          Machine Learning with Python – 4 Days培训

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