This 20-hour Machine Learning with Python 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 in Python.
Prerequisites
- Knowledge of Python programming
- Able to munge, analyze, and visualize data in Python
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.
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
This course is available for "remote" learning and will be available to anyone with access to an internet device with a microphone (this includes most models of computers, tablets). Classes will take place with a "Live" instructor at the date/times listed below.
Upon registration, the instructor will send along additional information about how to log-on and participate in the class.
School Notes: We offer a certification licensed by the NYS Board of Education.