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[FreeCourseSite.com] Udemy - Complete 2022 Data Science & Machine Learning Bootcamp
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04 - Introduction to Optimisation and the Gradient Descent Algorithm
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007 [Python] - Advanced Functions and the Pitfalls of Optimisation (Part 1).mp4 - 229.5 MB
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009 Understanding the Learning Rate.mp4 - 190.14 MB
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010 How to Create 3-Dimensional Charts.mp4 - 152.02 MB
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008 [Python] - Tuples and the Pitfalls of Optimisation (Part 2).mp4 - 145.22 MB
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013 [Python] - Loops and Performance Considerations.mp4 - 106.59 MB
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011 Understanding Partial Derivatives and How to use SymPy.mp4 - 102.67 MB
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020 Plotting the Mean Squared Error (MSE) on a Surface (Part 2).mp4 - 96.91 MB
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014 Reshaping and Slicing N-Dimensional Arrays.mp4 - 95.11 MB
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006 [Python] - Loops and the Gradient Descent Algorithm.mp4 - 92.92 MB
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021 Running Gradient Descent with a MSE Cost Function.mp4 - 74.32 MB
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012 Implementing Batch Gradient Descent with SymPy.mp4 - 65.49 MB
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005 Understanding the Power Rule & Creating Charts with Subplots.mp4 - 59.49 MB
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017 Transposing and Reshaping Arrays.mp4 - 58.02 MB
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018 Implementing a MSE Cost Function.mp4 - 54.55 MB
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019 Understanding Nested Loops and Plotting the MSE Function (Part 1).mp4 - 48.88 MB
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004 LaTeX Markdown and Generating Data with Numpy.mp4 - 47.01 MB
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016 Introduction to the Mean Squared Error (MSE).mp4 - 43.24 MB
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003 Introduction to Cost Functions.mp4 - 39.02 MB
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022 Visualising the Optimisation on a 3D Surface.mp4 - 35.61 MB
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015 Concatenating Numpy Arrays.mp4 - 32.48 MB
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001 What's Coming Up.mp4 - 12.92 MB
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002 How a Machine Learns.mp4 - 10.47 MB
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18179908-03-Gradient-Descent.ipynb.zip - 1.14 MB
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006 [Python] - Loops and the Gradient Descent Algorithm_en.vtt - 37.63 KB
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007 [Python] - Advanced Functions and the Pitfalls of Optimisation (Part 1)_en.vtt - 37.23 KB
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009 Understanding the Learning Rate_en.vtt - 32.56 KB
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008 [Python] - Tuples and the Pitfalls of Optimisation (Part 2)_en.vtt - 29.03 KB
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010 How to Create 3-Dimensional Charts_en.vtt - 22.61 KB
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014 Reshaping and Slicing N-Dimensional Arrays_en.vtt - 19.97 KB
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021 Running Gradient Descent with a MSE Cost Function_en.vtt - 19.24 KB
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011 Understanding Partial Derivatives and How to use SymPy_en.vtt - 17.62 KB
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013 [Python] - Loops and Performance Considerations_en.vtt - 15.67 KB
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005 Understanding the Power Rule & Creating Charts with Subplots_en.vtt - 15.59 KB
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020 Plotting the Mean Squared Error (MSE) on a Surface (Part 2)_en.vtt - 14.98 KB
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004 LaTeX Markdown and Generating Data with Numpy_en.vtt - 14.82 KB
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019 Understanding Nested Loops and Plotting the MSE Function (Part 1)_en.vtt - 12.12 KB
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018 Implementing a MSE Cost Function_en.vtt - 11.74 KB
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017 Transposing and Reshaping Arrays_en.vtt - 11.69 KB
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012 Implementing Batch Gradient Descent with SymPy_en.vtt - 11.26 KB
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016 Introduction to the Mean Squared Error (MSE)_en.vtt - 11.02 KB
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022 Visualising the Optimisation on a 3D Surface_en.vtt - 9.35 KB
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003 Introduction to Cost Functions_en.vtt - 8.19 KB
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015 Concatenating Numpy Arrays_en.vtt - 7.8 KB
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002 How a Machine Learns_en.vtt - 6.32 KB
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001 What's Coming Up_en.vtt - 3.36 KB
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024 Any Feedback on this Section.html - 520 B
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023 Download the Complete Notebook Here.html - 242 B
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external-assets-links.txt - 83 B
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12 - Serving a Tensorflow Model through a Website
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014 Calculating the Centre of Mass and Shifting the Image.mp4 - 210.44 MB
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007 Loading a Tensorflow.js Model and Starting your own Server.mp4 - 175.41 MB
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009 Styling an HTML Canvas.mp4 - 172.69 MB
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010 Drawing on an HTML Canvas.mp4 - 159.28 MB
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016 Adding the Game Logic.mp4 - 158.22 MB
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013 Resizing and Adding Padding to Images.mp4 - 147.83 MB
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006 HTML and CSS Styling.mp4 - 136.76 MB
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012 Introduction to OpenCV.mp4 - 133.24 MB
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002 Saving Tensorflow Models.mp4 - 103.67 MB
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015 Making a Prediction from a Digit drawn on the HTML Canvas.mp4 - 98.46 MB
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004 Converting a Model to Tensorflow.js.mp4 - 93.61 MB
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003 Loading a SavedModel.mp4 - 85.1 MB
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005 Introducing the Website Project and Tooling.mp4 - 68.79 MB
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001 What you'll make.mp4 - 35.52 MB
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017 Publish and Share your Website!.mp4 - 33.31 MB
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011 Data Pre-Processing for Tensorflow.js.mp4 - 25.56 MB
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008 Adding a Favicon.mp4 - 24.34 MB
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21028926-math-garden-stub-complete.zip - 4.09 MB
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21028932-math-garden-stub-12.12-checkpoint.zip - 4.09 MB
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21028876-MNIST-Model-Load-Files.zip - 2.84 MB
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21028894-TFJS.zip - 1.54 MB
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21028914-math-garden-stub.zip - 44.03 KB
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009 Styling an HTML Canvas_en.vtt - 35.37 KB
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012 Introduction to OpenCV_en.vtt - 34.43 KB
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006 HTML and CSS Styling_en.vtt - 33.85 KB
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007 Loading a Tensorflow.js Model and Starting your own Server_en.vtt - 33.67 KB
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016 Adding the Game Logic_en.vtt - 33.58 KB
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010 Drawing on an HTML Canvas_en.vtt - 33.21 KB
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014 Calculating the Centre of Mass and Shifting the Image_en.vtt - 32.1 KB
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013 Resizing and Adding Padding to Images_en.vtt - 24.19 KB
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003 Loading a SavedModel_en.vtt - 23.15 KB
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002 Saving Tensorflow Models_en.vtt - 19 KB
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004 Converting a Model to Tensorflow.js_en.vtt - 18.79 KB
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005 Introducing the Website Project and Tooling_en.vtt - 15.68 KB
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015 Making a Prediction from a Digit drawn on the HTML Canvas_en.vtt - 15.37 KB
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011 Data Pre-Processing for Tensorflow.js_en.vtt - 10.73 KB
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001 What you'll make_en.vtt - 8.73 KB
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017 Publish and Share your Website!_en.vtt - 8.19 KB
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008 Adding a Favicon_en.vtt - 6.5 KB
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21028850-11-Neural-Networks-TF-Handwriting-Recognition.ipynb.zip - 6.39 KB
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21028968-12-TF-SavedModel-Export-Completed.ipynb.zip - 6.13 KB
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21028978-x-test0-ylabel7.txt - 4.59 KB
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21028982-x-test1-ylabel2.txt - 4.59 KB
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21028988-x-test2-ylabel1.txt - 4.59 KB
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018 Any Feedback on this Section.html - 500 B
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05 - Predict House Prices with Multivariable Linear Regression
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031 Build a Valuation Tool (Part 3) Docstrings & Creating your own Python Module.mp4 - 200.93 MB
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014 Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques.mp4 - 175.24 MB
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027 Making Predictions (Part 1) MSE & R-Squared.mp4 - 126.58 MB
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023 Model Simplification & Baysian Information Criterion.mp4 - 119.72 MB
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011 Visualising Correlations with a Heatmap.mp4 - 108.43 MB
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004 Clean and Explore the Data (Part 2) Find Missing Values.mp4 - 107.48 MB
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022 Understanding VIF & Testing for Multicollinearity.mp4 - 105.42 MB
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007 Working with Index Data, Pandas Series, and Dummy Variables.mp4 - 103.76 MB
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029 Build a Valuation Tool (Part 1) Working with Pandas Series & Numpy ndarrays.mp4 - 102.57 MB
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026 Residual Analysis (Part 2) Graphing and Comparing Regression Residuals.mp4 - 99.19 MB
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030 [Python] - Conditional Statements - Build a Valuation Tool (Part 2).mp4 - 90.12 MB
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012 Techniques to Style Scatter Plots.mp4 - 83.87 MB
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010 Calculating Correlations and the Problem posed by Multicollinearity.mp4 - 82.51 MB
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025 Residual Analysis (Part 1) Predicted vs Actual Values.mp4 - 81.48 MB
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020 Improving the Model by Transforming the Data.mp4 - 81.41 MB
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028 Making Predictions (Part 2) Standard Deviation, RMSE, and Prediction Intervals.mp4 - 63.72 MB
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003 Clean and Explore the Data (Part 1) Understand the Nature of the Dataset.mp4 - 56.73 MB
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021 How to Interpret Coefficients using p-Values and Statistical Significance.mp4 - 49.02 MB
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002 Gathering the Boston House Price Data.mp4 - 47.58 MB
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016 How to Shuffle and Split Training & Testing Data.mp4 - 45.09 MB
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005 Visualising Data (Part 1) Historams, Distributions & Outliers.mp4 - 42.62 MB
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008 Understanding Descriptive Statistics the Mean vs the Median.mp4 - 41.03 MB
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017 Running a Multivariable Regression.mp4 - 40.2 MB
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006 Visualising Data (Part 2) Seaborn and Probability Density Functions.mp4 - 37.6 MB
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015 Understanding Multivariable Regression.mp4 - 31.51 MB
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001 Defining the Problem.mp4 - 30.02 MB
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024 How to Analyse and Plot Regression Residuals.mp4 - 28.05 MB
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018 How to Calculate the Model Fit with R-Squared.mp4 - 21.48 MB
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009 Introduction to Correlation Understanding Strength & Direction.mp4 - 12.92 MB
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019 Introduction to Model Evaluation.mp4 - 7.34 MB
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18179918-04-Multivariable-Regression.ipynb.zip - 3.54 MB
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014 Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques_en.vtt - 25 KB
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031 Build a Valuation Tool (Part 3) Docstrings & Creating your own Python Module_en.vtt - 24.52 KB
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022 Understanding VIF & Testing for Multicollinearity_en.vtt - 22.29 KB
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011 Visualising Correlations with a Heatmap_en.vtt - 21.22 KB
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027 Making Predictions (Part 1) MSE & R-Squared_en.vtt - 20.51 KB
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023 Model Simplification & Baysian Information Criterion_en.vtt - 20.18 KB
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026 Residual Analysis (Part 2) Graphing and Comparing Regression Residuals_en.vtt - 19.68 KB
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020 Improving the Model by Transforming the Data_en.vtt - 18.78 KB
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030 [Python] - Conditional Statements - Build a Valuation Tool (Part 2)_en.vtt - 18.44 KB
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012 Techniques to Style Scatter Plots_en.vtt - 18.04 KB
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007 Working with Index Data, Pandas Series, and Dummy Variables_en.vtt - 17.97 KB
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029 Build a Valuation Tool (Part 1) Working with Pandas Series & Numpy ndarrays_en.vtt - 17.87 KB
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004 Clean and Explore the Data (Part 2) Find Missing Values_en.vtt - 16.27 KB
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025 Residual Analysis (Part 1) Predicted vs Actual Values_en.vtt - 15.74 KB
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010 Calculating Correlations and the Problem posed by Multicollinearity_en.vtt - 15.48 KB
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003 Clean and Explore the Data (Part 1) Understand the Nature of the Dataset_en.vtt - 13.66 KB
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028 Making Predictions (Part 2) Standard Deviation, RMSE, and Prediction Intervals_en.vtt - 12.88 KB
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024 How to Analyse and Plot Regression Residuals_en.vtt - 12.85 KB
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005 Visualising Data (Part 1) Historams, Distributions & Outliers_en.vtt - 12.39 KB
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008 Understanding Descriptive Statistics the Mean vs the Median_en.vtt - 10.64 KB
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016 How to Shuffle and Split Training & Testing Data_en.vtt - 9.99 KB
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021 How to Interpret Coefficients using p-Values and Statistical Significance_en.vtt - 9.48 KB
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017 Running a Multivariable Regression_en.vtt - 8.53 KB
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006 Visualising Data (Part 2) Seaborn and Probability Density Functions_en.vtt - 7.84 KB
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002 Gathering the Boston House Price Data_en.vtt - 7.55 KB
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009 Introduction to Correlation Understanding Strength & Direction_en.vtt - 7.37 KB
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015 Understanding Multivariable Regression_en.vtt - 6.57 KB
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001 Defining the Problem_en.vtt - 5.61 KB
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018 How to Calculate the Model Fit with R-Squared_en.vtt - 3.84 KB
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019 Introduction to Model Evaluation_en.vtt - 3.31 KB
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18905386-boston-valuation.py - 3.05 KB
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18179928-04-Valuation-Tool.ipynb.zip - 2.93 KB
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033 Any Feedback on this Section.html - 512 B
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013 A Note for the Next Lesson.html - 476 B
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032 Download the Complete Notebook Here.html - 242 B
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external-assets-links.txt - 83 B
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10 - Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow
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012 Model Evaluation and the Confusion Matrix.mp4 - 193.26 MB
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010 Use the Model to Make Predictions.mp4 - 173.85 MB
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009 Use Regularisation to Prevent Overfitting Early Stopping & Dropout Techniques.mp4 - 152.54 MB
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004 Exploring the CIFAR Data.mp4 - 81.16 MB
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008 Fit a Keras Model and Use Tensorboard to Visualise Learning and Spot Problems.mp4 - 76.55 MB
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006 Compiling a Keras Model and Understanding the Cross Entropy Loss Function.mp4 - 76.04 MB
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005 Pre-processing Scaling Inputs and Creating a Validation Dataset.mp4 - 61.32 MB
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007 Interacting with the Operating System and the Python Try-Catch Block.mp4 - 45.79 MB
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011 Model Evaluation and the Confusion Matrix.mp4 - 40.05 MB
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002 Installing Tensorflow and Keras for Jupyter.mp4 - 31.92 MB
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003 Gathering the CIFAR 10 Dataset.mp4 - 20.6 MB
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001 Solving a Business Problem with Image Classification.mp4 - 19.47 MB
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18187728-10-Neural-Nets-Keras-CIFAR10-Classification.ipynb.zip - 120.11 KB
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012 Model Evaluation and the Confusion Matrix_en.vtt - 35.46 KB
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010 Use the Model to Make Predictions_en.vtt - 30.18 KB
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009 Use Regularisation to Prevent Overfitting Early Stopping & Dropout Techniques_en.vtt - 25.08 KB
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007 Interacting with the Operating System and the Python Try-Catch Block_en.vtt - 21.52 KB
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005 Pre-processing Scaling Inputs and Creating a Validation Dataset_en.vtt - 17.8 KB
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006 Compiling a Keras Model and Understanding the Cross Entropy Loss Function_en.vtt - 16.58 KB
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004 Exploring the CIFAR Data_en.vtt - 16.11 KB
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008 Fit a Keras Model and Use Tensorboard to Visualise Learning and Spot Problems_en.vtt - 12.76 KB
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011 Model Evaluation and the Confusion Matrix_en.vtt - 9.6 KB
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002 Installing Tensorflow and Keras for Jupyter_en.vtt - 5.9 KB
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003 Gathering the CIFAR 10 Dataset_en.vtt - 5.48 KB
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001 Solving a Business Problem with Image Classification_en.vtt - 4.49 KB
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014 Any Feedback on this Section.html - 521 B
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013 Download the Complete Notebook Here.html - 242 B
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external-assets-links.txt - 83 B
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03 - Python Programming for Data Science and Machine Learning
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008 [Python] - Module Imports.mp4 - 186.86 MB
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013 How to Make Sense of Python Documentation for Data Visualisation.mp4 - 138.12 MB
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014 Working with Python Objects to Analyse Data.mp4 - 135.45 MB
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012 [Python] - Objects - Understanding Attributes and Methods.mp4 - 125.35 MB
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007 [Python & Pandas] - Dataframes and Series.mp4 - 101.4 MB
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010 [Python] - Functions - Part 2 Arguments & Parameters.mp4 - 99.44 MB
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015 [Python] - Tips, Code Style and Naming Conventions.mp4 - 67.18 MB
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011 [Python] - Functions - Part 3 Results & Return Values.mp4 - 54.13 MB
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005 [Python] - Variables and Types.mp4 - 47.55 MB
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002 Mac Users - Install Anaconda.mp4 - 39.12 MB
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006 [Python] - Lists and Arrays.mp4 - 35.09 MB
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001 Windows Users - Install Anaconda.mp4 - 32.19 MB
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009 [Python] - Functions - Part 1 Defining and Calling Functions.mp4 - 27.38 MB
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003 Does LSD Make You Better at Maths.mp4 - 15.63 MB
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18204473-12-Rules-to-Learn-to-Code.pdf - 2.25 MB
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18179882-02-Python-Intro.ipynb.zip - 36.44 KB
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008 [Python] - Module Imports_en.vtt - 31.45 KB
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012 [Python] - Objects - Understanding Attributes and Methods_en.vtt - 25.93 KB
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007 [Python & Pandas] - Dataframes and Series_en.vtt - 24.43 KB
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014 Working with Python Objects to Analyse Data_en.vtt - 23.55 KB
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013 How to Make Sense of Python Documentation for Data Visualisation_en.vtt - 23.06 KB
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010 [Python] - Functions - Part 2 Arguments & Parameters_en.vtt - 18.18 KB
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015 [Python] - Tips, Code Style and Naming Conventions_en.vtt - 14.66 KB
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005 [Python] - Variables and Types_en.vtt - 14.49 KB
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011 [Python] - Functions - Part 3 Results & Return Values_en.vtt - 14.46 KB
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006 [Python] - Lists and Arrays_en.vtt - 10.55 KB
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009 [Python] - Functions - Part 1 Defining and Calling Functions_en.vtt - 9.15 KB
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001 Windows Users - Install Anaconda_en.vtt - 7.72 KB
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002 Mac Users - Install Anaconda_en.vtt - 7.07 KB
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003 Does LSD Make You Better at Maths_en.vtt - 6.42 KB
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004 Download the 12 Rules to Learn to Code.html - 1.12 KB
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017 Any Feedback on this Section.html - 513 B
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016 Download the Complete Notebook Here.html - 242 B
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18877814-lsd-math-score-data.csv - 155 B
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external-assets-links.txt - 83 B
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11 - Use Tensorflow to Classify Handwritten Digits
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012 Different Model Architectures Experimenting with Dropout.mp4 - 173.82 MB
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006 Creating Tensors and Setting up the Neural Network Architecture.mp4 - 110.62 MB
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009 Tensorboard Summaries and the Filewriter.mp4 - 98.73 MB
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010 Understanding the Tensorflow Graph Nodes and Edges.mp4 - 89.48 MB
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013 Prediction and Model Evaluation.mp4 - 87.34 MB
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008 TensorFlow Sessions and Batching Data.mp4 - 73.63 MB
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011 Name Scoping and Image Visualisation in Tensorboard.mp4 - 66.81 MB
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007 Defining the Cross Entropy Loss Function, the Optimizer and the Metrics.mp4 - 49.63 MB
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004 Data Preprocessing One-Hot Encoding and Creating the Validation Dataset.mp4 - 49.34 MB
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002 Getting the Data and Loading it into Numpy Arrays.mp4 - 39.66 MB
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005 What is a Tensor.mp4 - 37.85 MB
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003 Data Exploration and Understanding the Structure of the Input Data.mp4 - 20.6 MB
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18194656-MNIST.zip - 14.77 MB
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001 What's coming up.mp4 - 5.23 MB
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012 Different Model Architectures Experimenting with Dropout_en.vtt - 27.03 KB
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006 Creating Tensors and Setting up the Neural Network Architecture_en.vtt - 26.2 KB
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011 Name Scoping and Image Visualisation in Tensorboard_en.vtt - 23.85 KB
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009 Tensorboard Summaries and the Filewriter_en.vtt - 21.25 KB
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010 Understanding the Tensorflow Graph Nodes and Edges_en.vtt - 18.52 KB
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008 TensorFlow Sessions and Batching Data_en.vtt - 18.27 KB
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013 Prediction and Model Evaluation_en.vtt - 16.75 KB
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007 Defining the Cross Entropy Loss Function, the Optimizer and the Metrics_en.vtt - 12.68 KB
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004 Data Preprocessing One-Hot Encoding and Creating the Validation Dataset_en.vtt - 11.48 KB
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002 Getting the Data and Loading it into Numpy Arrays_en.vtt - 8.19 KB
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005 What is a Tensor_en.vtt - 8.18 KB
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18187740-11-Neural-Networks-TF-Handwriting-Recognition.ipynb.zip - 6.6 KB
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003 Data Exploration and Understanding the Structure of the Input Data_en.vtt - 5.73 KB
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001 What's coming up_en.vtt - 2.32 KB
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015 Any Feedback on this Section.html - 499 B
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014 Download the Complete Notebook Here.html - 242 B
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external-assets-links.txt - 83 B
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08 - Test and Evaluate a Naive Bayes Classifier Part 3
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006 Visualising the Decision Boundary.mp4 - 149.45 MB
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011 A Naive Bayes Implementation using SciKit Learn.mp4 - 145.69 MB
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002 Joint Conditional Probability (Part 1) Dot Product.mp4 - 47.03 MB
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003 Joint Conditional Probablity (Part 2) Priors.mp4 - 45.8 MB
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007 False Positive vs False Negatives.mp4 - 41.39 MB
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004 Making Predictions Comparing Joint Probabilities.mp4 - 37.12 MB
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009 The Precision Metric.mp4 - 34.47 MB
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005 The Accuracy Metric.mp4 - 28.66 MB
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18190700-SpamData.zip - 22.83 MB
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001 Set up the Testing Notebook.mp4 - 19.97 MB
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008 The Recall Metric.mp4 - 18.41 MB
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010 The F-score or F1 Metric.mp4 - 16.46 MB
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18180294-07-Bayes-Classifier-Testing-Inference-Evaluation.ipynb.zip - 243.05 KB
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006 Visualising the Decision Boundary_en.vtt - 30.3 KB
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011 A Naive Bayes Implementation using SciKit Learn_en.vtt - 29.27 KB
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18180296-08-Naive-Bayes-with-scikit-learn.ipynb.zip - 13.26 KB
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007 False Positive vs False Negatives_en.vtt - 11.47 KB
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002 Joint Conditional Probability (Part 1) Dot Product_en.vtt - 11.06 KB
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003 Joint Conditional Probablity (Part 2) Priors_en.vtt - 9.53 KB
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004 Making Predictions Comparing Joint Probabilities_en.vtt - 8.78 KB
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009 The Precision Metric_en.vtt - 8.42 KB
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005 The Accuracy Metric_en.vtt - 6.68 KB
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008 The Recall Metric_en.vtt - 5.76 KB
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010 The F-score or F1 Metric_en.vtt - 4.47 KB
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001 Set up the Testing Notebook_en.vtt - 3.34 KB
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013 Any Feedback on this Section.html - 509 B
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012 Download the Complete Notebook Here.html - 242 B
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external-assets-links.txt - 83 B
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09 - Introduction to Neural Networks and How to Use Pre-Trained Models
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002 Layers, Feature Generation and Learning.mp4 - 124.35 MB
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006 Making Predictions using InceptionResNet.mp4 - 103.2 MB
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007 Coding Challenge Solution Using other Keras Models.mp4 - 81.98 MB
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003 Costs and Disadvantages of Neural Networks.mp4 - 76.56 MB
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004 Preprocessing Image Data and How RGB Works.mp4 - 69.24 MB
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005 Importing Keras Models and the Tensorflow Graph.mp4 - 49.24 MB
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001 The Human Brain and the Inspiration for Artificial Neural Networks.mp4 - 32.71 MB
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18180490-09-Neural-Nets-Pretrained-Image-Classification.ipynb.zip - 571.83 KB
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18188466-TF-Keras-Classification-Images.zip - 501.1 KB
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002 Layers, Feature Generation and Learning_en.vtt - 24.61 KB
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006 Making Predictions using InceptionResNet_en.vtt - 17.08 KB
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003 Costs and Disadvantages of Neural Networks_en.vtt - 16.96 KB
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004 Preprocessing Image Data and How RGB Works_en.vtt - 14.37 KB
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007 Coding Challenge Solution Using other Keras Models_en.vtt - 11.83 KB
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005 Importing Keras Models and the Tensorflow Graph_en.vtt - 10.32 KB
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001 The Human Brain and the Inspiration for Artificial Neural Networks_en.vtt - 9.96 KB
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009 Any Feedback on this Section.html - 526 B
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008 Download the Complete Notebook Here.html - 264 B
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0. Websites you may like
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[FreeCourseSite.com].url - 127 B
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[CourseClub.Me].url - 122 B
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[GigaCourse.Com].url - 49 B
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external-assets-links.txt - 83 B
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06 - Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1
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028 Styling the Word Cloud with a Mask.mp4 - 105.94 MB
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011 [Python] - Generator Functions & the yield Keyword.mp4 - 104.22 MB
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030 Styling Word Clouds with Custom Fonts.mp4 - 99.44 MB
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002 Gathering Email Data and Working with Archives & Text Editors.mp4 - 95.99 MB
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019 Tokenizing, Removing Stop Words and the Python Set Data Structure.mp4 - 92.53 MB
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035 Sparse Matrix (Part 2) Data Munging with Nested Loops.mp4 - 91.56 MB
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013 Cleaning Data (Part 1) Check for Empty Emails & Null Entries.mp4 - 90.33 MB
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006 Joint & Conditional Probability.mp4 - 88.31 MB
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009 Reading Files (Part 2) Stream Objects and Email Structure.mp4 - 87.76 MB
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021 Removing HTML tags with BeautifulSoup.mp4 - 86.57 MB
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038 Checkpoint Understanding the Data.mp4 - 74.56 MB
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031 Create the Vocabulary for the Spam Classifier.mp4 - 70.09 MB
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016 Data Visualisation (Part 1) Pie Charts.mp4 - 70.09 MB
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036 Sparse Matrix (Part 3) Using groupby() and Saving .txt Files.mp4 - 61.3 MB
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034 Sparse Matrix (Part 1) Split the Training and Testing Data.mp4 - 58.08 MB
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025 [Python] - Logical Operators to Create Subsets and Indices.mp4 - 57.42 MB
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024 Advanced Subsetting on DataFrames the apply() Function.mp4 - 55.26 MB
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007 Bayes Theorem.mp4 - 51.07 MB
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026 Word Clouds & How to install Additional Python Packages.mp4 - 50.04 MB
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029 Solving the Hamlet Challenge.mp4 - 46.79 MB
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020 Word Stemming & Removing Punctuation.mp4 - 46.64 MB
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014 Cleaning Data (Part 2) Working with a DataFrame Index.mp4 - 46.5 MB
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027 Creating your First Word Cloud.mp4 - 45.54 MB
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015 Saving a JSON File with Pandas.mp4 - 43.41 MB
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033 Coding Challenge Find the Longest Email.mp4 - 41.1 MB
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017 Data Visualisation (Part 2) Donut Charts.mp4 - 40.95 MB
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008 Reading Files (Part 1) Absolute Paths and Relative Paths.mp4 - 39.54 MB
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018 Introduction to Natural Language Processing (NLP).mp4 - 37.45 MB
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012 Create a Pandas DataFrame of Email Bodies.mp4 - 37.37 MB
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001 How to Translate a Business Problem into a Machine Learning Problem.mp4 - 31.01 MB
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010 Extracting the Text in the Email Body.mp4 - 30.49 MB
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004 The Naive Bayes Algorithm and the Decision Boundary for a Classifier.mp4 - 29.41 MB
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022 Creating a Function for Text Processing.mp4 - 26.36 MB
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18190724-SpamData.zip - 21.28 MB
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003 How to Add the Lesson Resources to the Project.mp4 - 18.95 MB
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037 Coding Challenge Solution Preparing the Test Data.mp4 - 18.88 MB
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032 Coding Challenge Check for Membership in a Collection.mp4 - 14.85 MB
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005 Basic Probability.mp4 - 9.4 MB
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18179924-06-Bayes-Classifier-Pre-Processing.ipynb.zip - 978.02 KB
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011 [Python] - Generator Functions & the yield Keyword_en.vtt - 19.48 KB
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035 Sparse Matrix (Part 2) Data Munging with Nested Loops_en.vtt - 19.39 KB
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006 Joint & Conditional Probability_en.vtt - 17.25 KB
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019 Tokenizing, Removing Stop Words and the Python Set Data Structure_en.vtt - 16.58 KB
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013 Cleaning Data (Part 1) Check for Empty Emails & Null Entries_en.vtt - 15.52 KB
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031 Create the Vocabulary for the Spam Classifier_en.vtt - 15.4 KB
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028 Styling the Word Cloud with a Mask_en.vtt - 14.5 KB
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016 Data Visualisation (Part 1) Pie Charts_en.vtt - 14.02 KB
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025 [Python] - Logical Operators to Create Subsets and Indices_en.vtt - 13.4 KB
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007 Bayes Theorem_en.vtt - 13.19 KB
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034 Sparse Matrix (Part 1) Split the Training and Testing Data_en.vtt - 13.17 KB
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030 Styling Word Clouds with Custom Fonts_en.vtt - 12.88 KB
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009 Reading Files (Part 2) Stream Objects and Email Structure_en.vtt - 12.75 KB
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002 Gathering Email Data and Working with Archives & Text Editors_en.vtt - 12.26 KB
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027 Creating your First Word Cloud_en.vtt - 12.01 KB
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038 Checkpoint Understanding the Data_en.vtt - 11.87 KB
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024 Advanced Subsetting on DataFrames the apply() Function_en.vtt - 11.79 KB
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026 Word Clouds & How to install Additional Python Packages_en.vtt - 10.49 KB
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036 Sparse Matrix (Part 3) Using groupby() and Saving .txt Files_en.vtt - 10.46 KB
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008 Reading Files (Part 1) Absolute Paths and Relative Paths_en.vtt - 10.29 KB
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021 Removing HTML tags with BeautifulSoup_en.vtt - 9.73 KB
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020 Word Stemming & Removing Punctuation_en.vtt - 9.39 KB
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001 How to Translate a Business Problem into a Machine Learning Problem_en.vtt - 8.46 KB
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017 Data Visualisation (Part 2) Donut Charts_en.vtt - 8.31 KB
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014 Cleaning Data (Part 2) Working with a DataFrame Index_en.vtt - 7.95 KB
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018 Introduction to Natural Language Processing (NLP)_en.vtt - 7.22 KB
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022 Creating a Function for Text Processing_en.vtt - 7.21 KB
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033 Coding Challenge Find the Longest Email_en.vtt - 6.55 KB
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012 Create a Pandas DataFrame of Email Bodies_en.vtt - 6.24 KB
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015 Saving a JSON File with Pandas_en.vtt - 6.09 KB
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004 The Naive Bayes Algorithm and the Decision Boundary for a Classifier_en.vtt - 5.36 KB
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032 Coding Challenge Check for Membership in a Collection_en.vtt - 5.31 KB
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029 Solving the Hamlet Challenge_en.vtt - 5.21 KB
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010 Extracting the Text in the Email Body_en.vtt - 5.19 KB
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005 Basic Probability_en.vtt - 4.62 KB
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003 How to Add the Lesson Resources to the Project_en.vtt - 4.24 KB
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037 Coding Challenge Solution Preparing the Test Data_en.vtt - 3.93 KB
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040 Any Feedback on this Section.html - 519 B
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023 A Note for the Next Lesson.html - 476 B
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039 Download the Complete Notebook Here.html - 242 B
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external-assets-links.txt - 83 B
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02 - Predict Movie Box Office Revenue with Linear Regression
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003 Explore & Visualise the Data with Python.mp4 - 105.12 MB
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005 Analyse and Evaluate the Results.mp4 - 75.46 MB
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002 Gather & Clean the Data.mp4 - 40.86 MB
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001 Introduction to Linear Regression & Specifying the Problem.mp4 - 26.51 MB
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004 The Intuition behind the Linear Regression Model.mp4 - 12.86 MB
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9246634-cost-revenue-dirty.csv - 374.68 KB
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9249290-cost-revenue-clean.csv - 90.82 KB
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18175146-01-Linear-Regression-complete.ipynb.zip - 75.28 KB
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18175084-01-Linear-Regression-checkpoint.ipynb.zip - 37.64 KB
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003 Explore & Visualise the Data with Python_en.vtt - 26.99 KB
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005 Analyse and Evaluate the Results_en.vtt - 19.58 KB
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002 Gather & Clean the Data_en.vtt - 12.2 KB
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004 The Intuition behind the Linear Regression Model_en.vtt - 9.45 KB
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001 Introduction to Linear Regression & Specifying the Problem_en.vtt - 7.58 KB
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007 Join the Student Community.html - 715 B
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008 Any Feedback on this Section.html - 512 B
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006 Download the Complete Notebook Here.html - 242 B
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external-assets-links.txt - 212 B
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07 - Train a Naive Bayes Classifier to Create a Spam Filter Part 2
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002 Create a Full Matrix.mp4 - 104.85 MB
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003 Count the Tokens to Train the Naive Bayes Model.mp4 - 63.64 MB
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001 Setting up the Notebook and Understanding Delimiters in a Dataset.mp4 - 53.98 MB
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005 Calculate the Token Probabilities and Save the Trained Model.mp4 - 35.29 MB
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006 Coding Challenge Prepare the Test Data.mp4 - 28.62 MB
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004 Sum the Tokens across the Spam and Ham Subsets.mp4 - 24.37 MB
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18190704-SpamData.zip - 22.31 MB
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002 Create a Full Matrix_en.vtt - 19.11 KB
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003 Count the Tokens to Train the Naive Bayes Model_en.vtt - 16.19 KB
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001 Setting up the Notebook and Understanding Delimiters in a Dataset_en.vtt - 9.8 KB
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005 Calculate the Token Probabilities and Save the Trained Model_en.vtt - 8.26 KB
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004 Sum the Tokens across the Spam and Ham Subsets_en.vtt - 6.89 KB
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18180042-07-Bayes-Classifier-Training.ipynb.zip - 5.82 KB
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006 Coding Challenge Prepare the Test Data_en.vtt - 4.57 KB
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008 Any Feedback on this Section.html - 527 B
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007 Download the Complete Notebook Here.html - 242 B
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external-assets-links.txt - 83 B
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01 - Introduction to the Course
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001 What is Machine Learning.mp4 - 40.37 MB
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002 What is Data Science.mp4 - 39.83 MB
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18162714-ML-Data-Science-Syllabus.pdf - 103.97 KB
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001 What is Machine Learning_en.vtt - 6.02 KB
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002 What is Data Science_en.vtt - 5.04 KB
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004 Top Tips for Succeeding on this Course.html - 2.06 KB
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005 Course Resources List.html - 1.12 KB
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003 Download the Syllabus.html - 994 B
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external-assets-links.txt - 120 B
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13 - Next Steps
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001 Where next.html - 3.9 KB
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003 Stay in Touch!.html - 1.05 KB
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002 What Modules Do You Want to See.html - 431 B
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0. Websites you may like
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[FreeCourseSite.com].url - 127 B
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[CourseClub.Me].url - 122 B
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[GigaCourse.Com].url - 49 B
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InfoHash:
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Last updated:
Feb 27, 2023
File count:
434 B
File Category:
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Tags:
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