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/0. Websites you may like/
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[CourseClub.Me].url
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0.1 KB
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[FreeCourseSite.com].url
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[GigaCourse.Com].url
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/01 - Introduction to the Course/
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001 What is Machine Learning.mp4
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42.3 MB
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001 What is Machine Learning_en.vtt
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6.2 KB
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002 What is Data Science.mp4
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41.8 MB
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002 What is Data Science_en.vtt
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5.2 KB
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003 Download the Syllabus.html
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1.0 KB
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004 Top Tips for Succeeding on this Course.html
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2.1 KB
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005 Course Resources List.html
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1.1 KB
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18162714-ML-Data-Science-Syllabus.pdf
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106.5 KB
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external-assets-links.txt
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/02 - Predict Movie Box Office Revenue with Linear Regression/
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001 Introduction to Linear Regression & Specifying the Problem.mp4
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27.8 MB
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001 Introduction to Linear Regression & Specifying the Problem_en.vtt
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7.8 KB
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002 Gather & Clean the Data.mp4
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42.8 MB
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002 Gather & Clean the Data_en.vtt
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12.5 KB
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003 Explore & Visualise the Data with Python.mp4
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110.2 MB
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003 Explore & Visualise the Data with Python_en.vtt
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27.6 KB
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004 The Intuition behind the Linear Regression Model.mp4
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13.5 MB
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004 The Intuition behind the Linear Regression Model_en.vtt
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9.7 KB
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005 Analyse and Evaluate the Results.mp4
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79.1 MB
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005 Analyse and Evaluate the Results_en.vtt
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20.0 KB
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006 Download the Complete Notebook Here.html
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0.2 KB
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007 Join the Student Community.html
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0.7 KB
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008 Any Feedback on this Section.html
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0.5 KB
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18175084-01-Linear-Regression-checkpoint.ipynb.zip
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38.5 KB
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18175146-01-Linear-Regression-complete.ipynb.zip
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77.1 KB
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9246634-cost-revenue-dirty.csv
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383.7 KB
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9249290-cost-revenue-clean.csv
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93.0 KB
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external-assets-links.txt
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0.2 KB
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/03 - Python Programming for Data Science and Machine Learning/
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001 Windows Users - Install Anaconda.mp4
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33.7 MB
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001 Windows Users - Install Anaconda_en.vtt
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7.9 KB
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002 Mac Users - Install Anaconda.mp4
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41.0 MB
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002 Mac Users - Install Anaconda_en.vtt
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7.2 KB
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003 Does LSD Make You Better at Maths.mp4
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16.4 MB
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003 Does LSD Make You Better at Maths_en.vtt
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6.6 KB
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004 Download the 12 Rules to Learn to Code.html
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1.1 KB
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005 [Python] - Variables and Types.mp4
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49.9 MB
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005 [Python] - Variables and Types_en.vtt
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14.8 KB
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006 [Python] - Lists and Arrays.mp4
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36.8 MB
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006 [Python] - Lists and Arrays_en.vtt
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10.8 KB
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007 [Python & Pandas] - Dataframes and Series.mp4
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106.3 MB
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007 [Python & Pandas] - Dataframes and Series_en.vtt
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25.0 KB
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008 [Python] - Module Imports.mp4
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195.9 MB
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008 [Python] - Module Imports_en.vtt
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32.2 KB
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009 [Python] - Functions - Part 1 Defining and Calling Functions.mp4
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28.7 MB
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009 [Python] - Functions - Part 1 Defining and Calling Functions_en.vtt
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9.4 KB
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010 [Python] - Functions - Part 2 Arguments & Parameters.mp4
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104.3 MB
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010 [Python] - Functions - Part 2 Arguments & Parameters_en.vtt
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18.6 KB
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011 [Python] - Functions - Part 3 Results & Return Values.mp4
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56.8 MB
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011 [Python] - Functions - Part 3 Results & Return Values_en.vtt
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14.8 KB
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012 [Python] - Objects - Understanding Attributes and Methods.mp4
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131.4 MB
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012 [Python] - Objects - Understanding Attributes and Methods_en.vtt
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26.5 KB
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013 How to Make Sense of Python Documentation for Data Visualisation.mp4
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144.8 MB
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013 How to Make Sense of Python Documentation for Data Visualisation_en.vtt
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23.6 KB
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014 Working with Python Objects to Analyse Data.mp4
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142.0 MB
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014 Working with Python Objects to Analyse Data_en.vtt
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24.1 KB
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015 [Python] - Tips, Code Style and Naming Conventions.mp4
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70.4 MB
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015 [Python] - Tips, Code Style and Naming Conventions_en.vtt
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15.0 KB
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016 Download the Complete Notebook Here.html
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0.2 KB
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017 Any Feedback on this Section.html
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0.5 KB
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18179882-02-Python-Intro.ipynb.zip
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37.3 KB
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18204473-12-Rules-to-Learn-to-Code.pdf
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2.4 MB
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18877814-lsd-math-score-data.csv
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external-assets-links.txt
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/04 - Introduction to Optimisation and the Gradient Descent Algorithm/
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001 What's Coming Up.mp4
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13.5 MB
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001 What's Coming Up_en.vtt
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3.4 KB
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002 How a Machine Learns.mp4
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11.0 MB
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002 How a Machine Learns_en.vtt
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6.5 KB
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003 Introduction to Cost Functions.mp4
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40.9 MB
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003 Introduction to Cost Functions_en.vtt
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8.4 KB
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004 LaTeX Markdown and Generating Data with Numpy.mp4
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49.3 MB
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004 LaTeX Markdown and Generating Data with Numpy_en.vtt
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15.2 KB
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005 Understanding the Power Rule & Creating Charts with Subplots.mp4
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62.4 MB
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005 Understanding the Power Rule & Creating Charts with Subplots_en.vtt
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16.0 KB
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006 [Python] - Loops and the Gradient Descent Algorithm.mp4
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97.4 MB
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006 [Python] - Loops and the Gradient Descent Algorithm_en.vtt
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38.5 KB
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007 [Python] - Advanced Functions and the Pitfalls of Optimisation (Part 1).mp4
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240.6 MB
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007 [Python] - Advanced Functions and the Pitfalls of Optimisation (Part 1)_en.vtt
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38.1 KB
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008 [Python] - Tuples and the Pitfalls of Optimisation (Part 2).mp4
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152.3 MB
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008 [Python] - Tuples and the Pitfalls of Optimisation (Part 2)_en.vtt
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29.7 KB
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009 Understanding the Learning Rate.mp4
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199.4 MB
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009 Understanding the Learning Rate_en.vtt
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33.3 KB
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010 How to Create 3-Dimensional Charts.mp4
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159.4 MB
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010 How to Create 3-Dimensional Charts_en.vtt
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23.1 KB
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011 Understanding Partial Derivatives and How to use SymPy.mp4
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107.7 MB
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011 Understanding Partial Derivatives and How to use SymPy_en.vtt
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18.0 KB
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012 Implementing Batch Gradient Descent with SymPy.mp4
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68.7 MB
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012 Implementing Batch Gradient Descent with SymPy_en.vtt
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11.5 KB
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013 [Python] - Loops and Performance Considerations.mp4
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111.8 MB
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013 [Python] - Loops and Performance Considerations_en.vtt
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16.1 KB
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014 Reshaping and Slicing N-Dimensional Arrays.mp4
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99.7 MB
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014 Reshaping and Slicing N-Dimensional Arrays_en.vtt
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20.4 KB
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015 Concatenating Numpy Arrays.mp4
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34.1 MB
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015 Concatenating Numpy Arrays_en.vtt
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8.0 KB
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016 Introduction to the Mean Squared Error (MSE).mp4
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45.3 MB
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016 Introduction to the Mean Squared Error (MSE)_en.vtt
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11.3 KB
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017 Transposing and Reshaping Arrays.mp4
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60.8 MB
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017 Transposing and Reshaping Arrays_en.vtt
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12.0 KB
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018 Implementing a MSE Cost Function.mp4
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57.2 MB
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018 Implementing a MSE Cost Function_en.vtt
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12.0 KB
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019 Understanding Nested Loops and Plotting the MSE Function (Part 1).mp4
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51.3 MB
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019 Understanding Nested Loops and Plotting the MSE Function (Part 1)_en.vtt
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12.4 KB
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020 Plotting the Mean Squared Error (MSE) on a Surface (Part 2).mp4
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101.6 MB
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020 Plotting the Mean Squared Error (MSE) on a Surface (Part 2)_en.vtt
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15.3 KB
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021 Running Gradient Descent with a MSE Cost Function.mp4
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77.9 MB
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021 Running Gradient Descent with a MSE Cost Function_en.vtt
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19.7 KB
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022 Visualising the Optimisation on a 3D Surface.mp4
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37.3 MB
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022 Visualising the Optimisation on a 3D Surface_en.vtt
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9.6 KB
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023 Download the Complete Notebook Here.html
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0.2 KB
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024 Any Feedback on this Section.html
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0.5 KB
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18179908-03-Gradient-Descent.ipynb.zip
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1.2 MB
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external-assets-links.txt
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/05 - Predict House Prices with Multivariable Linear Regression/
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001 Defining the Problem.mp4
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31.5 MB
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001 Defining the Problem_en.vtt
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5.7 KB
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002 Gathering the Boston House Price Data.mp4
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49.9 MB
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002 Gathering the Boston House Price Data_en.vtt
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7.7 KB
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003 Clean and Explore the Data (Part 1) Understand the Nature of the Dataset.mp4
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59.5 MB
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003 Clean and Explore the Data (Part 1) Understand the Nature of the Dataset_en.vtt
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14.0 KB
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004 Clean and Explore the Data (Part 2) Find Missing Values.mp4
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112.7 MB
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004 Clean and Explore the Data (Part 2) Find Missing Values_en.vtt
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16.7 KB
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005 Visualising Data (Part 1) Historams, Distributions & Outliers.mp4
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44.7 MB
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005 Visualising Data (Part 1) Historams, Distributions & Outliers_en.vtt
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12.7 KB
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006 Visualising Data (Part 2) Seaborn and Probability Density Functions.mp4
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39.4 MB
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006 Visualising Data (Part 2) Seaborn and Probability Density Functions_en.vtt
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8.0 KB
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007 Working with Index Data, Pandas Series, and Dummy Variables.mp4
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108.8 MB
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007 Working with Index Data, Pandas Series, and Dummy Variables_en.vtt
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18.4 KB
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008 Understanding Descriptive Statistics the Mean vs the Median.mp4
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43.0 MB
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008 Understanding Descriptive Statistics the Mean vs the Median_en.vtt
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10.9 KB
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009 Introduction to Correlation Understanding Strength & Direction.mp4
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13.5 MB
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009 Introduction to Correlation Understanding Strength & Direction_en.vtt
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7.5 KB
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010 Calculating Correlations and the Problem posed by Multicollinearity.mp4
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86.5 MB
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010 Calculating Correlations and the Problem posed by Multicollinearity_en.vtt
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15.9 KB
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011 Visualising Correlations with a Heatmap.mp4
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113.7 MB
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011 Visualising Correlations with a Heatmap_en.vtt
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21.7 KB
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012 Techniques to Style Scatter Plots.mp4
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87.9 MB
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012 Techniques to Style Scatter Plots_en.vtt
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18.5 KB
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013 A Note for the Next Lesson.html
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0.5 KB
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014 Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques.mp4
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183.7 MB
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014 Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques_en.vtt
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25.6 KB
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015 Understanding Multivariable Regression.mp4
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33.0 MB
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015 Understanding Multivariable Regression_en.vtt
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6.7 KB
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016 How to Shuffle and Split Training & Testing Data.mp4
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47.3 MB
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016 How to Shuffle and Split Training & Testing Data_en.vtt
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10.2 KB
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017 Running a Multivariable Regression.mp4
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42.2 MB
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017 Running a Multivariable Regression_en.vtt
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8.7 KB
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018 How to Calculate the Model Fit with R-Squared.mp4
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22.5 MB
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018 How to Calculate the Model Fit with R-Squared_en.vtt
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3.9 KB
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019 Introduction to Model Evaluation.mp4
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7.7 MB
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019 Introduction to Model Evaluation_en.vtt
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3.4 KB
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020 Improving the Model by Transforming the Data.mp4
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85.4 MB
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020 Improving the Model by Transforming the Data_en.vtt
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19.2 KB
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021 How to Interpret Coefficients using p-Values and Statistical Significance.mp4
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51.4 MB
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021 How to Interpret Coefficients using p-Values and Statistical Significance_en.vtt
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9.7 KB
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022 Understanding VIF & Testing for Multicollinearity.mp4
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110.5 MB
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022 Understanding VIF & Testing for Multicollinearity_en.vtt
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22.8 KB
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023 Model Simplification & Baysian Information Criterion.mp4
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125.5 MB
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023 Model Simplification & Baysian Information Criterion_en.vtt
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20.7 KB
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024 How to Analyse and Plot Regression Residuals.mp4
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29.4 MB
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024 How to Analyse and Plot Regression Residuals_en.vtt
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13.2 KB
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025 Residual Analysis (Part 1) Predicted vs Actual Values.mp4
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85.4 MB
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025 Residual Analysis (Part 1) Predicted vs Actual Values_en.vtt
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16.1 KB
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026 Residual Analysis (Part 2) Graphing and Comparing Regression Residuals.mp4
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104.0 MB
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026 Residual Analysis (Part 2) Graphing and Comparing Regression Residuals_en.vtt
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20.2 KB
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027 Making Predictions (Part 1) MSE & R-Squared.mp4
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132.7 MB
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027 Making Predictions (Part 1) MSE & R-Squared_en.vtt
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21.0 KB
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028 Making Predictions (Part 2) Standard Deviation, RMSE, and Prediction Intervals.mp4
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66.8 MB
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028 Making Predictions (Part 2) Standard Deviation, RMSE, and Prediction Intervals_en.vtt
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13.2 KB
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029 Build a Valuation Tool (Part 1) Working with Pandas Series & Numpy ndarrays.mp4
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107.6 MB
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029 Build a Valuation Tool (Part 1) Working with Pandas Series & Numpy ndarrays_en.vtt
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18.3 KB
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030 [Python] - Conditional Statements - Build a Valuation Tool (Part 2).mp4
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94.5 MB
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030 [Python] - Conditional Statements - Build a Valuation Tool (Part 2)_en.vtt
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18.9 KB
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031 Build a Valuation Tool (Part 3) Docstrings & Creating your own Python Module.mp4
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210.7 MB
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031 Build a Valuation Tool (Part 3) Docstrings & Creating your own Python Module_en.vtt
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25.1 KB
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032 Download the Complete Notebook Here.html
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0.2 KB
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033 Any Feedback on this Section.html
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0.5 KB
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18179918-04-Multivariable-Regression.ipynb.zip
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3.7 MB
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18179928-04-Valuation-Tool.ipynb.zip
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18905386-boston-valuation.py
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external-assets-links.txt
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/06 - Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/
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001 How to Translate a Business Problem into a Machine Learning Problem.mp4
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32.5 MB
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001 How to Translate a Business Problem into a Machine Learning Problem_en.vtt
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8.7 KB
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002 Gathering Email Data and Working with Archives & Text Editors.mp4
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100.7 MB
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002 Gathering Email Data and Working with Archives & Text Editors_en.vtt
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12.6 KB
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003 How to Add the Lesson Resources to the Project.mp4
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19.9 MB
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003 How to Add the Lesson Resources to the Project_en.vtt
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4.3 KB
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004 The Naive Bayes Algorithm and the Decision Boundary for a Classifier.mp4
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30.8 MB
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004 The Naive Bayes Algorithm and the Decision Boundary for a Classifier_en.vtt
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5.5 KB
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005 Basic Probability.mp4
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9.9 MB
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005 Basic Probability_en.vtt
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4.7 KB
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006 Joint & Conditional Probability.mp4
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92.6 MB
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006 Joint & Conditional Probability_en.vtt
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007 Bayes Theorem.mp4
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53.6 MB
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007 Bayes Theorem_en.vtt
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008 Reading Files (Part 1) Absolute Paths and Relative Paths.mp4
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41.5 MB
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008 Reading Files (Part 1) Absolute Paths and Relative Paths_en.vtt
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10.5 KB
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009 Reading Files (Part 2) Stream Objects and Email Structure.mp4
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92.0 MB
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009 Reading Files (Part 2) Stream Objects and Email Structure_en.vtt
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13.1 KB
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010 Extracting the Text in the Email Body.mp4
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32.0 MB
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010 Extracting the Text in the Email Body_en.vtt
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5.3 KB
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011 [Python] - Generator Functions & the yield Keyword.mp4
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109.3 MB
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011 [Python] - Generator Functions & the yield Keyword_en.vtt
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19.9 KB
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012 Create a Pandas DataFrame of Email Bodies.mp4
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39.2 MB
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012 Create a Pandas DataFrame of Email Bodies_en.vtt
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6.4 KB
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013 Cleaning Data (Part 1) Check for Empty Emails & Null Entries.mp4
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94.7 MB
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013 Cleaning Data (Part 1) Check for Empty Emails & Null Entries_en.vtt
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15.9 KB
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014 Cleaning Data (Part 2) Working with a DataFrame Index.mp4
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48.8 MB
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014 Cleaning Data (Part 2) Working with a DataFrame Index_en.vtt
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8.1 KB
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015 Saving a JSON File with Pandas.mp4
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45.5 MB
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015 Saving a JSON File with Pandas_en.vtt
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6.2 KB
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016 Data Visualisation (Part 1) Pie Charts.mp4
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73.5 MB
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016 Data Visualisation (Part 1) Pie Charts_en.vtt
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14.4 KB
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017 Data Visualisation (Part 2) Donut Charts.mp4
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42.9 MB
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017 Data Visualisation (Part 2) Donut Charts_en.vtt
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8.5 KB
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018 Introduction to Natural Language Processing (NLP).mp4
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39.3 MB
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018 Introduction to Natural Language Processing (NLP)_en.vtt
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7.4 KB
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019 Tokenizing, Removing Stop Words and the Python Set Data Structure.mp4
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97.0 MB
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019 Tokenizing, Removing Stop Words and the Python Set Data Structure_en.vtt
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17.0 KB
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020 Word Stemming & Removing Punctuation.mp4
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48.9 MB
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020 Word Stemming & Removing Punctuation_en.vtt
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9.6 KB
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021 Removing HTML tags with BeautifulSoup.mp4
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90.8 MB
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021 Removing HTML tags with BeautifulSoup_en.vtt
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10.0 KB
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022 Creating a Function for Text Processing.mp4
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27.6 MB
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022 Creating a Function for Text Processing_en.vtt
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7.4 KB
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023 A Note for the Next Lesson.html
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0.5 KB
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024 Advanced Subsetting on DataFrames the apply() Function.mp4
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57.9 MB
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024 Advanced Subsetting on DataFrames the apply() Function_en.vtt
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12.1 KB
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025 [Python] - Logical Operators to Create Subsets and Indices.mp4
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60.2 MB
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025 [Python] - Logical Operators to Create Subsets and Indices_en.vtt
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13.7 KB
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026 Word Clouds & How to install Additional Python Packages.mp4
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52.5 MB
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026 Word Clouds & How to install Additional Python Packages_en.vtt
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10.7 KB
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027 Creating your First Word Cloud.mp4
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47.8 MB
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027 Creating your First Word Cloud_en.vtt
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12.3 KB
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028 Styling the Word Cloud with a Mask.mp4
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111.1 MB
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028 Styling the Word Cloud with a Mask_en.vtt
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14.8 KB
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029 Solving the Hamlet Challenge.mp4
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49.1 MB
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029 Solving the Hamlet Challenge_en.vtt
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5.3 KB
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030 Styling Word Clouds with Custom Fonts.mp4
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104.3 MB
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030 Styling Word Clouds with Custom Fonts_en.vtt
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13.2 KB
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031 Create the Vocabulary for the Spam Classifier.mp4
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73.5 MB
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031 Create the Vocabulary for the Spam Classifier_en.vtt
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15.8 KB
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032 Coding Challenge Check for Membership in a Collection.mp4
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15.6 MB
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032 Coding Challenge Check for Membership in a Collection_en.vtt
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5.4 KB
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033 Coding Challenge Find the Longest Email.mp4
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43.1 MB
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033 Coding Challenge Find the Longest Email_en.vtt
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6.7 KB
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034 Sparse Matrix (Part 1) Split the Training and Testing Data.mp4
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60.9 MB
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034 Sparse Matrix (Part 1) Split the Training and Testing Data_en.vtt
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13.5 KB
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035 Sparse Matrix (Part 2) Data Munging with Nested Loops.mp4
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96.0 MB
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035 Sparse Matrix (Part 2) Data Munging with Nested Loops_en.vtt
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19.9 KB
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036 Sparse Matrix (Part 3) Using groupby() and Saving .txt Files.mp4
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64.3 MB
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036 Sparse Matrix (Part 3) Using groupby() and Saving .txt Files_en.vtt
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10.7 KB
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037 Coding Challenge Solution Preparing the Test Data.mp4
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19.8 MB
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037 Coding Challenge Solution Preparing the Test Data_en.vtt
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038 Checkpoint Understanding the Data.mp4
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038 Checkpoint Understanding the Data_en.vtt
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039 Download the Complete Notebook Here.html
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040 Any Feedback on this Section.html
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0.5 KB
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/07 - Train a Naive Bayes Classifier to Create a Spam Filter Part 2/
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001 Setting up the Notebook and Understanding Delimiters in a Dataset.mp4
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56.6 MB
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001 Setting up the Notebook and Understanding Delimiters in a Dataset_en.vtt
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002 Create a Full Matrix.mp4
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002 Create a Full Matrix_en.vtt
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003 Count the Tokens to Train the Naive Bayes Model.mp4
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003 Count the Tokens to Train the Naive Bayes Model_en.vtt
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004 Sum the Tokens across the Spam and Ham Subsets.mp4
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004 Sum the Tokens across the Spam and Ham Subsets_en.vtt
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005 Calculate the Token Probabilities and Save the Trained Model.mp4
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006 Coding Challenge Prepare the Test Data.mp4
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007 Download the Complete Notebook Here.html
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008 Any Feedback on this Section.html
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/08 - Test and Evaluate a Naive Bayes Classifier Part 3/
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001 Set up the Testing Notebook.mp4
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20.9 MB
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001 Set up the Testing Notebook_en.vtt
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002 Joint Conditional Probability (Part 1) Dot Product.mp4
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002 Joint Conditional Probability (Part 1) Dot Product_en.vtt
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003 Joint Conditional Probablity (Part 2) Priors.mp4
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003 Joint Conditional Probablity (Part 2) Priors_en.vtt
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004 Making Predictions Comparing Joint Probabilities.mp4
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38.9 MB
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004 Making Predictions Comparing Joint Probabilities_en.vtt
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005 The Accuracy Metric.mp4
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30.1 MB
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005 The Accuracy Metric_en.vtt
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006 Visualising the Decision Boundary.mp4
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006 Visualising the Decision Boundary_en.vtt
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007 False Positive vs False Negatives.mp4
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007 False Positive vs False Negatives_en.vtt
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008 The Recall Metric.mp4
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008 The Recall Metric_en.vtt
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009 The Precision Metric.mp4
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009 The Precision Metric_en.vtt
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010 The F-score or F1 Metric.mp4
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010 The F-score or F1 Metric_en.vtt
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011 A Naive Bayes Implementation using SciKit Learn.mp4
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152.8 MB
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011 A Naive Bayes Implementation using SciKit Learn_en.vtt
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012 Download the Complete Notebook Here.html
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013 Any Feedback on this Section.html
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0.5 KB
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18180294-07-Bayes-Classifier-Testing-Inference-Evaluation.ipynb.zip
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/.../0. Websites you may like/
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[CourseClub.Me].url
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/09 - Introduction to Neural Networks and How to Use Pre-Trained Models/
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001 The Human Brain and the Inspiration for Artificial Neural Networks.mp4
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34.3 MB
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001 The Human Brain and the Inspiration for Artificial Neural Networks_en.vtt
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002 Layers, Feature Generation and Learning.mp4
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002 Layers, Feature Generation and Learning_en.vtt
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003 Costs and Disadvantages of Neural Networks.mp4
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003 Costs and Disadvantages of Neural Networks_en.vtt
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004 Preprocessing Image Data and How RGB Works.mp4
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72.6 MB
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004 Preprocessing Image Data and How RGB Works_en.vtt
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005 Importing Keras Models and the Tensorflow Graph.mp4
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51.6 MB
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005 Importing Keras Models and the Tensorflow Graph_en.vtt
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006 Making Predictions using InceptionResNet.mp4
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108.2 MB
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006 Making Predictions using InceptionResNet_en.vtt
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007 Coding Challenge Solution Using other Keras Models.mp4
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86.0 MB
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007 Coding Challenge Solution Using other Keras Models_en.vtt
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008 Download the Complete Notebook Here.html
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009 Any Feedback on this Section.html
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0.5 KB
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18180490-09-Neural-Nets-Pretrained-Image-Classification.ipynb.zip
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/10 - Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/
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001 Solving a Business Problem with Image Classification.mp4
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20.4 MB
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001 Solving a Business Problem with Image Classification_en.vtt
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002 Installing Tensorflow and Keras for Jupyter.mp4
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002 Installing Tensorflow and Keras for Jupyter_en.vtt
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003 Gathering the CIFAR 10 Dataset.mp4
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21.6 MB
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003 Gathering the CIFAR 10 Dataset_en.vtt
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004 Exploring the CIFAR Data.mp4
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004 Exploring the CIFAR Data_en.vtt
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005 Pre-processing Scaling Inputs and Creating a Validation Dataset.mp4
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006 Compiling a Keras Model and Understanding the Cross Entropy Loss Function.mp4
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006 Compiling a Keras Model and Understanding the Cross Entropy Loss Function_en.vtt
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007 Interacting with the Operating System and the Python Try-Catch Block.mp4
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48.0 MB
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007 Interacting with the Operating System and the Python Try-Catch Block_en.vtt
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008 Fit a Keras Model and Use Tensorboard to Visualise Learning and Spot Problems.mp4
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008 Fit a Keras Model and Use Tensorboard to Visualise Learning and Spot Problems_en.vtt
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009 Use Regularisation to Prevent Overfitting Early Stopping & Dropout Techniques.mp4
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159.9 MB
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009 Use Regularisation to Prevent Overfitting Early Stopping & Dropout Techniques_en.vtt
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010 Use the Model to Make Predictions.mp4
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010 Use the Model to Make Predictions_en.vtt
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011 Model Evaluation and the Confusion Matrix.mp4
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011 Model Evaluation and the Confusion Matrix_en.vtt
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012 Model Evaluation and the Confusion Matrix.mp4
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012 Model Evaluation and the Confusion Matrix_en.vtt
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013 Download the Complete Notebook Here.html
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014 Any Feedback on this Section.html
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0.5 KB
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/11 - Use Tensorflow to Classify Handwritten Digits/
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001 What's coming up.mp4
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001 What's coming up_en.vtt
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002 Getting the Data and Loading it into Numpy Arrays.mp4
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002 Getting the Data and Loading it into Numpy Arrays_en.vtt
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003 Data Exploration and Understanding the Structure of the Input Data.mp4
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004 Data Preprocessing One-Hot Encoding and Creating the Validation Dataset.mp4
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005 What is a Tensor.mp4
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005 What is a Tensor_en.vtt
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006 Creating Tensors and Setting up the Neural Network Architecture.mp4
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006 Creating Tensors and Setting up the Neural Network Architecture_en.vtt
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007 Defining the Cross Entropy Loss Function, the Optimizer and the Metrics.mp4
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008 TensorFlow Sessions and Batching Data.mp4
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008 TensorFlow Sessions and Batching Data_en.vtt
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009 Tensorboard Summaries and the Filewriter.mp4
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010 Understanding the Tensorflow Graph Nodes and Edges.mp4
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010 Understanding the Tensorflow Graph Nodes and Edges_en.vtt
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011 Name Scoping and Image Visualisation in Tensorboard.mp4
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012 Different Model Architectures Experimenting with Dropout.mp4
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012 Different Model Architectures Experimenting with Dropout_en.vtt
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013 Prediction and Model Evaluation.mp4
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013 Prediction and Model Evaluation_en.vtt
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014 Download the Complete Notebook Here.html
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015 Any Feedback on this Section.html
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001 What you'll make.mp4
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001 What you'll make_en.vtt
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002 Saving Tensorflow Models.mp4
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002 Saving Tensorflow Models_en.vtt
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003 Loading a SavedModel.mp4
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004 Converting a Model to Tensorflow.js.mp4
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006 HTML and CSS Styling.mp4
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010 Drawing on an HTML Canvas.mp4
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011 Data Pre-Processing for Tensorflow.js.mp4
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012 Introduction to OpenCV.mp4
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014 Calculating the Centre of Mass and Shifting the Image.mp4
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015 Making a Prediction from a Digit drawn on the HTML Canvas.mp4
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016 Adding the Game Logic.mp4
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016 Adding the Game Logic_en.vtt
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017 Publish and Share your Website!.mp4
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017 Publish and Share your Website!_en.vtt
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018 Any Feedback on this Section.html
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/13 - Next Steps/
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001 Where next.html
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002 What Modules Do You Want to See.html
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003 Stay in Touch!.html
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