1. Prepare (soft due Tu 10/26, hard due M 11/1)
   1. Content below, if you are new to machine learning some of the optional is strongly recommended.
   2. Sakai quizzes
2. Group Worksheet (soft due W 10/27, hard due M 11/1)
3. Practice (due M 11/8)
4. Perform (due M 11/22)

Content

6.A Predictive Modeling and Regression

1. Ordinary Linear Regression and Intro Scikit-Learn (21 min.)
2. Nonlinear Regression and Scikit-Learn Preprocessing (13 min.)
3. Binary Classification with Logistic Regression (22 min.)

6.B Machine Learning and Classification

1. Naïve Bayes and Text Classification (20 min.) – The video has a type on slide 10, see the pdf of the slides in Box for the fix.
2. K-Nearest Neighbors and Training/Testing (31 min.)

Optional Supplements

Chapter 5 Machine Learning from the Python Data Science Handbook provides a very nice treatment of many of the topics from the above videos and more. If you are new to machine learning, we highly recommend that you read sections 5.1 What is Machine Learning through 5.4 Feature Engineering after completing the videos. After that, you can optionally read any of the In-Depth sections about specific algorithms for prediction.

In addition, the scikit-learn documentation itself provides several resources for working with the library:

• Scikit-learn Getting Started and Scikit-learn tutorials provide some short introductory materials
• Scikit-learn examples has an extensive library of example applications with code
• Scikit-learn user guide explains the classes of models and features of the library
• Scikit-learn api reference contains the full api reference