Machine-Learning-2018

Introduction

The book for the course: "Machine Learning in Action, ISBN 9781617290183"

The book contains a few errors in the code examples, there are updated versions of the examples on the books GitHub page.: https://github.com/pbharrin/machinelearninginaction

A good place to find datasets to play with is: https://www.kaggle.com/

Course content

Workshop 1: (2/2-2018)

Reading: Chapter 1 + 2
Subjects:
    * PyCharm
    * Python (Syntax, operators, OOP (Linked List), NumPy)
    * Supervised (kNN)
Preparation:
    * Read chapters, install pycharm, install python 2.7

Workshop 2: (16/2-2018)

Reading: Chapter 3 + 4
Subjects:
    * Supervised (DT, Naive Bayes)
    * Cleaning Datasets
Preparation:
    * Read chapters

Workshop 3: (23/2-2018)

Reading: Chapter 8 + 10
Subjects:
    * Forecasting (Linear Regression)
    * Unsupervised (K-means)
Preparation:
    * Read chapters

Workshop 4: (2/3-2018)

Subjects:
    * Working with Sci-kit and SciPy
    * Putting the algorithms to use
Preparation:
    * Follow this course on DataCamp https://www.datacamp.com/courses/cleaning-data-in-python
    * Watch: Lynda.com: Data Science Foundations: Python Scientific Stack (primarily ch. 8 and 9, but skim the whole thing)

Workshop 5: (9/3-2018)

Reading: ???
Subjects:
    * Neural Networks
Preparation:
    * ???

Computer-Science-EAL/Machine-Learning-2018