Machine Learning with Python – 4 Days Certificate for Sim Hong Ming

Introduction to Applied Machine Learning

• Statistical learning vs. Machine learning
• Iteration and evaluation
• Bias-Variance trade-off

Supervised Learning and Unsupervised Learning

• Machine Learning Languages, Types, and Examples
• Supervised vs Unsupervised Learning

Supervised Learning

• Decision Trees
• Random Forests
• Model Evaluation

Machine Learning with Python

• Choice of libraries
• Add-on tools

Regression

• Linear regression
• Generalizations and Nonlinearity
• Exercises

Classification

• Bayesian refresher
• Naive Bayes
• Logistic regression
• K-Nearest neighbors
• Exercises

Cross-validation and Resampling

• Cross-validation approaches
• Bootstrap
• Exercises

Unsupervised Learning

• K-means clustering
• Examples
• Challenges of unsupervised learning and beyond K-means

Neural networks

• Layers and nodes
• Python neural network libraries
• Working with scikit-learn
• Working with PyBrain
• Deep Learning