ningkko / applied-machine-learning

Special Study at Smith Spring 2019

Applied Machine Learning

2019 Spring Special Study with professor John Foley @Smith College

Notes from the Hundred-Page Machine Learning Book:

• Chapter 1: Introduction
• Chapter 2: Notions and Definitions
• Chapter 3: Fundamental Algorithms
• Chapter 4: Anatomy of a Learning Algorithm
• Chapter 5: Basic Practice
• Chapter 6: Neural Networks and Deep Learning
• K Means Clustering and PCA

Progress:

1. The Hundred-Page Machine Learning Book

• Chapter 1: Introduction ✅
• Chapter 2: Notation and Definitions ✅
• Chapter 3: Fundamental Algorithms ✅
• Chapter 4: Anatomy of a Learning Algorithm ✅
• Chapter 5: Basic Practice ✅
• Chapter 6: Neural Networks and Deep Learning
• Chapter 7: Problems and Solutions
• Chapter 8: Advanced Practice
• Chapter 9: Unsupervised Learning ✅
• Chapter 10: Other Forms of Learning ✅
• Chapter 11: Conclusion

2. Learning scikit-learn: Machine Learning in Python

• Introduction ✅
• Supervised Learning> ✅
• Unsupervised Learning
• Advanced Features

3.Mastering Machine Learning with scikit-learn

• Chapter 1: The Fundamentals of Machine Learning 7✅
• Chapter 2: Linear Regression 21 ✅
• Chapter 3: Feature Extraction and Preprocessing 51 ✅
• Chapter 4: From Linear Regression to Logistic Regression 71 ✅
• Chapter 5: Nonlinear Classification and Regression with Decision Trees 97 ✅
• Chapter 6: Clustering with K-Means 115 ✅
• Chapter 7: Dimensionality Reduction with PCA 137 ✅
• Chapter 8: The Perceptron 155 ⭕
• Chapter 9: From the Perceptron to Support Vector Machines 171 ⭕
• Chapter 10: From the Perceptron to Artificial Neural Networks 187

5. Implements

• Linear Regression ✅
• Perceptron ✅
• SVM ✅
• Decision Tree with CART ✅
• NNWs

Links:

1. CSC293 syllabus
2. The Hundred-Page Machine Learning Book
3. Mastering Machine Learning with Scikit-Learn
4. Learning scikit-learn: Machine Learning in Python
5. A Course in Machine Learning