The Complete Machine Learning Course with Python
This is a complete note of course related to Machine Learning with Python by Anthony NG.
Table of Contents
Section 2: Setting up Environment
Section 5: Support Vector Machine
Section 9: Unsupervised Machine Learning - Dimensionality Reduction
Section 10: Unsupervised Machine Learning - Clustering
Appendix A1: Foundations of Deep Learning
Appendix A2: Foundations of CNN
Copyright 2019 Anthony Ng. Licensed under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at https://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
Google Colab
A note on opening Google Colab files, you may need to install the app.
Github
Link to GitHub for all the notebooks for Machine Learning section. These notebooks can be used for running on your local machine.
Machine Learning 2019
These are the links to the notebooks for Machine Learning section. These notebooks can be run via google colab. This is especially useful for training large and complex algorithms such as tree based models.
Drive Link to Machine Learning
Section 2: Setting up Environment
Installing Anaconda to your local Machine
Jupyter Notebook, lab and environment management
Section 3: Regression
Boston Housing Price Prediction
Section 4: Classification
Section 5: Support Vector Machine
Section 6: Tree
Section 7: Ensemble Models
Bagging Machine Learning Algorithm
AdaBoost and Gradient Boosting Machine
XGBoost Installation Instruction
Section 8: k-Nearest Neighbor
Section 9: Unsupervised Machine Learning - Dimensionality Reduction
Linear Discriminant Analysis (LDA)
Section 10: Unsupervised Machine Learning - Clustering
Deep Learning 2019
ANN
Motivational Example 1 with MNIST dataset
Binary Classification 1 with MNIST dataset
Binary Classification 2 with IMDB dataset
Regression with Boston Housing dataset
CNN
CNN Motivational Example with MNIST dataset
CNN with dogs and cats (subset) dataset
Appendix A1: Foundations of Deep Learning
Drive Link to Foundations of Deep Learning
Binary Classification Example 1
Binary Classification Example 2
Appendix A2: Foundations of CNN
Drive Link to Foundations of CNN
Cats and Dogs - Loading Previously Trained Model
Cats and Dogs - Data Augmentation
Features Extraction with VGG16