[] Udemy - Complete 2022 Data Science & Machine Learning Bootcamp

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04. Introduction to Optimisation and the Gradient Descent Algorithm/08. [Python] - Advanced Functions and the Pitfalls of Optimisation (Part 1).mp4 291MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/06. [Python] - Loops and the Gradient Descent Algorithm.mp4 287MB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/12. Model Evaluation and the Confusion Matrix.mp4 252MB
05. Predict House Prices with Multivariable Linear Regression/32. Build a Valuation Tool (Part 3) Docstrings & Creating your own Python Module.mp4 244MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/10. Understanding the Learning Rate.mp4 237MB
12. Serving a Tensorflow Model through a Website/12. Introduction to OpenCV.mp4 235MB
03. Python Programming for Data Science and Machine Learning/10. [Python] - Module Imports.mp4 232MB
12. Serving a Tensorflow Model through a Website/14. Calculating the Centre of Mass and Shifting the Image.mp4 223MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/09. [Python] - Tuples and the Pitfalls of Optimisation (Part 2).mp4 219MB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/10. Use the Model to Make Predictions.mp4 218MB
05. Predict House Prices with Multivariable Linear Regression/14. Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques.mp4 214MB
11. Use Tensorflow to Classify Handwritten Digits/12. Different Model Architectures Experimenting with Dropout.mp4 214MB
08. Test and Evaluate a Naive Bayes Classifier Part 3/06. Visualising the Decision Boundary.mp4 205MB
08. Test and Evaluate a Naive Bayes Classifier Part 3/11. A Naive Bayes Implementation using SciKit Learn.mp4 195MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/11. How to Create 3-Dimensional Charts.mp4 193MB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/09. Use Regularisation to Prevent Overfitting Early Stopping & Dropout Techniques.mp4 192MB
12. Serving a Tensorflow Model through a Website/07. Loading a Tensorflow.js Model and Starting your own Server.mp4 188MB
12. Serving a Tensorflow Model through a Website/09. Styling an HTML Canvas.mp4 187MB
12. Serving a Tensorflow Model through a Website/16. Adding the Game Logic.mp4 173MB
12. Serving a Tensorflow Model through a Website/10. Drawing on an HTML Canvas.mp4 172MB
03. Python Programming for Data Science and Machine Learning/18. How to Make Sense of Python Documentation for Data Visualisation.mp4 171MB
03. Python Programming for Data Science and Machine Learning/19. Working with Python Objects to Analyse Data.mp4 170MB
05. Predict House Prices with Multivariable Linear Regression/11. Visualising Correlations with a Heatmap.mp4 169MB
12. Serving a Tensorflow Model through a Website/13. Resizing and Adding Padding to Images.mp4 158MB
03. Python Programming for Data Science and Machine Learning/17. [Python] - Objects - Understanding Attributes and Methods.mp4 157MB
11. Use Tensorflow to Classify Handwritten Digits/11. Name Scoping and Image Visualisation in Tensorboard.mp4 155MB
03. Python Programming for Data Science and Machine Learning/09. [Python & Pandas] - Dataframes and Series.mp4 153MB
05. Predict House Prices with Multivariable Linear Regression/26. Residual Analysis (Part 2) Graphing and Comparing Regression Residuals.mp4 153MB
05. Predict House Prices with Multivariable Linear Regression/27. Making Predictions (Part 1) MSE & R-Squared.mp4 153MB
11. Use Tensorflow to Classify Handwritten Digits/06. Creating Tensors and Setting up the Neural Network Architecture.mp4 151MB
12. Serving a Tensorflow Model through a Website/06. HTML and CSS Styling.mp4 150MB
05. Predict House Prices with Multivariable Linear Regression/23. Model Simplification & Baysian Information Criterion.mp4 150MB
02. Predict Movie Box Office Revenue with Linear Regression/03. Explore & Visualise the Data with Python.mp4 148MB
09. Introduction to Neural Networks and How to Use Pre-Trained Models/02. Layers, Feature Generation and Learning.mp4 147MB
05. Predict House Prices with Multivariable Linear Regression/22. Understanding VIF & Testing for Multicollinearity.mp4 144MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/06. Joint & Conditional Probability.mp4 142MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/15. Reshaping and Slicing N-Dimensional Arrays.mp4 141MB
05. Predict House Prices with Multivariable Linear Regression/07. Working with Index Data, Pandas Series, and Dummy Variables.mp4 141MB
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 137MB
05. Predict House Prices with Multivariable Linear Regression/04. Clean and Explore the Data (Part 2) Find Missing Values.mp4 135MB
09. Introduction to Neural Networks and How to Use Pre-Trained Models/06. Making Predictions using InceptionResNet.mp4 135MB
05. Predict House Prices with Multivariable Linear Regression/30. [Python] - Conditional Statements - Build a Valuation Tool (Part 2).mp4 134MB
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 133MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/11. [Python] - Generator Functions & the yield Keyword.mp4 133MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/12. Understanding Partial Derivatives and How to use SymPy.mp4 133MB
12. Serving a Tensorflow Model through a Website/04. Converting a Model to Tensorflow.js.mp4 132MB
07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/02. Create a Full Matrix.mp4 132MB
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 131MB
05. Predict House Prices with Multivariable Linear Regression/29. Build a Valuation Tool (Part 1) Working with Pandas Series & Numpy ndarrays.mp4 131MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/14. [Python] - Loops and Performance Considerations.mp4 131MB
05. Predict House Prices with Multivariable Linear Regression/12. Techniques to Style Scatter Plots.mp4 129MB
11. Use Tensorflow to Classify Handwritten Digits/09. Tensorboard Summaries and the Filewriter.mp4 128MB
03. Python Programming for Data Science and Machine Learning/13. [Python] - Functions - Part 2 Arguments & Parameters.mp4 128MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/30. Styling Word Clouds with Custom Fonts.mp4 127MB
05. Predict House Prices with Multivariable Linear Regression/20. Improving the Model by Transforming the Data.mp4 127MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/21. Plotting the Mean Squared Error (MSE) on a Surface (Part 2).mp4 125MB
05. Predict House Prices with Multivariable Linear Regression/25. Residual Analysis (Part 1) Predicted vs Actual Values.mp4 124MB
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 122MB
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 118MB
11. Use Tensorflow to Classify Handwritten Digits/10. Understanding the Tensorflow Graph Nodes and Edges.mp4 116MB
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 112MB
05. Predict House Prices with Multivariable Linear Regression/10. Calculating Correlations and the Problem posed by Multicollinearity.mp4 111MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/22. Running Gradient Descent with a MSE Cost Function.mp4 111MB
11. Use Tensorflow to Classify Handwritten Digits/13. Prediction and Model Evaluation.mp4 111MB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/04. Exploring the CIFAR Data.mp4 110MB
12. Serving a Tensorflow Model through a Website/02. Saving Tensorflow Models.mp4 110MB
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 107MB
02. Predict Movie Box Office Revenue with Linear Regression/05. Analyse and Evaluate the Results.mp4 105MB
12. Serving a Tensorflow Model through a Website/15. Making a Prediction from a Digit drawn on the HTML Canvas.mp4 104MB
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 104MB
12. Serving a Tensorflow Model through a Website/03. Loading a SavedModel.mp4 104MB
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 104MB
09. Introduction to Neural Networks and How to Use Pre-Trained Models/07. Coding Challenge Solution Using other Keras Models.mp4 104MB
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 100MB
11. Use Tensorflow to Classify Handwritten Digits/08. TensorFlow Sessions and Batching Data.mp4 100MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/27. Creating your First Word Cloud.mp4 98MB
02. Predict Movie Box Office Revenue with Linear Regression/02. Gather & Clean the Data.mp4 97MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/38. Checkpoint Understanding the Data.mp4 96MB
07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/03. Count the Tokens to Train the Naive Bayes Model.mp4 96MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/21. Removing HTML tags with BeautifulSoup.mp4 96MB
09. Introduction to Neural Networks and How to Use Pre-Trained Models/04. Preprocessing Image Data and How RGB Works.mp4 94MB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/05. Pre-processing Scaling Inputs and Creating a Validation Dataset.mp4 93MB
09. Introduction to Neural Networks and How to Use Pre-Trained Models/03. Costs and Disadvantages of Neural Networks.mp4 92MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/16. Data Visualisation (Part 1) Pie Charts.mp4 91MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/04. LaTeX Markdown and Generating Data with Numpy.mp4 91MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/05. Understanding the Power Rule & Creating Charts with Subplots.mp4 90MB
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 88MB
05. Predict House Prices with Multivariable Linear Regression/03. Clean and Explore the Data (Part 1) Understand the Nature of the Dataset.mp4 87MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/18. Transposing and Reshaping Arrays.mp4 87MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/13. Implementing Batch Gradient Descent with SymPy.mp4 87MB
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 86MB
05. Predict House Prices with Multivariable Linear Regression/28. Making Predictions (Part 2) Standard Deviation, RMSE, and Prediction Intervals.mp4 85MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/07. Bayes Theorem.mp4 84MB
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 83MB
03. Python Programming for Data Science and Machine Learning/15. [Python] - Functions - Part 3 Results & Return Values.mp4 83MB
03. Python Programming for Data Science and Machine Learning/20. [Python] - Tips, Code Style and Naming Conventions.mp4 82MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/19. Implementing a MSE Cost Function.mp4 81MB
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 80MB
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 79MB
12. Serving a Tensorflow Model through a Website/05. Introducing the Website Project and Tooling.mp4 78MB
11. Use Tensorflow to Classify Handwritten Digits/07. Defining the Cross Entropy Loss Function, the Optimizer and the Metrics.mp4 75MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/23. Visualising the Optimisation on a 3D Surface.mp4 75MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/20. Understanding Nested Loops and Plotting the MSE Function (Part 1).mp4 73MB
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 72MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/20. Word Stemming & Removing Punctuation.mp4 71MB
03. Python Programming for Data Science and Machine Learning/05. [Python] - Variables and Types.mp4 71MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/16. Concatenating Numpy Arrays.mp4 71MB
11. Use Tensorflow to Classify Handwritten Digits/04. Data Preprocessing One-Hot Encoding and Creating the Validation Dataset.mp4 70MB
08. Test and Evaluate a Naive Bayes Classifier Part 3/02. Joint Conditional Probability (Part 1) Dot Product.mp4 66MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/03. Introduction to Cost Functions.mp4 66MB
09. Introduction to Neural Networks and How to Use Pre-Trained Models/05. Importing Keras Models and the Tensorflow Graph.mp4 65MB
05. Predict House Prices with Multivariable Linear Regression/21. How to Interpret Coefficients using p-Values and Statistical Significance.mp4 65MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/17. Introduction to the Mean Squared Error (MSE).mp4 65MB
05. Predict House Prices with Multivariable Linear Regression/05. Visualising Data (Part 1) Historams, Distributions & Outliers.mp4 65MB
05. Predict House Prices with Multivariable Linear Regression/16. How to Shuffle and Split Training & Testing Data.mp4 64MB
05. Predict House Prices with Multivariable Linear Regression/24. How to Analyse and Plot Regression Residuals.mp4 64MB
08. Test and Evaluate a Naive Bayes Classifier Part 3/03. Joint Conditional Probablity (Part 2) Priors.mp4 64MB
08. Test and Evaluate a Naive Bayes Classifier Part 3/07. False Positive vs False Negatives.mp4 63MB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/11. Model Evaluation and the Confusion Matrix.mp4 63MB
05. Predict House Prices with Multivariable Linear Regression/08. Understanding Descriptive Statistics the Mean vs the Median.mp4 62MB
12. Serving a Tensorflow Model through a Website/11. Data Pre-Processing for Tensorflow.js.mp4 62MB
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 62MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/17. Data Visualisation (Part 2) Donut Charts.mp4 62MB
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 61MB
05. Predict House Prices with Multivariable Linear Regression/06. Visualising Data (Part 2) Seaborn and Probability Density Functions.mp4 57MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/29. Solving the Hamlet Challenge.mp4 57MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/15. Saving a JSON File with Pandas.mp4 56MB
05. Predict House Prices with Multivariable Linear Regression/02. Gathering the Boston House Price Data.mp4 56MB
05. Predict House Prices with Multivariable Linear Regression/17. Running a Multivariable Regression.mp4 56MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/33. Coding Challenge Find the Longest Email.mp4 54MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/22. Creating a Function for Text Processing.mp4 54MB
03. Python Programming for Data Science and Machine Learning/07. [Python] - Lists and Arrays.mp4 53MB
07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/05. Calculate the Token Probabilities and Save the Trained Model.mp4 53MB
08. Test and Evaluate a Naive Bayes Classifier Part 3/09. The Precision Metric.mp4 53MB
11. Use Tensorflow to Classify Handwritten Digits/02. Getting the Data and Loading it into Numpy Arrays.mp4 53MB
03. Python Programming for Data Science and Machine Learning/02. Mac Users - Install Anaconda.mp4 52MB
08. Test and Evaluate a Naive Bayes Classifier Part 3/04. Making Predictions Comparing Joint Probabilities.mp4 52MB
09. Introduction to Neural Networks and How to Use Pre-Trained Models/01. The Human Brain and the Inspiration for Artificial Neural Networks.mp4 52MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/18. Introduction to Natural Language Processing (NLP).mp4 51MB
03. Python Programming for Data Science and Machine Learning/01. Windows Users - Install Anaconda.mp4 50MB
05. Predict House Prices with Multivariable Linear Regression/15. Understanding Multivariable Regression.mp4 49MB
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 49MB
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 47MB
07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/04. Sum the Tokens across the Spam and Ham Subsets.mp4 47MB
11. Use Tensorflow to Classify Handwritten Digits/05. What is a Tensor.mp4 45MB
01. Introduction to the Course/01. What is Machine Learning.mp4 45MB
01. Introduction to the Course/02. What is Data Science.mp4 43MB
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 42MB
03. Python Programming for Data Science and Machine Learning/03. Does LSD Make You Better at Maths.mp4 42MB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/02. Installing Tensorflow and Keras for Jupyter.mp4 42MB
03. Python Programming for Data Science and Machine Learning/11. [Python] - Functions - Part 1 Defining and Calling Functions.mp4 42MB
12. Serving a Tensorflow Model through a Website/08. Adding a Favicon.mp4 42MB
08. Test and Evaluate a Naive Bayes Classifier Part 3/05. The Accuracy Metric.mp4 41MB
05. Predict House Prices with Multivariable Linear Regression/01. Defining the Problem.mp4 40MB
12. Serving a Tensorflow Model through a Website/17. Publish and Share your Website!.mp4 39MB
12. Serving a Tensorflow Model through a Website/01. What you'll make.mp4 38MB
07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/06. Coding Challenge Prepare the Test Data.mp4 36MB
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 33MB
05. Predict House Prices with Multivariable Linear Regression/09. Introduction to Correlation Understanding Strength & Direction.mp4 33MB
11. Use Tensorflow to Classify Handwritten Digits/03. Data Exploration and Understanding the Structure of the Input Data.mp4 32MB
05. Predict House Prices with Multivariable Linear Regression/18. How to Calculate the Model Fit with R-Squared.mp4 32MB
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 32MB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/03. Gathering the CIFAR 10 Dataset.mp4 31MB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/01. Solving a Business Problem with Image Classification.mp4 31MB
02. Predict Movie Box Office Revenue with Linear Regression/01. Introduction to Linear Regression & Specifying the Problem.mp4 30MB
02. Predict Movie Box Office Revenue with Linear Regression/04. The Intuition behind the Linear Regression Model.mp4 30MB
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 29MB
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 29MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/05. Basic Probability.mp4 29MB
08. Test and Evaluate a Naive Bayes Classifier Part 3/08. The Recall Metric.mp4 28MB
08. Test and Evaluate a Naive Bayes Classifier Part 3/01. Set up the Testing Notebook.mp4 26MB
08. Test and Evaluate a Naive Bayes Classifier Part 3/10. The F-score or F1 Metric.mp4 25MB
08. Test and Evaluate a Naive Bayes Classifier Part 3/01.1 SpamData.zip 23MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/02. How a Machine Learns.mp4 23MB
07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/01.2 SpamData.zip 22MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/02.1 SpamData.zip 21MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/01. What's Coming Up.mp4 21MB
05. Predict House Prices with Multivariable Linear Regression/19. Introduction to Model Evaluation.mp4 16MB
11. Use Tensorflow to Classify Handwritten Digits/02.1 MNIST.zip 15MB
11. Use Tensorflow to Classify Handwritten Digits/01. What's coming up.mp4 7MB
12. Serving a Tensorflow Model through a Website/16.1 math_garden_stub complete.zip 4MB
12. Serving a Tensorflow Model through a Website/12.1 math_garden_stub 12.12 checkpoint.zip 4MB
05. Predict House Prices with Multivariable Linear Regression/33.1 04 Multivariable Regression.ipynb.zip 4MB
12. Serving a Tensorflow Model through a Website/03.1 MNIST_Model_Load_Files.zip 3MB
03. Python Programming for Data Science and Machine Learning/04.1 12 Rules to Learn to Code.pdf 2MB
12. Serving a Tensorflow Model through a Website/04.1 TFJS.zip 2MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/24.1 03 Gradient Descent.ipynb.zip 1MB
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 978KB
09. Introduction to Neural Networks and How to Use Pre-Trained Models/08.1 09 Neural Nets Pretrained Image Classification.ipynb.zip 572KB
09. Introduction to Neural Networks and How to Use Pre-Trained Models/04.1 TF_Keras_Classification_Images.zip 501KB
02. Predict Movie Box Office Revenue with Linear Regression/02.2 cost_revenue_dirty.csv 375KB
08. Test and Evaluate a Naive Bayes Classifier Part 3/12.2 07 Bayes Classifier - Testing, Inference & Evaluation.ipynb.zip 243KB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/13.1 10 Neural Nets - Keras CIFAR10 Classification.ipynb.zip 120KB
01. Introduction to the Course/03.1 ML Data Science Syllabus.pdf 104KB
02. Predict Movie Box Office Revenue with Linear Regression/03.2 cost_revenue_clean.csv 91KB
02. Predict Movie Box Office Revenue with Linear Regression/06.1 01 Linear Regression (complete).ipynb.zip 75KB
04. Introduction to Optimisation and the Gradient Descent Algorithm/06. [Python] - Loops and the Gradient Descent Algorithm.srt 44KB
12. Serving a Tensorflow Model through a Website/05.1 math_garden_stub.zip 44KB
04. Introduction to Optimisation and the Gradient Descent Algorithm/08. [Python] - Advanced Functions and the Pitfalls of Optimisation (Part 1).srt 43KB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/12. Model Evaluation and the Confusion Matrix.srt 40KB
12. Serving a Tensorflow Model through a Website/09. Styling an HTML Canvas.srt 39KB
12. Serving a Tensorflow Model through a Website/12. Introduction to OpenCV.srt 38KB
12. Serving a Tensorflow Model through a Website/16. Adding the Game Logic.srt 38KB
12. Serving a Tensorflow Model through a Website/06. HTML and CSS Styling.srt 38KB
12. Serving a Tensorflow Model through a Website/10. Drawing on an HTML Canvas.srt 38KB
04. Introduction to Optimisation and the Gradient Descent Algorithm/10. Understanding the Learning Rate.srt 38KB
02. Predict Movie Box Office Revenue with Linear Regression/04.1 01 Linear Regression (checkpoint).ipynb.zip 38KB
12. Serving a Tensorflow Model through a Website/07. Loading a Tensorflow.js Model and Starting your own Server.srt 37KB
03. Python Programming for Data Science and Machine Learning/21.1 02 Python Intro.ipynb.zip 36KB
03. Python Programming for Data Science and Machine Learning/10. [Python] - Module Imports.srt 36KB
12. Serving a Tensorflow Model through a Website/14. Calculating the Centre of Mass and Shifting the Image.srt 35KB
08. Test and Evaluate a Naive Bayes Classifier Part 3/11. A Naive Bayes Implementation using SciKit Learn.srt 34KB
04. Introduction to Optimisation and the Gradient Descent Algorithm/09. [Python] - Tuples and the Pitfalls of Optimisation (Part 2).srt 34KB
08. Test and Evaluate a Naive Bayes Classifier Part 3/06. Visualising the Decision Boundary.srt 33KB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/10. Use the Model to Make Predictions.srt 33KB
02. Predict Movie Box Office Revenue with Linear Regression/03. Explore & Visualise the Data with Python.srt 31KB
11. Use Tensorflow to Classify Handwritten Digits/12. Different Model Architectures Experimenting with Dropout.srt 30KB
03. Python Programming for Data Science and Machine Learning/17. [Python] - Objects - Understanding Attributes and Methods.srt 30KB
11. Use Tensorflow to Classify Handwritten Digits/06. Creating Tensors and Setting up the Neural Network Architecture.srt 29KB
05. Predict House Prices with Multivariable Linear Regression/14. Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques.srt 29KB
05. Predict House Prices with Multivariable Linear Regression/32. Build a Valuation Tool (Part 3) Docstrings & Creating your own Python Module.srt 28KB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/09. Use Regularisation to Prevent Overfitting Early Stopping & Dropout Techniques.srt 28KB
03. Python Programming for Data Science and Machine Learning/09. [Python & Pandas] - Dataframes and Series.srt 28KB
09. Introduction to Neural Networks and How to Use Pre-Trained Models/02. Layers, Feature Generation and Learning.srt 28KB
03. Python Programming for Data Science and Machine Learning/19. Working with Python Objects to Analyse Data.srt 27KB
12. Serving a Tensorflow Model through a Website/13. Resizing and Adding Padding to Images.srt 27KB
03. Python Programming for Data Science and Machine Learning/18. How to Make Sense of Python Documentation for Data Visualisation.srt 27KB
11. Use Tensorflow to Classify Handwritten Digits/11. Name Scoping and Image Visualisation in Tensorboard.srt 26KB
12. Serving a Tensorflow Model through a Website/03. Loading a SavedModel.srt 26KB
04. Introduction to Optimisation and the Gradient Descent Algorithm/11. How to Create 3-Dimensional Charts.srt 26KB
05. Predict House Prices with Multivariable Linear Regression/22. Understanding VIF & Testing for Multicollinearity.srt 26KB
05. Predict House Prices with Multivariable Linear Regression/11. Visualising Correlations with a Heatmap.srt 24KB
05. Predict House Prices with Multivariable Linear Regression/27. Making Predictions (Part 1) MSE & R-Squared.srt 24KB
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 24KB
11. Use Tensorflow to Classify Handwritten Digits/09. Tensorboard Summaries and the Filewriter.srt 23KB
05. Predict House Prices with Multivariable Linear Regression/23. Model Simplification & Baysian Information Criterion.srt 23KB
04. Introduction to Optimisation and the Gradient Descent Algorithm/15. Reshaping and Slicing N-Dimensional Arrays.srt 23KB
05. Predict House Prices with Multivariable Linear Regression/26. Residual Analysis (Part 2) Graphing and Comparing Regression Residuals.srt 23KB
02. Predict Movie Box Office Revenue with Linear Regression/05. Analyse and Evaluate the Results.srt 22KB
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 22KB
04. Introduction to Optimisation and the Gradient Descent Algorithm/22. Running Gradient Descent with a MSE Cost Function.srt 22KB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/11. [Python] - Generator Functions & the yield Keyword.srt 22KB
07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/02. Create a Full Matrix.srt 22KB
05. Predict House Prices with Multivariable Linear Regression/20. Improving the Model by Transforming the Data.srt 22KB
05. Predict House Prices with Multivariable Linear Regression/30. [Python] - Conditional Statements - Build a Valuation Tool (Part 2).srt 21KB
12. Serving a Tensorflow Model through a Website/02. Saving Tensorflow Models.srt 21KB
11. Use Tensorflow to Classify Handwritten Digits/10. Understanding the Tensorflow Graph Nodes and Edges.srt 21KB
12. Serving a Tensorflow Model through a Website/04. Converting a Model to Tensorflow.js.srt 21KB
05. Predict House Prices with Multivariable Linear Regression/29. Build a Valuation Tool (Part 1) Working with Pandas Series & Numpy ndarrays.srt 21KB
03. Python Programming for Data Science and Machine Learning/13. [Python] - Functions - Part 2 Arguments & Parameters.srt 21KB
05. Predict House Prices with Multivariable Linear Regression/07. Working with Index Data, Pandas Series, and Dummy Variables.srt 21KB
05. Predict House Prices with Multivariable Linear Regression/12. Techniques to Style Scatter Plots.srt 21KB
11. Use Tensorflow to Classify Handwritten Digits/08. TensorFlow Sessions and Batching Data.srt 20KB
04. Introduction to Optimisation and the Gradient Descent Algorithm/12. Understanding Partial Derivatives and How to use SymPy.srt 20KB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/05. Pre-processing Scaling Inputs and Creating a Validation Dataset.srt 20KB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/06. Joint & Conditional Probability.srt 20KB
09. Introduction to Neural Networks and How to Use Pre-Trained Models/03. Costs and Disadvantages of Neural Networks.srt 19KB
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 19KB
09. Introduction to Neural Networks and How to Use Pre-Trained Models/06. Making Predictions using InceptionResNet.srt 19KB
11. Use Tensorflow to Classify Handwritten Digits/13. Prediction and Model Evaluation.srt 19KB
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 19KB
05. Predict House Prices with Multivariable Linear Regression/04. Clean and Explore the Data (Part 2) Find Missing Values.srt 19KB
07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/03. Count the Tokens to Train the Naive Bayes Model.srt 18KB
05. Predict House Prices with Multivariable Linear Regression/25. Residual Analysis (Part 1) Predicted vs Actual Values.srt 18KB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/04. Exploring the CIFAR Data.srt 18KB
04. Introduction to Optimisation and the Gradient Descent Algorithm/05. Understanding the Power Rule & Creating Charts with Subplots.srt 18KB
04. Introduction to Optimisation and the Gradient Descent Algorithm/14. [Python] - Loops and Performance Considerations.srt 18KB
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 18KB
05. Predict House Prices with Multivariable Linear Regression/10. Calculating Correlations and the Problem posed by Multicollinearity.srt 18KB
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 18KB
04. Introduction to Optimisation and the Gradient Descent Algorithm/21. Plotting the Mean Squared Error (MSE) on a Surface (Part 2).srt 17KB
04. Introduction to Optimisation and the Gradient Descent Algorithm/04. LaTeX Markdown and Generating Data with Numpy.srt 17KB
12. Serving a Tensorflow Model through a Website/05. Introducing the Website Project and Tooling.srt 17KB
12. Serving a Tensorflow Model through a Website/15. Making a Prediction from a Digit drawn on the HTML Canvas.srt 17KB
03. Python Programming for Data Science and Machine Learning/20. [Python] - Tips, Code Style and Naming Conventions.srt 17KB
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 17KB
03. Python Programming for Data Science and Machine Learning/05. [Python] - Variables and Types.srt 17KB
03. Python Programming for Data Science and Machine Learning/15. [Python] - Functions - Part 3 Results & Return Values.srt 17KB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/16. Data Visualisation (Part 1) Pie Charts.srt 16KB
09. Introduction to Neural Networks and How to Use Pre-Trained Models/04. Preprocessing Image Data and How RGB Works.srt 16KB
05. Predict House Prices with Multivariable Linear Regression/03. Clean and Explore the Data (Part 1) Understand the Nature of the Dataset.srt 16KB
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 15KB
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 15KB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/07. Bayes Theorem.srt 15KB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/30. Styling Word Clouds with Custom Fonts.srt 15KB
05. Predict House Prices with Multivariable Linear Regression/24. How to Analyse and Plot Regression Residuals.srt 15KB
05. Predict House Prices with Multivariable Linear Regression/28. Making Predictions (Part 2) Standard Deviation, RMSE, and Prediction Intervals.srt 15KB
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 15KB
05. Predict House Prices with Multivariable Linear Regression/05. Visualising Data (Part 1) Historams, Distributions & Outliers.srt 14KB
11. Use Tensorflow to Classify Handwritten Digits/07. Defining the Cross Entropy Loss Function, the Optimizer and the Metrics.srt 14KB
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 14KB
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 14KB
04. Introduction to Optimisation and the Gradient Descent Algorithm/20. Understanding Nested Loops and Plotting the MSE Function (Part 1).srt 14KB
02. Predict Movie Box Office Revenue with Linear Regression/02. Gather & Clean the Data.srt 14KB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/27. Creating your First Word Cloud.srt 14KB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/38. Checkpoint Understanding the Data.srt 14KB
04. Introduction to Optimisation and the Gradient Descent Algorithm/19. Implementing a MSE Cost Function.srt 14KB
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 14KB
04. Introduction to Optimisation and the Gradient Descent Algorithm/18. Transposing and Reshaping Arrays.srt 14KB
08. Test and Evaluate a Naive Bayes Classifier Part 3/12.1 08 Naive Bayes with scikit-learn.ipynb.zip 13KB
09. Introduction to Neural Networks and How to Use Pre-Trained Models/07. Coding Challenge Solution Using other Keras Models.srt 13KB
04. Introduction to Optimisation and the Gradient Descent Algorithm/13. Implementing Batch Gradient Descent with SymPy.srt 13KB
08. Test and Evaluate a Naive Bayes Classifier Part 3/07. False Positive vs False Negatives.srt 13KB
08. Test and Evaluate a Naive Bayes Classifier Part 3/02. Joint Conditional Probability (Part 1) Dot Product.srt 13KB
11. Use Tensorflow to Classify Handwritten Digits/04. Data Preprocessing One-Hot Encoding and Creating the Validation Dataset.srt 13KB
04. Introduction to Optimisation and the Gradient Descent Algorithm/17. Introduction to the Mean Squared Error (MSE).srt 13KB
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 12KB
03. Python Programming for Data Science and Machine Learning/07. [Python] - Lists and Arrays.srt 12KB
05. Predict House Prices with Multivariable Linear Regression/08. Understanding Descriptive Statistics the Mean vs the Median.srt 12KB
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 12KB
12. Serving a Tensorflow Model through a Website/11. Data Pre-Processing for Tensorflow.js.srt 12KB
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 12KB
05. Predict House Prices with Multivariable Linear Regression/16. How to Shuffle and Split Training & Testing Data.srt 12KB
09. Introduction to Neural Networks and How to Use Pre-Trained Models/05. Importing Keras Models and the Tensorflow Graph.srt 11KB
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 11KB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/21. Removing HTML tags with BeautifulSoup.srt 11KB
09. Introduction to Neural Networks and How to Use Pre-Trained Models/01. The Human Brain and the Inspiration for Artificial Neural Networks.srt 11KB
02. Predict Movie Box Office Revenue with Linear Regression/04. The Intuition behind the Linear Regression Model.srt 11KB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/11. Model Evaluation and the Confusion Matrix.srt 11KB
05. Predict House Prices with Multivariable Linear Regression/21. How to Interpret Coefficients using p-Values and Statistical Significance.srt 11KB
04. Introduction to Optimisation and the Gradient Descent Algorithm/23. Visualising the Optimisation on a 3D Surface.srt 11KB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/20. Word Stemming & Removing Punctuation.srt 11KB
08. Test and Evaluate a Naive Bayes Classifier Part 3/03. Joint Conditional Probablity (Part 2) Priors.srt 11KB
03. Python Programming for Data Science and Machine Learning/11. [Python] - Functions - Part 1 Defining and Calling Functions.srt 10KB
05. Predict House Prices with Multivariable Linear Regression/17. Running a Multivariable Regression.srt 10KB
12. Serving a Tensorflow Model through a Website/01. What you'll make.srt 10KB
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 10KB
08. Test and Evaluate a Naive Bayes Classifier Part 3/04. Making Predictions Comparing Joint Probabilities.srt 10KB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/17. Data Visualisation (Part 2) Donut Charts.srt 10KB
12. Serving a Tensorflow Model through a Website/17. Publish and Share your Website!.srt 10KB
08. Test and Evaluate a Naive Bayes Classifier Part 3/09. The Precision Metric.srt 10KB
04. Introduction to Optimisation and the Gradient Descent Algorithm/03. Introduction to Cost Functions.srt 9KB
07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/05. Calculate the Token Probabilities and Save the Trained Model.srt 9KB
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 9KB
11. Use Tensorflow to Classify Handwritten Digits/02. Getting the Data and Loading it into Numpy Arrays.srt 9KB
11. Use Tensorflow to Classify Handwritten Digits/05. What is a Tensor.srt 9KB
05. Predict House Prices with Multivariable Linear Regression/06. Visualising Data (Part 2) Seaborn and Probability Density Functions.srt 9KB
04. Introduction to Optimisation and the Gradient Descent Algorithm/16. Concatenating Numpy Arrays.srt 9KB
03. Python Programming for Data Science and Machine Learning/01. Windows Users - Install Anaconda.srt 9KB
02. Predict Movie Box Office Revenue with Linear Regression/01. Introduction to Linear Regression & Specifying the Problem.srt 9KB
05. Predict House Prices with Multivariable Linear Regression/02. Gathering the Boston House Price Data.srt 9KB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/22. Creating a Function for Text Processing.srt 8KB
05. Predict House Prices with Multivariable Linear Regression/09. Introduction to Correlation Understanding Strength & Direction.srt 8KB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/18. Introduction to Natural Language Processing (NLP).srt 8KB
03. Python Programming for Data Science and Machine Learning/02. Mac Users - Install Anaconda.srt 8KB
07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/04. Sum the Tokens across the Spam and Ham Subsets.srt 8KB
08. Test and Evaluate a Naive Bayes Classifier Part 3/05. The Accuracy Metric.srt 8KB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/33. Coding Challenge Find the Longest Email.srt 8KB
05. Predict House Prices with Multivariable Linear Regression/15. Understanding Multivariable Regression.srt 8KB
12. Serving a Tensorflow Model through a Website/08. Adding a Favicon.srt 7KB
03. Python Programming for Data Science and Machine Learning/03. Does LSD Make You Better at Maths.srt 7KB
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 7KB
04. Introduction to Optimisation and the Gradient Descent Algorithm/02. How a Machine Learns.srt 7KB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/15. Saving a JSON File with Pandas.srt 7KB
01. Introduction to the Course/01. What is Machine Learning.srt 7KB
11. Use Tensorflow to Classify Handwritten Digits/14.1 11 Neural Networks - TF Handwriting Recognition.ipynb.zip 7KB
08. Test and Evaluate a Naive Bayes Classifier Part 3/08. The Recall Metric.srt 7KB
11. Use Tensorflow to Classify Handwritten Digits/03. Data Exploration and Understanding the Structure of the Input Data.srt 6KB
05. Predict House Prices with Multivariable Linear Regression/01. Defining the Problem.srt 6KB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/02. Installing Tensorflow and Keras for Jupyter.srt 6KB
12. Serving a Tensorflow Model through a Website/02.1 11 Neural Networks - TF Handwriting Recognition.ipynb.zip 6KB
12. Serving a Tensorflow Model through a Website/03.2 12 TF SavedModel Export Completed.ipynb.zip 6KB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/03. Gathering the CIFAR 10 Dataset.srt 6KB
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 6KB
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 6KB
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 6KB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/29. Solving the Hamlet Challenge.srt 6KB
07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/07.1 07 Bayes Classifier - Training.ipynb.zip 6KB
01. Introduction to the Course/02. What is Data Science.srt 6KB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/05. Basic Probability.srt 5KB
07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/06. Coding Challenge Prepare the Test Data.srt 5KB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/01. Solving a Business Problem with Image Classification.srt 5KB
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 5KB
12. Serving a Tensorflow Model through a Website/07.1 x_test2_ylabel1.txt 5KB
12. Serving a Tensorflow Model through a Website/07.2 x_test0_ylabel7.txt 5KB
12. Serving a Tensorflow Model through a Website/07.3 x_test1_ylabel2.txt 5KB
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 5KB
08. Test and Evaluate a Naive Bayes Classifier Part 3/10. The F-score or F1 Metric.srt 4KB
05. Predict House Prices with Multivariable Linear Regression/18. How to Calculate the Model Fit with R-Squared.srt 4KB
13. Next Steps/01. Where next.html 4KB
04. Introduction to Optimisation and the Gradient Descent Algorithm/01. What's Coming Up.srt 4KB
08. Test and Evaluate a Naive Bayes Classifier Part 3/01. Set up the Testing Notebook.srt 4KB
05. Predict House Prices with Multivariable Linear Regression/19. Introduction to Model Evaluation.srt 4KB
05. Predict House Prices with Multivariable Linear Regression/33.3 boston_valuation.py 3KB
05. Predict House Prices with Multivariable Linear Regression/33.2 04 Valuation Tool.ipynb.zip 3KB
11. Use Tensorflow to Classify Handwritten Digits/01. What's coming up.srt 2KB
01. Introduction to the Course/04. Top Tips for Succeeding on this Course.html 2KB
03. Python Programming for Data Science and Machine Learning/04. Download the 12 Rules to Learn to Code.html 1KB
01. Introduction to the Course/05. Course Resources List.html 1KB
13. Next Steps/03. Stay in Touch!.html 1KB
01. Introduction to the Course/03. Download the Syllabus.html 1KB
02. Predict Movie Box Office Revenue with Linear Regression/07. Join the Student Community.html 730B
07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/08. Any Feedback on this Section.html 527B
09. Introduction to Neural Networks and How to Use Pre-Trained Models/09. Any Feedback on this Section.html 526B
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03. Python Programming for Data Science and Machine Learning/22. Any Feedback on this Section.html 513B
02. Predict Movie Box Office Revenue with Linear Regression/08. Any Feedback on this Section.html 512B
05. Predict House Prices with Multivariable Linear Regression/34. Any Feedback on this Section.html 512B
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13. Next Steps/02. What Modules Do You Want to See.html 431B
09. Introduction to Neural Networks and How to Use Pre-Trained Models/08. Download the Complete Notebook Here.html 264B
02. Predict Movie Box Office Revenue with Linear Regression/06. Download the Complete Notebook Here.html 242B
03. Python Programming for Data Science and Machine Learning/21. Download the Complete Notebook Here.html 242B
04. Introduction to Optimisation and the Gradient Descent Algorithm/24. Download the Complete Notebook Here.html 242B
05. Predict House Prices with Multivariable Linear Regression/33. Download the Complete Notebook Here.html 242B
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/39. Download the Complete Notebook Here.html 242B
07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/07. Download the Complete Notebook Here.html 242B
08. Test and Evaluate a Naive Bayes Classifier Part 3/12. Download the Complete Notebook Here.html 242B
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/13. Download the Complete Notebook Here.html 242B
11. Use Tensorflow to Classify Handwritten Digits/14. Download the Complete Notebook Here.html 242B
03. Python Programming for Data Science and Machine Learning/06. Python Variable Coding Exercise.html 156B
03. Python Programming for Data Science and Machine Learning/08. Python Lists Coding Exercise.html 156B
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03. Python Programming for Data Science and Machine Learning/16. Python Functions Coding Exercise - Part 3.html 156B
04. Introduction to Optimisation and the Gradient Descent Algorithm/07. Python Loops Coding Exercise.html 156B
05. Predict House Prices with Multivariable Linear Regression/31. Python Conditional Statement Coding Exercise.html 156B
03. Python Programming for Data Science and Machine Learning/09.1 lsd_math_score_data.csv 155B
01. Introduction to the Course/04.1 App Brewery Cornell Notes Template.html 141B
0. Websites you may like/[FCS Forum].url 133B
0. Websites you may like/[FreeCourseSite.com].url 127B
0. Websites you may like/[CourseClub.ME].url 122B
02. Predict Movie Box Office Revenue with Linear Regression/01.1 Course Resources.html 122B
03. Python Programming for Data Science and Machine Learning/01.1 Course Resources.html 122B
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04. Introduction to Optimisation and the Gradient Descent Algorithm/01.1 Course Resources.html 122B
05. Predict House Prices with Multivariable Linear Regression/01.1 Course Resources.html 122B
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09. Introduction to Neural Networks and How to Use Pre-Trained Models/01.1 Course Resources.html 122B
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/01.1 Course Resources.html 122B
11. Use Tensorflow to Classify Handwritten Digits/01.1 Course Resources.html 122B
02. Predict Movie Box Office Revenue with Linear Regression/02.1 The-Numbers Movie Budgets.html 102B
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0. Websites you may like/[GigaCourse.Com].url 49B