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

04. Introduction to Optimisation and the Gradient Descent Algorithm/08. [Python] - Advanced Functions and the Pitfalls of Optimisation (Part 1).mp4

04. Introduction to Optimisation and the Gradient Descent Algorithm/06. [Python] - Loops and the Gradient Descent Algorithm.mp4

10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/12. Model Evaluation and the Confusion Matrix.mp4

05. Predict House Prices with Multivariable Linear Regression/32. Build a Valuation Tool (Part 3) Docstrings & Creating your own Python Module.mp4

04. Introduction to Optimisation and the Gradient Descent Algorithm/10. Understanding the Learning Rate.mp4

12. Serving a Tensorflow Model through a Website/12. Introduction to OpenCV.mp4

03. Python Programming for Data Science and Machine Learning/10. [Python] - Module Imports.mp4

12. Serving a Tensorflow Model through a Website/14. Calculating the Centre of Mass and Shifting the Image.mp4

04. Introduction to Optimisation and the Gradient Descent Algorithm/09. [Python] - Tuples and the Pitfalls of Optimisation (Part 2).mp4

10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/10. Use the Model to Make Predictions.mp4

05. Predict House Prices with Multivariable Linear Regression/14. Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques.mp4

11. Use Tensorflow to Classify Handwritten Digits/12. Different Model Architectures Experimenting with Dropout.mp4

08. Test and Evaluate a Naive Bayes Classifier Part 3/06. Visualising the Decision Boundary.mp4

08. Test and Evaluate a Naive Bayes Classifier Part 3/11. A Naive Bayes Implementation using SciKit Learn.mp4

04. Introduction to Optimisation and the Gradient Descent Algorithm/11. How to Create 3-Dimensional Charts.mp4

10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/09. Use Regularisation to Prevent Overfitting Early Stopping & Dropout Techniques.mp4

12. Serving a Tensorflow Model through a Website/07. Loading a Tensorflow.js Model and Starting your own Server.mp4

12. Serving a Tensorflow Model through a Website/09. Styling an HTML Canvas.mp4

12. Serving a Tensorflow Model through a Website/16. Adding the Game Logic.mp4

12. Serving a Tensorflow Model through a Website/10. Drawing on an HTML Canvas.mp4

03. Python Programming for Data Science and Machine Learning/18. How to Make Sense of Python Documentation for Data Visualisation.mp4

03. Python Programming for Data Science and Machine Learning/19. Working with Python Objects to Analyse Data.mp4

05. Predict House Prices with Multivariable Linear Regression/11. Visualising Correlations with a Heatmap.mp4

12. Serving a Tensorflow Model through a Website/13. Resizing and Adding Padding to Images.mp4

03. Python Programming for Data Science and Machine Learning/17. [Python] - Objects - Understanding Attributes and Methods.mp4

11. Use Tensorflow to Classify Handwritten Digits/11. Name Scoping and Image Visualisation in Tensorboard.mp4

03. Python Programming for Data Science and Machine Learning/09. [Python & Pandas] - Dataframes and Series.mp4

05. Predict House Prices with Multivariable Linear Regression/26. Residual Analysis (Part 2) Graphing and Comparing Regression Residuals.mp4

05. Predict House Prices with Multivariable Linear Regression/27. Making Predictions (Part 1) MSE & R-Squared.mp4

11. Use Tensorflow to Classify Handwritten Digits/06. Creating Tensors and Setting up the Neural Network Architecture.mp4

12. Serving a Tensorflow Model through a Website/06. HTML and CSS Styling.mp4

05. Predict House Prices with Multivariable Linear Regression/23. Model Simplification & Baysian Information Criterion.mp4

02. Predict Movie Box Office Revenue with Linear Regression/03. Explore & Visualise the Data with Python.mp4

09. Introduction to Neural Networks and How to Use Pre-Trained Models/02. Layers, Feature Generation and Learning.mp4

05. Predict House Prices with Multivariable Linear Regression/22. Understanding VIF & Testing for Multicollinearity.mp4

06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/06. Joint & Conditional Probability.mp4

04. Introduction to Optimisation and the Gradient Descent Algorithm/15. Reshaping and Slicing N-Dimensional Arrays.mp4

05. Predict House Prices with Multivariable Linear Regression/07. Working with Index Data, Pandas Series, and Dummy Variables.mp4

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

05. Predict House Prices with Multivariable Linear Regression/04. Clean and Explore the Data (Part 2) Find Missing Values.mp4

09. Introduction to Neural Networks and How to Use Pre-Trained Models/06. Making Predictions using InceptionResNet.mp4

05. Predict House Prices with Multivariable Linear Regression/30. [Python] - Conditional Statements - Build a Valuation Tool (Part 2).mp4

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

06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/11. [Python] - Generator Functions & the yield Keyword.mp4

04. Introduction to Optimisation and the Gradient Descent Algorithm/12. Understanding Partial Derivatives and How to use SymPy.mp4

12. Serving a Tensorflow Model through a Website/04. Converting a Model to Tensorflow.js.mp4

07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/02. Create a Full Matrix.mp4

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

05. Predict House Prices with Multivariable Linear Regression/29. Build a Valuation Tool (Part 1) Working with Pandas Series & Numpy ndarrays.mp4

04. Introduction to Optimisation and the Gradient Descent Algorithm/14. [Python] - Loops and Performance Considerations.mp4