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

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