |
197.71Мб |
| 099 Create a Pandas DataFrame of Email Bodies.en_US.srt |
6.67Кб |
| 099 Create a Pandas DataFrame of Email Bodies.mp4 |
67.91Мб |
| 100 Cleaning Data (Part 1)_ Check for Empty Emails & Null Entries.en_US.srt |
16.54Кб |
| 100 Cleaning Data (Part 1)_ Check for Empty Emails & Null Entries.mp4 |
191.89Мб |
| 101 Cleaning Data (Part 2)_ Working with a DataFrame Index.en_US.srt |
8.50Кб |
| 101 Cleaning Data (Part 2)_ Working with a DataFrame Index.mp4 |
84.91Мб |
| 102 Saving a JSON File with Pandas.en_US.srt |
6.45Кб |
| 102 Saving a JSON File with Pandas.mp4 |
81.28Мб |
| 103 Data Visualisation (Part 1)_ Pie Charts.en_US.srt |
14.93Кб |
| 103 Data Visualisation (Part 1)_ Pie Charts.mp4 |
131.08Мб |
| 104 Data Visualisation (Part 2)_ Donut Charts.en_US.srt |
8.85Кб |
| 104 Data Visualisation (Part 2)_ Donut Charts.mp4 |
73.40Мб |
| 105 Introduction to Natural Language Processing (NLP).en_US.srt |
7.64Кб |
| 105 Introduction to Natural Language Processing (NLP).mp4 |
58.19Мб |
| 106 Tokenizing, Removing Stop Words and the Python Set Data Structure.en_US.srt |
17.62Кб |
| 106 Tokenizing, Removing Stop Words and the Python Set Data Structure.mp4 |
138.05Мб |
| 107 Word Stemming & Removing Punctuation.en_US.srt |
9.89Кб |
| 107 Word Stemming & Removing Punctuation.mp4 |
83.68Мб |
| 108 Removing HTML tags with BeautifulSoup.en_US.srt |
10.28Кб |
| 108 Removing HTML tags with BeautifulSoup.mp4 |
167.47Мб |
| 109 Creating a Function for Text Processing.en_US.srt |
8.04Кб |
| 109 Creating a Function for Text Processing.mp4 |
44.76Мб |
| 110 A Note for the Next Lesson.html |
961б |
| 111 Advanced Subsetting on DataFrames_ the apply() Function.en_US.srt |
12.53Кб |
| 111 Advanced Subsetting on DataFrames_ the apply() Function.mp4 |
97.91Мб |
| 112 [Python] - Logical Operators to Create Subsets and Indices.en_US.srt |
14.87Кб |
| 112 [Python] - Logical Operators to Create Subsets and Indices.mp4 |
101.30Мб |
| 113 Word Clouds & How to install Additional Python Packages.en_US.srt |
11.12Кб |
| 113 Word Clouds & How to install Additional Python Packages.mp4 |
92.38Мб |
| 114 Creating your First Word Cloud.en_US.srt |
12.72Кб |
| 114 Creating your First Word Cloud.mp4 |
152.62Мб |
| 115 Styling the Word Cloud with a Mask.en_US.srt |
15.43Кб |
| 115 Styling the Word Cloud with a Mask.mp4 |
184.63Мб |
| 116 Solving the Hamlet Challenge.en_US.srt |
5.55Кб |
| 116 Solving the Hamlet Challenge.mp4 |
92.62Мб |
| 117 Styling Word Clouds with Custom Fonts.en_US.srt |
13.68Кб |
| 117 Styling Word Clouds with Custom Fonts.mp4 |
184.20Мб |
| 118 Create the Vocabulary for the Spam Classifier.en_US.srt |
16.40Кб |
| 118 Create the Vocabulary for the Spam Classifier.mp4 |
124.04Мб |
| 119 Coding Challenge_ Check for Membership in a Collection.en_US.srt |
5.64Кб |
| 119 Coding Challenge_ Check for Membership in a Collection.mp4 |
19.91Мб |
| 120 Coding Challenge_ Find the Longest Email.en_US.srt |
6.97Кб |
| 120 Coding Challenge_ Find the Longest Email.mp4 |
76.59Мб |
| 121 Sparse Matrix (Part 1)_ Split the Training and Testing Data.en_US.srt |
14.05Кб |
| 121 Sparse Matrix (Part 1)_ Split the Training and Testing Data.mp4 |
119.49Мб |
| 122 Sparse Matrix (Part 2)_ Data Munging with Nested Loops.en_US.srt |
20.62Кб |
| 122 Sparse Matrix (Part 2)_ Data Munging with Nested Loops.mp4 |
160.46Мб |
| 123 Sparse Matrix (Part 3)_ Using groupby() and Saving .txt Files.en_US.srt |
11.19Кб |
| 123 Sparse Matrix (Part 3)_ Using groupby() and Saving .txt Files.mp4 |
110.82Мб |
| 124 Coding Challenge Solution_ Preparing the Test Data.en_US.srt |
4.18Кб |
| 124 Coding Challenge Solution_ Preparing the Test Data.mp4 |
37.54Мб |
| 125 Checkpoint_ Understanding the Data.en_US.srt |
12.63Кб |
| 125 Checkpoint_ Understanding the Data.mp4 |
134.39Мб |
| 126 06-Bayes-Classifier-Pre-Processing.ipynb.zip |
978.02Кб |
| 126 Download the Complete Notebook Here.html |
727б |
| 127 Any Feedback on this Section_.html |
1004б |
| 128 Course-Resources.txt |
62б |
| 128 Setting up the Notebook and Understanding Delimiters in a Dataset.en_US.srt |
10.75Кб |
| 128 Setting up the Notebook and Understanding Delimiters in a Dataset.mp4 |
99.17Мб |
| 128 SpamData.zip |
22.31Мб |
| 129 Create a Full Matrix.en_US.srt |
20.86Кб |
| 129 Create a Full Matrix.mp4 |
200.50Мб |
| 130 Count the Tokens to Train the Naive Bayes Model.en_US.srt |
17.66Кб |
| 130 Count the Tokens to Train the Naive Bayes Model.mp4 |
111.37Мб |
| 131 Sum the Tokens across the Spam and Ham Subsets.en_US.srt |
7.49Кб |
| 131 Sum the Tokens across the Spam and Ham Subsets.mp4 |
39.64Мб |
| 132 Calculate the Token Probabilities and Save the Trained Model.en_US.srt |
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| 132 Calculate the Token Probabilities and Save the Trained Model.mp4 |
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| 133 Coding Challenge_ Prepare the Test Data.en_US.srt |
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| 133 Coding Challenge_ Prepare the Test Data.mp4 |
53.69Мб |
| 134 07-Bayes-Classifier-Training.ipynb.zip |
5.82Кб |
| 134 Download the Complete Notebook Here.html |
727б |
| 135 Any Feedback on this Section_.html |
1012б |
| 136 Course-Resources.txt |
62б |
| 136 Set up the Testing Notebook.en_US.srt |
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| 136 Set up the Testing Notebook.mp4 |
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| 136 SpamData.zip |
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| 137 Joint Conditional Probability (Part 1)_ Dot Product.en_US.srt |
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| 137 Joint Conditional Probability (Part 1)_ Dot Product.mp4 |
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| 138 Joint Conditional Probablity (Part 2)_ Priors.en_US.srt |
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| 138 Joint Conditional Probablity (Part 2)_ Priors.mp4 |
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| 139 Making Predictions_ Comparing Joint Probabilities.en_US.srt |
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| 139 Making Predictions_ Comparing Joint Probabilities.mp4 |
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| 140 The Accuracy Metric.en_US.srt |
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| 140 The Accuracy Metric.mp4 |
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| 141 Visualising the Decision Boundary.en_US.srt |
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| 141 Visualising the Decision Boundary.mp4 |
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| 142 False Positive vs False Negatives.en_US.srt |
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| 142 False Positive vs False Negatives.mp4 |
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| 143 The Recall Metric.en_US.srt |
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| 143 The Recall Metric.mp4 |
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| 144 The Precision Metric.en_US.srt |
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| 144 The Precision Metric.mp4 |
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| 145 The F-score or F1 Metric.en_US.srt |
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| 145 The F-score or F1 Metric.mp4 |
28.68Мб |
| 146 A Naive Bayes Implementation using SciKit Learn.en_US.srt |
32.36Кб |
| 146 A Naive Bayes Implementation using SciKit Learn.mp4 |
270.52Мб |
| 147 07-Bayes-Classifier-Testing-Inference-Evaluation.ipynb.zip |
243.05Кб |
| 147 08-Naive-Bayes-with-scikit-learn.ipynb.zip |
13.26Кб |
| 147 Download the Complete Notebook Here.html |
727б |
| 148 Any Feedback on this Section_.html |
994б |
| 149 Course-Resources.txt |
62б |
| 149 The Human Brain and the Inspiration for Artificial Neural Networks.en_US.srt |
10.48Кб |
| 149 The Human Brain and the Inspiration for Artificial Neural Networks.mp4 |
60.54Мб |
| 150 Layers, Feature Generation and Learning.en_US.srt |
26.75Кб |
| 150 Layers, Feature Generation and Learning.mp4 |
237.20Мб |
| 151 Costs and Disadvantages of Neural Networks.en_US.srt |
18.52Кб |
| 151 Costs and Disadvantages of Neural Networks.mp4 |
145.87Мб |
| 152 Preprocessing Image Data and How RGB Works.en_US.srt |
15.54Кб |
| 152 Preprocessing Image Data and How RGB Works.mp4 |
129.57Мб |
| 152 TF-Keras-Classification-Images.zip |
501.10Кб |
| 153 Importing Keras Models and the Tensorflow Graph.en_US.srt |
11.03Кб |
| 153 Importing Keras Models and the Tensorflow Graph.mp4 |
73.36Мб |
| 154 Making Predictions using InceptionResNet.en_US.srt |
18.18Кб |
| 154 Making Predictions using InceptionResNet.mp4 |
181.21Мб |
| 155 Coding Challenge Solution_ Using other Keras Models.en_US.srt |
12.47Кб |
| 155 Coding Challenge Solution_ Using other Keras Models.mp4 |
154.95Мб |
| 156 09-Neural-Nets-Pretrained-Image-Classification.ipynb.zip |
571.83Кб |
| 156 Download the Complete Notebook Here.html |
749б |
| 157 Any Feedback on this Section_.html |
1011б |
| 158 Course-Resources.txt |
62б |
| 158 Solving a Business Problem with Image Classification.en_US.srt |
4.79Кб |
| 158 Solving a Business Problem with Image Classification.mp4 |
37.55Мб |
| 159 Installing Tensorflow and Keras for Jupyter.en_US.srt |
6.20Кб |
| 159 Installing Tensorflow and Keras for Jupyter.mp4 |
58.44Мб |
| 160 Gathering the CIFAR 10 Dataset.en_US.srt |
5.88Кб |
| 160 Gathering the CIFAR 10 Dataset.mp4 |
35.34Мб |
| 161 Exploring the CIFAR Data.en_US.srt |
17.51Кб |
| 161 Exploring the CIFAR Data.mp4 |
151.05Мб |
| 162 Pre-processing_ Scaling Inputs and Creating a Validation Dataset.en_US.srt |
19.17Кб |
| 162 Pre-processing_ Scaling Inputs and Creating a Validation Dataset.mp4 |
103.65Мб |
| 163 Compiling a Keras Model and Understanding the Cross Entropy Loss Function.en_US.srt |
17.94Кб |
| 163 Compiling a Keras Model and Understanding the Cross Entropy Loss Function.mp4 |
139.62Мб |
| 164 Interacting with the Operating System and the Python Try-Catch Block.en_US.srt |
22.83Кб |
| 164 Interacting with the Operating System and the Python Try-Catch Block.mp4 |
193.73Мб |
| 165 Fit a Keras Model and Use Tensorboard to Visualise Learning and Spot Problems.en_US.srt |
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| 165 Fit a Keras Model and Use Tensorboard to Visualise Learning and Spot Problems.mp4 |
145.59Мб |
| 166 Use Regularisation to Prevent Overfitting_ Early Stopping & Dropout Techniques.en_US.srt |
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| 166 Use Regularisation to Prevent Overfitting_ Early Stopping & Dropout Techniques.mp4 |
287.09Мб |
| 167 Use the Model to Make Predictions.en_US.srt |
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| 167 Use the Model to Make Predictions.mp4 |
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| 168 Model Evaluation and the Confusion Matrix.en_US.srt |
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| 168 Model Evaluation and the Confusion Matrix.mp4 |
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| 169 Model Evaluation and the Confusion Matrix.en_US.srt |
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| 169 Model Evaluation and the Confusion Matrix.mp4 |
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| 170 10-Neural-Nets-Keras-CIFAR10-Classification.ipynb.zip |
120.11Кб |
| 170 Download the Complete Notebook Here.html |
727б |
| 171 Any Feedback on this Section_.html |
1006б |
| 172 Course-Resources.txt |
62б |
| 172 What's coming up_.en_US.srt |
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| 172 What's coming up_.mp4 |
7.62Мб |
| 173 Getting the Data and Loading it into Numpy Arrays.en_US.srt |
8.67Кб |
| 173 Getting the Data and Loading it into Numpy Arrays.mp4 |
75.07Мб |
| 173 MNIST.zip |
14.77Мб |
| 174 Data Exploration and Understanding the Structure of the Input Data.en_US.srt |
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| 174 Data Exploration and Understanding the Structure of the Input Data.mp4 |
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| 175 Data Preprocessing_ One-Hot Encoding and Creating the Validation Dataset.en_US.srt |
12.20Кб |
| 175 Data Preprocessing_ One-Hot Encoding and Creating the Validation Dataset.mp4 |
88.53Мб |
| 176 What is a Tensor_.en_US.srt |
8.66Кб |
| 176 What is a Tensor_.mp4 |
69.70Мб |
| 177 Creating Tensors and Setting up the Neural Network Architecture.en_US.srt |
27.97Кб |
| 177 Creating Tensors and Setting up the Neural Network Architecture.mp4 |
205.12Мб |
| 178 Defining the Cross Entropy Loss Function, the Optimizer and the Metrics.en_US.srt |
13.62Кб |
| 178 Defining the Cross Entropy Loss Function, the Optimizer and the Metrics.mp4 |
84.85Мб |
| 179 TensorFlow Sessions and Batching Data.en_US.srt |
19.71Кб |
| 179 TensorFlow Sessions and Batching Data.mp4 |
128.85Мб |
| 180 Tensorboard Summaries and the Filewriter.en_US.srt |
22.36Кб |
| 180 Tensorboard Summaries and the Filewriter.mp4 |
186.50Мб |
| 181 Understanding the Tensorflow Graph_ Nodes and Edges.en_US.srt |
20.45Кб |
| 181 Understanding the Tensorflow Graph_ Nodes and Edges.mp4 |
167.36Мб |
| 182 Name Scoping and Image Visualisation in Tensorboard.en_US.srt |
25.29Кб |
| 182 Name Scoping and Image Visualisation in Tensorboard.mp4 |
88.68Мб |
| 183 Different Model Architectures_ Experimenting with Dropout.en_US.srt |
29.00Кб |
| 183 Different Model Architectures_ Experimenting with Dropout.mp4 |
335.83Мб |
| 184 Prediction and Model Evaluation.en_US.srt |
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| 184 Prediction and Model Evaluation.mp4 |
162.28Мб |
| 185 11-Neural-Networks-TF-Handwriting-Recognition.ipynb.zip |
6.60Кб |
| 185 Download the Complete Notebook Here.html |
727б |
| 186 Any Feedback on this Section_.html |
984б |
| 187 What you'll make.en_US.srt |
9.39Кб |
| 187 What you'll make.mp4 |
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| 188 11-Neural-Networks-TF-Handwriting-Recognition.ipynb.zip |
6.39Кб |
| 188 Saving Tensorflow Models.en_US.srt |
20.45Кб |
| 188 Saving Tensorflow Models.mp4 |
191.76Мб |
| 189 12-TF-SavedModel-Export-Completed.ipynb.zip |
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| 189 Loading a SavedModel.en_US.srt |
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| 189 Loading a SavedModel.mp4 |
144.86Мб |
| 189 MNIST-Model-Load-Files.zip |
2.84Мб |
| 190 Converting a Model to Tensorflow.js.en_US.srt |
20.34Кб |
| 190 Converting a Model to Tensorflow.js.mp4 |
171.61Мб |
| 190 TFJS.zip |
1.54Мб |
| 191 Introducing the Website Project and Tooling.en_US.srt |
16.60Кб |
| 191 Introducing the Website Project and Tooling.mp4 |
125.38Мб |
| 191 math-garden-stub.zip |
44.03Кб |
| 192 HTML and CSS Styling.en_US.srt |
36.48Кб |
| 192 HTML and CSS Styling.mp4 |
244.58Мб |
| 193 Loading a Tensorflow.js Model and Starting your own Server.en_US.srt |
35.83Кб |
| 193 Loading a Tensorflow.js Model and Starting your own Server.mp4 |
321.34Мб |
| 193 x-test0-ylabel7.txt |
4.59Кб |
| 193 x-test1-ylabel2.txt |
4.59Кб |
| 193 x-test2-ylabel1.txt |
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| 194 Adding a Favicon.en_US.srt |
7.11Кб |
| 194 Adding a Favicon.mp4 |
42.12Мб |
| 195 Styling an HTML Canvas.en_US.srt |
37.97Кб |
| 195 Styling an HTML Canvas.mp4 |
312.41Мб |
| 196 Drawing on an HTML Canvas.en_US.srt |
36.32Кб |
| 196 Drawing on an HTML Canvas.mp4 |
290.97Мб |
| 197 Data Pre-Processing for Tensorflow.js.en_US.srt |
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| 197 Data Pre-Processing for Tensorflow.js.mp4 |
42.36Мб |
| 198 Introduction to OpenCV.en_US.srt |
36.93Кб |
| 198 Introduction to OpenCV.mp4 |
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| 198 math-garden-stub-12.12-checkpoint.zip |
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| 199 Resizing and Adding Padding to Images.en_US.srt |
25.81Кб |
| 199 Resizing and Adding Padding to Images.mp4 |
286.25Мб |
| 200 Calculating the Centre of Mass and Shifting the Image.en_US.srt |
34.09Кб |
| 200 Calculating the Centre of Mass and Shifting the Image.mp4 |
405.91Мб |
| 201 Making a Prediction from a Digit drawn on the HTML Canvas.en_US.srt |
16.40Кб |
| 201 Making a Prediction from a Digit drawn on the HTML Canvas.mp4 |
181.80Мб |
| 202 Adding the Game Logic.en_US.srt |
36.55Кб |
| 202 Adding the Game Logic.mp4 |
286.00Мб |
| 202 math-garden-stub-complete.zip |
4.09Мб |
| 203 Publish and Share your Website!.en_US.srt |
9.14Кб |
| 203 Publish and Share your Website!.mp4 |
59.48Мб |
| 204 Any Feedback on this Section_.html |
985б |
| 205 Where next_.html |
4.56Кб |
| 206 What Modules Do You Want to See_.html |
916б |
| 207 Stay in Touch!.html |
1.52Кб |