Introduction-to-Machine-Learning

This repo contains the tutorial of the basics of machine learning. Majority of the content is in Jupyter NoteBook. There are some .py Python files. This tutorial is my learning notes which is heavily based on the Machine Learning Specialization by DeepLearning.AI. We recreated all the charts and reorganized the content. Overtime, we have added more details and expanded certain topics from other courses. But the examples and sample data mainly comes from the Machine Learning Specialization course.

Acknowledgement:

The original tutorial is heavily based on the Machine Learning Specialization and earlier Machine Learning Certificate Course offered by DeepLearning.AI and Stanford Univerity. Please checkout the details here. https://www.deeplearning.ai/courses/machine-learning-specialization/

Additional materials are added from some older Cousera Machine Learning Course from University Of Washington and University of Michigan

https://www.coursera.org/specializations/machine-learning

https://www.coursera.org/learn/python-machine-learning

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Name		Name	Last commit message	Last commit date
Latest commit History 11 Commits
data		data
.gitignore		.gitignore
Introduction_AIML.ipynb		Introduction_AIML.ipynb
ML Master Notes 0 - Learning Machine Learning.ipynb		ML Master Notes 0 - Learning Machine Learning.ipynb
ML Master Notes 1 - Introduction to Machine Learning.ipynb		ML Master Notes 1 - Introduction to Machine Learning.ipynb
ML Master Notes 10 - Feature Scaling.ipynb		ML Master Notes 10 - Feature Scaling.ipynb
ML Master Notes 11 - Feature Scaling - Reversing Scaled Weights.ipynb		ML Master Notes 11 - Feature Scaling - Reversing Scaled Weights.ipynb
ML Master Notes 12 - Learning Rate.ipynb		ML Master Notes 12 - Learning Rate.ipynb
ML Master Notes 13 - Polynomial Regression and Feature Engineering.ipynb		ML Master Notes 13 - Polynomial Regression and Feature Engineering.ipynb
ML Master Notes 14 - Fitting Problem and Regularization.ipynb		ML Master Notes 14 - Fitting Problem and Regularization.ipynb
ML Master Notes 15 - Regularization with SciKit Learn.ipynb		ML Master Notes 15 - Regularization with SciKit Learn.ipynb
ML Master Notes 16 - Logistic Regression.ipynb		ML Master Notes 16 - Logistic Regression.ipynb
ML Master Notes 17 - Logistic Regression and Regularization.ipynb		ML Master Notes 17 - Logistic Regression and Regularization.ipynb
ML Master Notes 17B - Logistic Regression and Regularization (Module Test).ipynb		ML Master Notes 17B - Logistic Regression and Regularization (Module Test).ipynb
ML Master Notes 18 - Logistic Regression on Multi-Class Classification.ipynb		ML Master Notes 18 - Logistic Regression on Multi-Class Classification.ipynb
ML Master Notes 19 - Logistic Softmax and Loss Function.ipynb		ML Master Notes 19 - Logistic Softmax and Loss Function.ipynb
ML Master Notes 2 - Overview of Linear Regression (One Feature).ipynb		ML Master Notes 2 - Overview of Linear Regression (One Feature).ipynb
ML Master Notes 20 - Logistic Categorical CE Softmax Regression.ipynb		ML Master Notes 20 - Logistic Categorical CE Softmax Regression.ipynb
ML Master Notes 21 - Logistic Sparse Categorical CE Softmax Regression.ipynb		ML Master Notes 21 - Logistic Sparse Categorical CE Softmax Regression.ipynb
ML Master Notes 22 - Logistic Softmax Regression and Regularization.ipynb		ML Master Notes 22 - Logistic Softmax Regression and Regularization.ipynb
ML Master Notes 22B - Logistic Softmax Regression and Regularization (Module Test).ipynb		ML Master Notes 22B - Logistic Softmax Regression and Regularization (Module Test).ipynb
ML Master Notes 3 - Simple Linear Regression (One Feature No Intercept).ipynb		ML Master Notes 3 - Simple Linear Regression (One Feature No Intercept).ipynb
ML Master Notes 4 - Simple Linear Regression and Cost Function.ipynb		ML Master Notes 4 - Simple Linear Regression and Cost Function.ipynb
ML Master Notes 5 - Linear Regression (One Feature with Intercept).ipynb		ML Master Notes 5 - Linear Regression (One Feature with Intercept).ipynb
ML Master Notes 6 - Linear Regression (Cost Function vs Weights and Intercept).ipynb		ML Master Notes 6 - Linear Regression (Cost Function vs Weights and Intercept).ipynb
ML Master Notes 7 - Gradient Descent on Linear Regression.ipynb		ML Master Notes 7 - Gradient Descent on Linear Regression.ipynb
ML Master Notes 8 - Multiple Regression and Vectorization.ipynb		ML Master Notes 8 - Multiple Regression and Vectorization.ipynb
ML Master Notes 9 - Multiple Linear Regression and Vectorized Gradient Descent.ipynb		ML Master Notes 9 - Multiple Linear Regression and Vectorized Gradient Descent.ipynb
ML Master Notes Supplument F - Calculus for Gradient Descent.ipynb		ML Master Notes Supplument F - Calculus for Gradient Descent.ipynb
MyRegressionProgramV1.py		MyRegressionProgramV1.py
MyRegressionProgramV2.py		MyRegressionProgramV2.py
MyRegressionProgramV3.py		MyRegressionProgramV3.py
README.md		README.md
testFile.py		testFile.py

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