Advanced Data Science with Python: Machine Learning

Advanced Data Science with Python: Machine Learning

Prerequisites
Knowledge of Python programming and basic features of Python
Able to munge, analyze, and visualize data in Python with Pandas and charting

Syllabus

Unit 1: Introduction and Regression

How to dive into Machine Learning
Simple Linear Regression and Multiple Linear Regression
Forward and Backward Selection
Numpy/Scikit-Learn Lab

Class 2:
Part Classification I
Logistic Regression - Application in Default and other variables
Discriminant Analysis
Naive Bayes
Supervised Learning Lab

Resampling and Model Selection

Cross-Validation
Bootstrap - Breaking it down into simple
Feature Selection
Model Selection and Regularization lab

Class 3:
Classification II
Support Vector Machines SVM
Decision Trees - and Branch Analysis
Bagging and Random Forests
Decision Tree in Python and SVM Lab

Class 4:
Unsupervised Learning - Breaking it down
Principal Component Analysis
Kmeans and Hierarchical Clustering
PCA and Clustering Lab

Recommended Readings

An Introduction to Statistical Learning, by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
Applied Predictive Modeling, by Max Kuhn and Kjell Johnson
Machine Learning for Hackers, by Drew Conway, John White


R Course Recommended Readings

An Introduction to Statistical Learning with Applications in R, by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
Applied Predictive Modeling, by Max Kuhn and Kjell Johnson
Data Mining with R, by Luis Torgo
Machine Learning with R, by Brett Lantz


http://www.qcfinance.in/python-for-data-science-machine-learning/