Machine Learning in Python
Machine Learning using Python is your path to the most exciting careers in data analysis today. As data sources proliferate along with the computing power to process them, going straight to the data is one of the most straightforward ways to quickly gain insights and make predictions.
Machine Learning using Python brings together computer science and statistics to harness that predictive power. It’s a mandatory skill for all aspiring data analysts and data scientists, or anyone else who wants to wrestle all that raw data into refined trends and predictions.
This is a class that will teach you the end-to-end process of investigating data through a machine learning lens. It will teach you how to extract and identify useful features that best represent your data, a few of the most important machine learning algorithms, and how to evaluate the performance of your machine learning algorithms.
This workshop is for analysts, product managers, mathematicians, business managers or anyone else that wants to learn how to code in Python.
In this workshop you’ll learn the end-to-end data science process:
- Collect data from a variety of sources (e.g., Excel, web-scraping, APIs and others)
- Explore large data sets
- Clean and “munge” the data to prepare it for analysis
- Apply machine learning algorithms to gain insight from the data
- Visualize the results of your analysis
This is a very practical and hands-on workshop that has lots of class exercises. You’ll build your own library of Python scripts that can be reused after your done with the course.
Prereqs & Preparation
You must bring a laptop with a text editor.
Sublime Text is recommended and has a free trial version (http://www.sublimetext.com/).
In addition, students should install Anaconda, which is a free package that includes python and a number of tools that will be used in class (http://continuum.io/downloads).
Session I: Introduction
- Setting up your virtual environment
- Python Fundamentals
- Introduction to Data Exploration
- Introduction to Machine Learning
Session II: Fundamental Modeling Techniques
- K-Nearest Neighbors Classification
- Naive Bayes Classification
- Regression and Regularization
- Logistic Regression
Session III: Modeling Techniques and More
- K-Means Clustering
- Ensemble Techniques
- Decision Trees and Random Forests
- Support Vector Machines
- Dimensionality Reduction
Session IV: Hack-A-Day
- Final Project Session
- Final Project Presentation
- Resources, Tools & further study