# Data Science with Python: Machine Learning

### offered by NYC Data Science Academy

Overview

This 20-hour course covers all the basic machine learning methods and Python modules (especially Scikit-Learn) for implementing them. The five sessions cover: simple and multiple Linear regressions; classification methods including logistic regression, discriminant analysis and naive bayes, support vector machines (SVMs) and tree based methods; cross-validation and feature selection; regularization; principal component analysis (PCA) and clustering algorithms. After successfully completing of this course, you will be able to explain the principles of machine learning algorithms and implement these methods to analyze complex datasets and make predictions.

Syllabus

Unit 1: Introduction and Regression

What is Machine Learning

Simple Linear Regression

Multiple Linear Regression

Numpy/Scikit-Learn Lab

Unit 2: Classification I

Logistic Regression

Discriminant Analysis

Naive Bayes

Supervised Learning Lab

Unit 3: Resampling and Model Selection

Cross-Validation

Bootstrap

Feature Selection

Model Selection and Regularization lab

Unit 4: Classification II

Support Vector Machines

Decision Trees

Bagging and Random Forests

Decision Tree and SVM Lab

Unit 5: Unsupervised Learning

Principal Component Analysis

Kmeans and Hierarchical Clustering

PCA and Clustering Lab

Final Project

After 20 hours of structured lectures, students are encouraged to work on an exploratory data analysis project based on their own interests. A project presentation demo will be arranged afterwards.