GetFreeCourses.Co-Udemy-Complete 2022 Data Science & Machine Learning Bootcamp

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GetFreeCourses.Co-Udemy-Complete 2022 Data Science & Machine Learning Bootcamp

磁力链接/BT种子简介

种子哈希：8b707fe6211817dfccd49fb21aa2c5d7939dfb85
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收录时间：2022-02-15
最近下载：2024-11-09

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文件列表

  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/08. [Python] - Advanced Functions and the Pitfalls of Optimisation (Part 1).mp4 305.5 MB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/06. [Python] - Loops and the Gradient Descent Algorithm.mp4 301.4 MB
  • 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/12. Model Evaluation and the Confusion Matrix.mp4 264.1 MB
  • 05. Predict House Prices with Multivariable Linear Regression/32. Build a Valuation Tool (Part 3) Docstrings & Creating your own Python Module.mp4 256.0 MB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/10. Understanding the Learning Rate.mp4 248.1 MB
  • 12. Serving a Tensorflow Model through a Website/12. Introduction to OpenCV.mp4 246.8 MB
  • 03. Python Programming for Data Science and Machine Learning/10. [Python] - Module Imports.mp4 243.3 MB
  • 12. Serving a Tensorflow Model through a Website/14. Calculating the Centre of Mass and Shifting the Image.mp4 234.1 MB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/09. [Python] - Tuples and the Pitfalls of Optimisation (Part 2).mp4 229.6 MB
  • 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/10. Use the Model to Make Predictions.mp4 228.9 MB
  • 05. Predict House Prices with Multivariable Linear Regression/14. Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques.mp4 224.8 MB
  • 11. Use Tensorflow to Classify Handwritten Digits/12. Different Model Architectures Experimenting with Dropout.mp4 224.1 MB
  • 08. Test and Evaluate a Naive Bayes Classifier Part 3/06. Visualising the Decision Boundary.mp4 215.3 MB
  • 08. Test and Evaluate a Naive Bayes Classifier Part 3/11. A Naive Bayes Implementation using SciKit Learn.mp4 204.6 MB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/11. How to Create 3-Dimensional Charts.mp4 202.9 MB
  • 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/09. Use Regularisation to Prevent Overfitting Early Stopping & Dropout Techniques.mp4 200.8 MB
  • 12. Serving a Tensorflow Model through a Website/07. Loading a Tensorflow.js Model and Starting your own Server.mp4 197.2 MB
  • 12. Serving a Tensorflow Model through a Website/09. Styling an HTML Canvas.mp4 196.5 MB
  • 12. Serving a Tensorflow Model through a Website/16. Adding the Game Logic.mp4 181.2 MB
  • 12. Serving a Tensorflow Model through a Website/10. Drawing on an HTML Canvas.mp4 180.3 MB
  • 03. Python Programming for Data Science and Machine Learning/18. How to Make Sense of Python Documentation for Data Visualisation.mp4 179.8 MB
  • 03. Python Programming for Data Science and Machine Learning/19. Working with Python Objects to Analyse Data.mp4 178.2 MB
  • 05. Predict House Prices with Multivariable Linear Regression/11. Visualising Correlations with a Heatmap.mp4 176.8 MB
  • 12. Serving a Tensorflow Model through a Website/13. Resizing and Adding Padding to Images.mp4 165.2 MB
  • 03. Python Programming for Data Science and Machine Learning/17. [Python] - Objects - Understanding Attributes and Methods.mp4 164.4 MB
  • 11. Use Tensorflow to Classify Handwritten Digits/11. Name Scoping and Image Visualisation in Tensorboard.mp4 162.9 MB
  • 03. Python Programming for Data Science and Machine Learning/09. [Python & Pandas] - Dataframes and Series.mp4 160.6 MB
  • 05. Predict House Prices with Multivariable Linear Regression/26. Residual Analysis (Part 2) Graphing and Comparing Regression Residuals.mp4 160.4 MB
  • 05. Predict House Prices with Multivariable Linear Regression/27. Making Predictions (Part 1) MSE & R-Squared.mp4 160.1 MB
  • 11. Use Tensorflow to Classify Handwritten Digits/06. Creating Tensors and Setting up the Neural Network Architecture.mp4 158.2 MB
  • 12. Serving a Tensorflow Model through a Website/06. HTML and CSS Styling.mp4 157.5 MB
  • 05. Predict House Prices with Multivariable Linear Regression/23. Model Simplification & Baysian Information Criterion.mp4 157.4 MB
  • 02. Predict Movie Box Office Revenue with Linear Regression/03. Explore & Visualise the Data with Python.mp4 155.3 MB
  • 09. Introduction to Neural Networks and How to Use Pre-Trained Models/02. Layers, Feature Generation and Learning.mp4 153.8 MB
  • 05. Predict House Prices with Multivariable Linear Regression/22. Understanding VIF & Testing for Multicollinearity.mp4 150.8 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/06. Joint & Conditional Probability.mp4 148.7 MB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/15. Reshaping and Slicing N-Dimensional Arrays.mp4 147.7 MB
  • 05. Predict House Prices with Multivariable Linear Regression/07. Working with Index Data, Pandas Series, and Dummy Variables.mp4 147.6 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/35. Sparse Matrix (Part 2) Data Munging with Nested Loops.mp4 143.9 MB
  • 05. Predict House Prices with Multivariable Linear Regression/04. Clean and Explore the Data (Part 2) Find Missing Values.mp4 141.6 MB
  • 09. Introduction to Neural Networks and How to Use Pre-Trained Models/06. Making Predictions using InceptionResNet.mp4 141.1 MB
  • 05. Predict House Prices with Multivariable Linear Regression/30. [Python] - Conditional Statements - Build a Valuation Tool (Part 2).mp4 140.9 MB
  • 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/07. Interacting with the Operating System and the Python Try-Catch Block.mp4 139.9 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/11. [Python] - Generator Functions & the yield Keyword.mp4 139.6 MB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/12. Understanding Partial Derivatives and How to use SymPy.mp4 139.3 MB
  • 12. Serving a Tensorflow Model through a Website/04. Converting a Model to Tensorflow.js.mp4 138.9 MB
  • 07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/02. Create a Full Matrix.mp4 138.7 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/28. Styling the Word Cloud with a Mask.mp4 137.8 MB
  • 05. Predict House Prices with Multivariable Linear Regression/29. Build a Valuation Tool (Part 1) Working with Pandas Series & Numpy ndarrays.mp4 137.7 MB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/14. [Python] - Loops and Performance Considerations.mp4 137.4 MB
  • 05. Predict House Prices with Multivariable Linear Regression/12. Techniques to Style Scatter Plots.mp4 134.8 MB
  • 11. Use Tensorflow to Classify Handwritten Digits/09. Tensorboard Summaries and the Filewriter.mp4 134.5 MB
  • 03. Python Programming for Data Science and Machine Learning/13. [Python] - Functions - Part 2 Arguments & Parameters.mp4 134.4 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/30. Styling Word Clouds with Custom Fonts.mp4 133.5 MB
  • 05. Predict House Prices with Multivariable Linear Regression/20. Improving the Model by Transforming the Data.mp4 133.0 MB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/21. Plotting the Mean Squared Error (MSE) on a Surface (Part 2).mp4 130.9 MB
  • 05. Predict House Prices with Multivariable Linear Regression/25. Residual Analysis (Part 1) Predicted vs Actual Values.mp4 130.5 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/13. Cleaning Data (Part 1) Check for Empty Emails & Null Entries.mp4 127.9 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/19. Tokenizing, Removing Stop Words and the Python Set Data Structure.mp4 123.5 MB
  • 11. Use Tensorflow to Classify Handwritten Digits/10. Understanding the Tensorflow Graph Nodes and Edges.mp4 121.4 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/02. Gathering Email Data and Working with Archives & Text Editors.mp4 117.5 MB
  • 05. Predict House Prices with Multivariable Linear Regression/10. Calculating Correlations and the Problem posed by Multicollinearity.mp4 116.8 MB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/22. Running Gradient Descent with a MSE Cost Function.mp4 116.6 MB
  • 11. Use Tensorflow to Classify Handwritten Digits/13. Prediction and Model Evaluation.mp4 116.1 MB
  • 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/04. Exploring the CIFAR Data.mp4 115.7 MB
  • 12. Serving a Tensorflow Model through a Website/02. Saving Tensorflow Models.mp4 115.3 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/31. Create the Vocabulary for the Spam Classifier.mp4 112.2 MB
  • 02. Predict Movie Box Office Revenue with Linear Regression/05. Analyse and Evaluate the Results.mp4 110.3 MB
  • 12. Serving a Tensorflow Model through a Website/15. Making a Prediction from a Digit drawn on the HTML Canvas.mp4 109.5 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/09. Reading Files (Part 2) Stream Objects and Email Structure.mp4 109.4 MB
  • 12. Serving a Tensorflow Model through a Website/03. Loading a SavedModel.mp4 109.0 MB
  • 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/06. Compiling a Keras Model and Understanding the Cross Entropy Loss Function.mp4 108.6 MB
  • 09. Introduction to Neural Networks and How to Use Pre-Trained Models/07. Coding Challenge Solution Using other Keras Models.mp4 108.6 MB
  • 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/08. Fit a Keras Model and Use Tensorboard to Visualise Learning and Spot Problems.mp4 105.3 MB
  • 11. Use Tensorflow to Classify Handwritten Digits/08. TensorFlow Sessions and Batching Data.mp4 105.2 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/27. Creating your First Word Cloud.mp4 103.2 MB
  • 02. Predict Movie Box Office Revenue with Linear Regression/02. Gather & Clean the Data.mp4 101.7 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/38. Checkpoint Understanding the Data.mp4 101.1 MB
  • 07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/03. Count the Tokens to Train the Naive Bayes Model.mp4 100.8 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/21. Removing HTML tags with BeautifulSoup.mp4 100.5 MB
  • 09. Introduction to Neural Networks and How to Use Pre-Trained Models/04. Preprocessing Image Data and How RGB Works.mp4 98.1 MB
  • 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/05. Pre-processing Scaling Inputs and Creating a Validation Dataset.mp4 97.7 MB
  • 09. Introduction to Neural Networks and How to Use Pre-Trained Models/03. Costs and Disadvantages of Neural Networks.mp4 96.5 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/16. Data Visualisation (Part 1) Pie Charts.mp4 95.1 MB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/04. LaTeX Markdown and Generating Data with Numpy.mp4 94.9 MB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/05. Understanding the Power Rule & Creating Charts with Subplots.mp4 94.5 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/34. Sparse Matrix (Part 1) Split the Training and Testing Data.mp4 91.9 MB
  • 05. Predict House Prices with Multivariable Linear Regression/03. Clean and Explore the Data (Part 1) Understand the Nature of the Dataset.mp4 91.4 MB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/18. Transposing and Reshaping Arrays.mp4 91.1 MB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/13. Implementing Batch Gradient Descent with SymPy.mp4 91.0 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/25. [Python] - Logical Operators to Create Subsets and Indices.mp4 90.6 MB
  • 05. Predict House Prices with Multivariable Linear Regression/28. Making Predictions (Part 2) Standard Deviation, RMSE, and Prediction Intervals.mp4 89.0 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/07. Bayes Theorem.mp4 87.7 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/24. Advanced Subsetting on DataFrames the apply() Function.mp4 87.4 MB
  • 03. Python Programming for Data Science and Machine Learning/15. [Python] - Functions - Part 3 Results & Return Values.mp4 86.6 MB
  • 03. Python Programming for Data Science and Machine Learning/20. [Python] - Tips, Code Style and Naming Conventions.mp4 85.5 MB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/19. Implementing a MSE Cost Function.mp4 85.1 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/36. Sparse Matrix (Part 3) Using groupby() and Saving .txt Files.mp4 84.4 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/26. Word Clouds & How to install Additional Python Packages.mp4 83.3 MB
  • 12. Serving a Tensorflow Model through a Website/05. Introducing the Website Project and Tooling.mp4 81.8 MB
  • 11. Use Tensorflow to Classify Handwritten Digits/07. Defining the Cross Entropy Loss Function, the Optimizer and the Metrics.mp4 78.8 MB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/23. Visualising the Optimisation on a 3D Surface.mp4 78.4 MB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/20. Understanding Nested Loops and Plotting the MSE Function (Part 1).mp4 76.7 MB
  • 07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/01. Setting up the Notebook and Understanding Delimiters in a Dataset.mp4 76.0 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/20. Word Stemming & Removing Punctuation.mp4 74.9 MB
  • 03. Python Programming for Data Science and Machine Learning/05. [Python] - Variables and Types.mp4 74.8 MB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/16. Concatenating Numpy Arrays.mp4 74.8 MB
  • 11. Use Tensorflow to Classify Handwritten Digits/04. Data Preprocessing One-Hot Encoding and Creating the Validation Dataset.mp4 73.6 MB
  • 08. Test and Evaluate a Naive Bayes Classifier Part 3/02. Joint Conditional Probability (Part 1) Dot Product.mp4 69.6 MB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/03. Introduction to Cost Functions.mp4 69.4 MB
  • 09. Introduction to Neural Networks and How to Use Pre-Trained Models/05. Importing Keras Models and the Tensorflow Graph.mp4 68.6 MB
  • 05. Predict House Prices with Multivariable Linear Regression/21. How to Interpret Coefficients using p-Values and Statistical Significance.mp4 68.6 MB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/17. Introduction to the Mean Squared Error (MSE).mp4 67.7 MB
  • 05. Predict House Prices with Multivariable Linear Regression/05. Visualising Data (Part 1) Historams, Distributions & Outliers.mp4 67.7 MB
  • 05. Predict House Prices with Multivariable Linear Regression/16. How to Shuffle and Split Training & Testing Data.mp4 67.5 MB
  • 05. Predict House Prices with Multivariable Linear Regression/24. How to Analyse and Plot Regression Residuals.mp4 67.3 MB
  • 08. Test and Evaluate a Naive Bayes Classifier Part 3/03. Joint Conditional Probablity (Part 2) Priors.mp4 67.1 MB
  • 08. Test and Evaluate a Naive Bayes Classifier Part 3/07. False Positive vs False Negatives.mp4 66.3 MB
  • 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/11. Model Evaluation and the Confusion Matrix.mp4 65.8 MB
  • 05. Predict House Prices with Multivariable Linear Regression/08. Understanding Descriptive Statistics the Mean vs the Median.mp4 65.2 MB
  • 12. Serving a Tensorflow Model through a Website/11. Data Pre-Processing for Tensorflow.js.mp4 64.9 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/14. Cleaning Data (Part 2) Working with a DataFrame Index.mp4 64.8 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/17. Data Visualisation (Part 2) Donut Charts.mp4 64.8 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/08. Reading Files (Part 1) Absolute Paths and Relative Paths.mp4 63.9 MB
  • 05. Predict House Prices with Multivariable Linear Regression/06. Visualising Data (Part 2) Seaborn and Probability Density Functions.mp4 60.1 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/29. Solving the Hamlet Challenge.mp4 59.9 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/15. Saving a JSON File with Pandas.mp4 59.1 MB
  • 05. Predict House Prices with Multivariable Linear Regression/02. Gathering the Boston House Price Data.mp4 59.0 MB
  • 05. Predict House Prices with Multivariable Linear Regression/17. Running a Multivariable Regression.mp4 58.3 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/33. Coding Challenge Find the Longest Email.mp4 57.1 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/22. Creating a Function for Text Processing.mp4 56.5 MB
  • 03. Python Programming for Data Science and Machine Learning/07. [Python] - Lists and Arrays.mp4 56.1 MB
  • 07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/05. Calculate the Token Probabilities and Save the Trained Model.mp4 56.1 MB
  • 08. Test and Evaluate a Naive Bayes Classifier Part 3/09. The Precision Metric.mp4 55.9 MB
  • 11. Use Tensorflow to Classify Handwritten Digits/02. Getting the Data and Loading it into Numpy Arrays.mp4 55.4 MB
  • 03. Python Programming for Data Science and Machine Learning/02. Mac Users - Install Anaconda.mp4 55.0 MB
  • 08. Test and Evaluate a Naive Bayes Classifier Part 3/04. Making Predictions Comparing Joint Probabilities.mp4 54.9 MB
  • 09. Introduction to Neural Networks and How to Use Pre-Trained Models/01. The Human Brain and the Inspiration for Artificial Neural Networks.mp4 54.3 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/18. Introduction to Natural Language Processing (NLP).mp4 53.3 MB
  • 03. Python Programming for Data Science and Machine Learning/01. Windows Users - Install Anaconda.mp4 52.0 MB
  • 05. Predict House Prices with Multivariable Linear Regression/15. Understanding Multivariable Regression.mp4 51.2 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/12. Create a Pandas DataFrame of Email Bodies.mp4 51.0 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/10. Extracting the Text in the Email Body.mp4 49.7 MB
  • 07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/04. Sum the Tokens across the Spam and Ham Subsets.mp4 49.0 MB
  • 11. Use Tensorflow to Classify Handwritten Digits/05. What is a Tensor.mp4 47.6 MB
  • 01. Introduction to the Course/01. What is Machine Learning.mp4 47.5 MB
  • 01. Introduction to the Course/02. What is Data Science.mp4 44.9 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/01. How to Translate a Business Problem into a Machine Learning Problem.mp4 44.3 MB
  • 03. Python Programming for Data Science and Machine Learning/03. Does LSD Make You Better at Maths.mp4 44.3 MB
  • 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/02. Installing Tensorflow and Keras for Jupyter.mp4 44.1 MB
  • 03. Python Programming for Data Science and Machine Learning/11. [Python] - Functions - Part 1 Defining and Calling Functions.mp4 43.6 MB
  • 12. Serving a Tensorflow Model through a Website/08. Adding a Favicon.mp4 43.5 MB
  • 08. Test and Evaluate a Naive Bayes Classifier Part 3/05. The Accuracy Metric.mp4 42.5 MB
  • 05. Predict House Prices with Multivariable Linear Regression/01. Defining the Problem.mp4 41.8 MB
  • 12. Serving a Tensorflow Model through a Website/17. Publish and Share your Website!.mp4 40.6 MB
  • 12. Serving a Tensorflow Model through a Website/01. What you'll make.mp4 40.3 MB
  • 07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/06. Coding Challenge Prepare the Test Data.mp4 37.3 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/04. The Naive Bayes Algorithm and the Decision Boundary for a Classifier.mp4 35.0 MB
  • 05. Predict House Prices with Multivariable Linear Regression/09. Introduction to Correlation Understanding Strength & Direction.mp4 34.7 MB
  • 11. Use Tensorflow to Classify Handwritten Digits/03. Data Exploration and Understanding the Structure of the Input Data.mp4 34.0 MB
  • 05. Predict House Prices with Multivariable Linear Regression/18. How to Calculate the Model Fit with R-Squared.mp4 34.0 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/32. Coding Challenge Check for Membership in a Collection.mp4 33.9 MB
  • 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/03. Gathering the CIFAR 10 Dataset.mp4 32.9 MB
  • 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/01. Solving a Business Problem with Image Classification.mp4 32.0 MB
  • 02. Predict Movie Box Office Revenue with Linear Regression/01. Introduction to Linear Regression & Specifying the Problem.mp4 31.8 MB
  • 02. Predict Movie Box Office Revenue with Linear Regression/04. The Intuition behind the Linear Regression Model.mp4 31.1 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/37. Coding Challenge Solution Preparing the Test Data.mp4 30.3 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/03. How to Add the Lesson Resources to the Project.mp4 30.3 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/05. Basic Probability.mp4 29.9 MB
  • 08. Test and Evaluate a Naive Bayes Classifier Part 3/08. The Recall Metric.mp4 29.5 MB
  • 08. Test and Evaluate a Naive Bayes Classifier Part 3/01. Set up the Testing Notebook.mp4 27.7 MB
  • 08. Test and Evaluate a Naive Bayes Classifier Part 3/10. The F-score or F1 Metric.mp4 25.9 MB
  • 08. Test and Evaluate a Naive Bayes Classifier Part 3/01.1 SpamData.zip 23.9 MB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/02. How a Machine Learns.mp4 23.9 MB
  • 07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/01.2 SpamData.zip 23.4 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/02.1 SpamData.zip 22.3 MB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/01. What's Coming Up.mp4 21.9 MB
  • 05. Predict House Prices with Multivariable Linear Regression/19. Introduction to Model Evaluation.mp4 16.8 MB
  • 11. Use Tensorflow to Classify Handwritten Digits/02.1 MNIST.zip 15.5 MB
  • 11. Use Tensorflow to Classify Handwritten Digits/01. What's coming up.mp4 7.4 MB
  • 12. Serving a Tensorflow Model through a Website/16.1 math_garden_stub complete.zip 4.3 MB
  • 12. Serving a Tensorflow Model through a Website/12.1 math_garden_stub 12.12 checkpoint.zip 4.3 MB
  • 05. Predict House Prices with Multivariable Linear Regression/33.1 04 Multivariable Regression.ipynb.zip 3.7 MB
  • 12. Serving a Tensorflow Model through a Website/03.1 MNIST_Model_Load_Files.zip 3.0 MB
  • 03. Python Programming for Data Science and Machine Learning/04.1 12 Rules to Learn to Code.pdf 2.4 MB
  • 12. Serving a Tensorflow Model through a Website/04.1 TFJS.zip 1.6 MB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/24.1 03 Gradient Descent.ipynb.zip 1.2 MB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/39.1 06 Bayes Classifier - Pre-Processing.ipynb.zip 1.0 MB
  • 09. Introduction to Neural Networks and How to Use Pre-Trained Models/08.1 09 Neural Nets Pretrained Image Classification.ipynb.zip 585.6 kB
  • 09. Introduction to Neural Networks and How to Use Pre-Trained Models/04.1 TF_Keras_Classification_Images.zip 513.1 kB
  • 02. Predict Movie Box Office Revenue with Linear Regression/02.2 cost_revenue_dirty.csv 383.7 kB
  • 08. Test and Evaluate a Naive Bayes Classifier Part 3/12.2 07 Bayes Classifier - Testing, Inference & Evaluation.ipynb.zip 248.9 kB
  • 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/13.1 10 Neural Nets - Keras CIFAR10 Classification.ipynb.zip 123.0 kB
  • 01. Introduction to the Course/03.1 ML Data Science Syllabus.pdf 106.5 kB
  • 02. Predict Movie Box Office Revenue with Linear Regression/03.2 cost_revenue_clean.csv 93.0 kB
  • 02. Predict Movie Box Office Revenue with Linear Regression/06.1 01 Linear Regression (complete).ipynb.zip 77.1 kB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/06. [Python] - Loops and the Gradient Descent Algorithm.srt 45.1 kB
  • 12. Serving a Tensorflow Model through a Website/05.1 math_garden_stub.zip 45.1 kB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/08. [Python] - Advanced Functions and the Pitfalls of Optimisation (Part 1).srt 44.0 kB
  • 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/12. Model Evaluation and the Confusion Matrix.srt 41.5 kB
  • 12. Serving a Tensorflow Model through a Website/09. Styling an HTML Canvas.srt 40.4 kB
  • 12. Serving a Tensorflow Model through a Website/12. Introduction to OpenCV.srt 39.3 kB
  • 12. Serving a Tensorflow Model through a Website/16. Adding the Game Logic.srt 39.0 kB
  • 12. Serving a Tensorflow Model through a Website/06. HTML and CSS Styling.srt 38.8 kB
  • 12. Serving a Tensorflow Model through a Website/10. Drawing on an HTML Canvas.srt 38.7 kB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/10. Understanding the Learning Rate.srt 38.6 kB
  • 02. Predict Movie Box Office Revenue with Linear Regression/04.1 01 Linear Regression (checkpoint).ipynb.zip 38.5 kB
  • 12. Serving a Tensorflow Model through a Website/07. Loading a Tensorflow.js Model and Starting your own Server.srt 38.1 kB
  • 03. Python Programming for Data Science and Machine Learning/21.1 02 Python Intro.ipynb.zip 37.3 kB
  • 03. Python Programming for Data Science and Machine Learning/10. [Python] - Module Imports.srt 37.0 kB
  • 12. Serving a Tensorflow Model through a Website/14. Calculating the Centre of Mass and Shifting the Image.srt 36.3 kB
  • 08. Test and Evaluate a Naive Bayes Classifier Part 3/11. A Naive Bayes Implementation using SciKit Learn.srt 34.5 kB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/09. [Python] - Tuples and the Pitfalls of Optimisation (Part 2).srt 34.3 kB
  • 08. Test and Evaluate a Naive Bayes Classifier Part 3/06. Visualising the Decision Boundary.srt 34.2 kB
  • 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/10. Use the Model to Make Predictions.srt 33.8 kB
  • 02. Predict Movie Box Office Revenue with Linear Regression/03. Explore & Visualise the Data with Python.srt 31.8 kB
  • 11. Use Tensorflow to Classify Handwritten Digits/12. Different Model Architectures Experimenting with Dropout.srt 30.8 kB
  • 03. Python Programming for Data Science and Machine Learning/17. [Python] - Objects - Understanding Attributes and Methods.srt 30.6 kB
  • 11. Use Tensorflow to Classify Handwritten Digits/06. Creating Tensors and Setting up the Neural Network Architecture.srt 29.7 kB
  • 05. Predict House Prices with Multivariable Linear Regression/14. Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques.srt 29.4 kB
  • 05. Predict House Prices with Multivariable Linear Regression/32. Build a Valuation Tool (Part 3) Docstrings & Creating your own Python Module.srt 29.1 kB
  • 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/09. Use Regularisation to Prevent Overfitting Early Stopping & Dropout Techniques.srt 29.0 kB
  • 03. Python Programming for Data Science and Machine Learning/09. [Python & Pandas] - Dataframes and Series.srt 28.8 kB
  • 09. Introduction to Neural Networks and How to Use Pre-Trained Models/02. Layers, Feature Generation and Learning.srt 28.5 kB
  • 03. Python Programming for Data Science and Machine Learning/19. Working with Python Objects to Analyse Data.srt 27.9 kB
  • 12. Serving a Tensorflow Model through a Website/13. Resizing and Adding Padding to Images.srt 27.5 kB
  • 03. Python Programming for Data Science and Machine Learning/18. How to Make Sense of Python Documentation for Data Visualisation.srt 27.1 kB
  • 11. Use Tensorflow to Classify Handwritten Digits/11. Name Scoping and Image Visualisation in Tensorboard.srt 26.9 kB
  • 12. Serving a Tensorflow Model through a Website/03. Loading a SavedModel.srt 26.8 kB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/11. How to Create 3-Dimensional Charts.srt 26.7 kB
  • 05. Predict House Prices with Multivariable Linear Regression/22. Understanding VIF & Testing for Multicollinearity.srt 26.2 kB
  • 05. Predict House Prices with Multivariable Linear Regression/11. Visualising Correlations with a Heatmap.srt 25.0 kB
  • 05. Predict House Prices with Multivariable Linear Regression/27. Making Predictions (Part 1) MSE & R-Squared.srt 24.3 kB
  • 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/07. Interacting with the Operating System and the Python Try-Catch Block.srt 24.3 kB
  • 11. Use Tensorflow to Classify Handwritten Digits/09. Tensorboard Summaries and the Filewriter.srt 23.8 kB
  • 05. Predict House Prices with Multivariable Linear Regression/23. Model Simplification & Baysian Information Criterion.srt 23.7 kB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/15. Reshaping and Slicing N-Dimensional Arrays.srt 23.5 kB
  • 05. Predict House Prices with Multivariable Linear Regression/26. Residual Analysis (Part 2) Graphing and Comparing Regression Residuals.srt 23.3 kB
  • 02. Predict Movie Box Office Revenue with Linear Regression/05. Analyse and Evaluate the Results.srt 22.9 kB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/35. Sparse Matrix (Part 2) Data Munging with Nested Loops.srt 22.9 kB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/22. Running Gradient Descent with a MSE Cost Function.srt 22.9 kB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/11. [Python] - Generator Functions & the yield Keyword.srt 22.9 kB
  • 07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/02. Create a Full Matrix.srt 22.2 kB
  • 05. Predict House Prices with Multivariable Linear Regression/20. Improving the Model by Transforming the Data.srt 22.1 kB
  • 05. Predict House Prices with Multivariable Linear Regression/30. [Python] - Conditional Statements - Build a Valuation Tool (Part 2).srt 21.9 kB
  • 12. Serving a Tensorflow Model through a Website/02. Saving Tensorflow Models.srt 21.8 kB
  • 11. Use Tensorflow to Classify Handwritten Digits/10. Understanding the Tensorflow Graph Nodes and Edges.srt 21.8 kB
  • 12. Serving a Tensorflow Model through a Website/04. Converting a Model to Tensorflow.js.srt 21.6 kB
  • 05. Predict House Prices with Multivariable Linear Regression/29. Build a Valuation Tool (Part 1) Working with Pandas Series & Numpy ndarrays.srt 21.3 kB
  • 03. Python Programming for Data Science and Machine Learning/13. [Python] - Functions - Part 2 Arguments & Parameters.srt 21.3 kB
  • 05. Predict House Prices with Multivariable Linear Regression/07. Working with Index Data, Pandas Series, and Dummy Variables.srt 21.2 kB
  • 05. Predict House Prices with Multivariable Linear Regression/12. Techniques to Style Scatter Plots.srt 21.1 kB
  • 11. Use Tensorflow to Classify Handwritten Digits/08. TensorFlow Sessions and Batching Data.srt 21.0 kB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/12. Understanding Partial Derivatives and How to use SymPy.srt 20.7 kB
  • 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/05. Pre-processing Scaling Inputs and Creating a Validation Dataset.srt 20.4 kB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/06. Joint & Conditional Probability.srt 20.3 kB
  • 09. Introduction to Neural Networks and How to Use Pre-Trained Models/03. Costs and Disadvantages of Neural Networks.srt 19.7 kB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/19. Tokenizing, Removing Stop Words and the Python Set Data Structure.srt 19.5 kB
  • 09. Introduction to Neural Networks and How to Use Pre-Trained Models/06. Making Predictions using InceptionResNet.srt 19.4 kB
  • 11. Use Tensorflow to Classify Handwritten Digits/13. Prediction and Model Evaluation.srt 19.4 kB
  • 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/06. Compiling a Keras Model and Understanding the Cross Entropy Loss Function.srt 19.1 kB
  • 05. Predict House Prices with Multivariable Linear Regression/04. Clean and Explore the Data (Part 2) Find Missing Values.srt 19.0 kB
  • 07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/03. Count the Tokens to Train the Naive Bayes Model.srt 18.8 kB
  • 05. Predict House Prices with Multivariable Linear Regression/25. Residual Analysis (Part 1) Predicted vs Actual Values.srt 18.7 kB
  • 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/04. Exploring the CIFAR Data.srt 18.7 kB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/05. Understanding the Power Rule & Creating Charts with Subplots.srt 18.5 kB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/14. [Python] - Loops and Performance Considerations.srt 18.5 kB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/13. Cleaning Data (Part 1) Check for Empty Emails & Null Entries.srt 18.4 kB
  • 05. Predict House Prices with Multivariable Linear Regression/10. Calculating Correlations and the Problem posed by Multicollinearity.srt 18.3 kB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/31. Create the Vocabulary for the Spam Classifier.srt 18.2 kB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/21. Plotting the Mean Squared Error (MSE) on a Surface (Part 2).srt 17.9 kB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/04. LaTeX Markdown and Generating Data with Numpy.srt 17.7 kB
  • 12. Serving a Tensorflow Model through a Website/05. Introducing the Website Project and Tooling.srt 17.6 kB
  • 12. Serving a Tensorflow Model through a Website/15. Making a Prediction from a Digit drawn on the HTML Canvas.srt 17.5 kB
  • 03. Python Programming for Data Science and Machine Learning/20. [Python] - Tips, Code Style and Naming Conventions.srt 17.1 kB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/28. Styling the Word Cloud with a Mask.srt 17.1 kB
  • 03. Python Programming for Data Science and Machine Learning/05. [Python] - Variables and Types.srt 16.9 kB
  • 03. Python Programming for Data Science and Machine Learning/15. [Python] - Functions - Part 3 Results & Return Values.srt 16.9 kB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/16. Data Visualisation (Part 1) Pie Charts.srt 16.6 kB
  • 09. Introduction to Neural Networks and How to Use Pre-Trained Models/04. Preprocessing Image Data and How RGB Works.srt 16.5 kB
  • 05. Predict House Prices with Multivariable Linear Regression/03. Clean and Explore the Data (Part 1) Understand the Nature of the Dataset.srt 16.0 kB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/25. [Python] - Logical Operators to Create Subsets and Indices.srt 15.9 kB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/34. Sparse Matrix (Part 1) Split the Training and Testing Data.srt 15.6 kB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/07. Bayes Theorem.srt 15.5 kB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/30. Styling Word Clouds with Custom Fonts.srt 15.1 kB
  • 05. Predict House Prices with Multivariable Linear Regression/24. How to Analyse and Plot Regression Residuals.srt 15.1 kB
  • 05. Predict House Prices with Multivariable Linear Regression/28. Making Predictions (Part 2) Standard Deviation, RMSE, and Prediction Intervals.srt 15.1 kB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/09. Reading Files (Part 2) Stream Objects and Email Structure.srt 14.9 kB
  • 05. Predict House Prices with Multivariable Linear Regression/05. Visualising Data (Part 1) Historams, Distributions & Outliers.srt 14.6 kB
  • 11. Use Tensorflow to Classify Handwritten Digits/07. Defining the Cross Entropy Loss Function, the Optimizer and the Metrics.srt 14.5 kB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/02. Gathering Email Data and Working with Archives & Text Editors.srt 14.5 kB
  • 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/08. Fit a Keras Model and Use Tensorboard to Visualise Learning and Spot Problems.srt 14.4 kB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/20. Understanding Nested Loops and Plotting the MSE Function (Part 1).srt 14.3 kB
  • 02. Predict Movie Box Office Revenue with Linear Regression/02. Gather & Clean the Data.srt 14.3 kB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/27. Creating your First Word Cloud.srt 14.0 kB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/38. Checkpoint Understanding the Data.srt 14.0 kB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/19. Implementing a MSE Cost Function.srt 13.9 kB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/24. Advanced Subsetting on DataFrames the apply() Function.srt 13.9 kB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/18. Transposing and Reshaping Arrays.srt 13.8 kB
  • 08. Test and Evaluate a Naive Bayes Classifier Part 3/12.1 08 Naive Bayes with scikit-learn.ipynb.zip 13.6 kB
  • 09. Introduction to Neural Networks and How to Use Pre-Trained Models/07. Coding Challenge Solution Using other Keras Models.srt 13.2 kB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/13. Implementing Batch Gradient Descent with SymPy.srt 13.2 kB
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  • 08. Test and Evaluate a Naive Bayes Classifier Part 3/02. Joint Conditional Probability (Part 1) Dot Product.srt 13.0 kB
  • 11. Use Tensorflow to Classify Handwritten Digits/04. Data Preprocessing One-Hot Encoding and Creating the Validation Dataset.srt 13.0 kB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/17. Introduction to the Mean Squared Error (MSE).srt 12.9 kB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/36. Sparse Matrix (Part 3) Using groupby() and Saving .txt Files.srt 12.5 kB
  • 03. Python Programming for Data Science and Machine Learning/07. [Python] - Lists and Arrays.srt 12.4 kB
  • 05. Predict House Prices with Multivariable Linear Regression/08. Understanding Descriptive Statistics the Mean vs the Median.srt 12.4 kB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/26. Word Clouds & How to install Additional Python Packages.srt 12.3 kB
  • 12. Serving a Tensorflow Model through a Website/11. Data Pre-Processing for Tensorflow.js.srt 12.2 kB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/08. Reading Files (Part 1) Absolute Paths and Relative Paths.srt 12.0 kB
  • 05. Predict House Prices with Multivariable Linear Regression/16. How to Shuffle and Split Training & Testing Data.srt 11.8 kB
  • 09. Introduction to Neural Networks and How to Use Pre-Trained Models/05. Importing Keras Models and the Tensorflow Graph.srt 11.7 kB
  • 07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/01. Setting up the Notebook and Understanding Delimiters in a Dataset.srt 11.4 kB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/21. Removing HTML tags with BeautifulSoup.srt 11.3 kB
  • 09. Introduction to Neural Networks and How to Use Pre-Trained Models/01. The Human Brain and the Inspiration for Artificial Neural Networks.srt 11.1 kB
  • 02. Predict Movie Box Office Revenue with Linear Regression/04. The Intuition behind the Linear Regression Model.srt 11.1 kB
  • 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/11. Model Evaluation and the Confusion Matrix.srt 11.1 kB
  • 05. Predict House Prices with Multivariable Linear Regression/21. How to Interpret Coefficients using p-Values and Statistical Significance.srt 11.0 kB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/23. Visualising the Optimisation on a 3D Surface.srt 11.0 kB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/20. Word Stemming & Removing Punctuation.srt 10.8 kB
  • 08. Test and Evaluate a Naive Bayes Classifier Part 3/03. Joint Conditional Probablity (Part 2) Priors.srt 10.8 kB
  • 03. Python Programming for Data Science and Machine Learning/11. [Python] - Functions - Part 1 Defining and Calling Functions.srt 10.7 kB
  • 05. Predict House Prices with Multivariable Linear Regression/17. Running a Multivariable Regression.srt 10.0 kB
  • 12. Serving a Tensorflow Model through a Website/01. What you'll make.srt 10.0 kB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/01. How to Translate a Business Problem into a Machine Learning Problem.srt 9.9 kB
  • 08. Test and Evaluate a Naive Bayes Classifier Part 3/04. Making Predictions Comparing Joint Probabilities.srt 9.9 kB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/17. Data Visualisation (Part 2) Donut Charts.srt 9.8 kB
  • 12. Serving a Tensorflow Model through a Website/17. Publish and Share your Website!.srt 9.7 kB
  • 08. Test and Evaluate a Naive Bayes Classifier Part 3/09. The Precision Metric.srt 9.7 kB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/03. Introduction to Cost Functions.srt 9.7 kB
  • 07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/05. Calculate the Token Probabilities and Save the Trained Model.srt 9.7 kB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/14. Cleaning Data (Part 2) Working with a DataFrame Index.srt 9.5 kB
  • 11. Use Tensorflow to Classify Handwritten Digits/02. Getting the Data and Loading it into Numpy Arrays.srt 9.2 kB
  • 11. Use Tensorflow to Classify Handwritten Digits/05. What is a Tensor.srt 9.2 kB
  • 05. Predict House Prices with Multivariable Linear Regression/06. Visualising Data (Part 2) Seaborn and Probability Density Functions.srt 9.2 kB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/16. Concatenating Numpy Arrays.srt 9.1 kB
  • 03. Python Programming for Data Science and Machine Learning/01. Windows Users - Install Anaconda.srt 9.0 kB
  • 02. Predict Movie Box Office Revenue with Linear Regression/01. Introduction to Linear Regression & Specifying the Problem.srt 9.0 kB
  • 05. Predict House Prices with Multivariable Linear Regression/02. Gathering the Boston House Price Data.srt 8.9 kB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/22. Creating a Function for Text Processing.srt 8.6 kB
  • 05. Predict House Prices with Multivariable Linear Regression/09. Introduction to Correlation Understanding Strength & Direction.srt 8.6 kB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/18. Introduction to Natural Language Processing (NLP).srt 8.4 kB
  • 03. Python Programming for Data Science and Machine Learning/02. Mac Users - Install Anaconda.srt 8.2 kB
  • 07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/04. Sum the Tokens across the Spam and Ham Subsets.srt 7.9 kB
  • 08. Test and Evaluate a Naive Bayes Classifier Part 3/05. The Accuracy Metric.srt 7.8 kB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/33. Coding Challenge Find the Longest Email.srt 7.7 kB
  • 05. Predict House Prices with Multivariable Linear Regression/15. Understanding Multivariable Regression.srt 7.7 kB
  • 12. Serving a Tensorflow Model through a Website/08. Adding a Favicon.srt 7.6 kB
  • 03. Python Programming for Data Science and Machine Learning/03. Does LSD Make You Better at Maths.srt 7.5 kB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/12. Create a Pandas DataFrame of Email Bodies.srt 7.4 kB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/02. How a Machine Learns.srt 7.4 kB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/15. Saving a JSON File with Pandas.srt 7.1 kB
  • 01. Introduction to the Course/01. What is Machine Learning.srt 7.1 kB
  • 11. Use Tensorflow to Classify Handwritten Digits/14.1 11 Neural Networks - TF Handwriting Recognition.ipynb.zip 6.8 kB
  • 08. Test and Evaluate a Naive Bayes Classifier Part 3/08. The Recall Metric.srt 6.7 kB
  • 11. Use Tensorflow to Classify Handwritten Digits/03. Data Exploration and Understanding the Structure of the Input Data.srt 6.6 kB
  • 05. Predict House Prices with Multivariable Linear Regression/01. Defining the Problem.srt 6.6 kB
  • 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/02. Installing Tensorflow and Keras for Jupyter.srt 6.6 kB
  • 12. Serving a Tensorflow Model through a Website/02.1 11 Neural Networks - TF Handwriting Recognition.ipynb.zip 6.5 kB
  • 12. Serving a Tensorflow Model through a Website/03.2 12 TF SavedModel Export Completed.ipynb.zip 6.3 kB
  • 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/03. Gathering the CIFAR 10 Dataset.srt 6.2 kB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/32. Coding Challenge Check for Membership in a Collection.srt 6.2 kB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/04. The Naive Bayes Algorithm and the Decision Boundary for a Classifier.srt 6.2 kB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/10. Extracting the Text in the Email Body.srt 6.1 kB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/29. Solving the Hamlet Challenge.srt 6.1 kB
  • 07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/07.1 07 Bayes Classifier - Training.ipynb.zip 6.0 kB
  • 01. Introduction to the Course/02. What is Data Science.srt 5.9 kB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/05. Basic Probability.srt 5.4 kB
  • 07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/06. Coding Challenge Prepare the Test Data.srt 5.3 kB
  • 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/01. Solving a Business Problem with Image Classification.srt 5.1 kB
  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/03. How to Add the Lesson Resources to the Project.srt 5.1 kB
  • 12. Serving a Tensorflow Model through a Website/07.1 x_test2_ylabel1.txt 4.7 kB
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  • 06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/37. Coding Challenge Solution Preparing the Test Data.srt 4.6 kB
  • 08. Test and Evaluate a Naive Bayes Classifier Part 3/10. The F-score or F1 Metric.srt 4.6 kB
  • 05. Predict House Prices with Multivariable Linear Regression/18. How to Calculate the Model Fit with R-Squared.srt 4.5 kB
  • 13. Next Steps/01. Where next.html 4.0 kB
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/01. What's Coming Up.srt 3.9 kB
  • 08. Test and Evaluate a Naive Bayes Classifier Part 3/01. Set up the Testing Notebook.srt 3.9 kB
  • 05. Predict House Prices with Multivariable Linear Regression/19. Introduction to Model Evaluation.srt 3.9 kB
  • 05. Predict House Prices with Multivariable Linear Regression/33.3 boston_valuation.py 3.1 kB
  • 05. Predict House Prices with Multivariable Linear Regression/33.2 04 Valuation Tool.ipynb.zip 3.0 kB
  • 11. Use Tensorflow to Classify Handwritten Digits/01. What's coming up.srt 2.5 kB
  • 01. Introduction to the Course/04. Top Tips for Succeeding on this Course.html 2.1 kB
  • 03. Python Programming for Data Science and Machine Learning/04. Download the 12 Rules to Learn to Code.html 1.2 kB
  • 01. Introduction to the Course/05. Course Resources List.html 1.2 kB
  • 13. Next Steps/03. Stay in Touch!.html 1.1 kB
  • 01. Introduction to the Course/03. Download the Syllabus.html 1.1 kB
  • 02. Predict Movie Box Office Revenue with Linear Regression/07. Join the Student Community.html 730 Bytes
  • 07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/08. Any Feedback on this Section.html 527 Bytes
  • 09. Introduction to Neural Networks and How to Use Pre-Trained Models/09. Any Feedback on this Section.html 526 Bytes
  • 10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/14. Any Feedback on this Section.html 521 Bytes
  • 04. Introduction to Optimisation and the Gradient Descent Algorithm/25. Any Feedback on this Section.html 520 Bytes
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