Intro to Machine Learning: Algorithms for the Rest of Us

Machine learning is often thought of as a black-box of complicated algorithms that scientists use to breathe life into computers. By leveraging their computational power, we can program adaptive algorithms to learn from an otherwise incomprehensible wealth of data. Aimed to demystify the magic behind the machines, we will illuminate the theory and practice of the field in a human friendly manner. After all, there is no need to invent the future... if you can predict it.

In this class you will:

• Learn the difference between Recommenders, Classifiers, and Clustering.
• Understand how to pick the correct algorithm for your analysis.
• Examine a case study showing how to decompose a problem to train an algorithm.
• Learn Validation methods to test whether your algorithm is effective.
• Compare Supervised vs. Unsupervised learning and their tradeoffs.
• Meet other machine learning enthusiasts and learn how to participate in the active artificial intelligence community.

This class is the third of a six part series providing a survey of each aspect of data science.

• May 28: Introduction to Data Science
• June 4: Introduction to Statistics: Finding the Signal in the Noise
• June 11: Algorithms for the Rest of Us: A Gentle introduction to Machine Learning
• June 18: Data Wrangling 101: Looking for Data, and What to Do When You've Found it
• June 25: Computing at Scale: An Introduction to Secrets of Managing Big Data
• July 2: Introduction to Visualization: Learn How to Effectively Tell your Data Story

Interested in attending but live elsewhere? Have a scheduling conflict? Due to popular demand we are now offering this class remotely! Buy a "Virtual" ticket and gain access to an online discussion board where the instructors answer all your questions, receive a video of the lecture, and get all the class materials.

Prerequisites

A childlike curiosity.

About Your Teachers

Jonathan and Ryan are the founders of Zipfian Academy, a school which trains the next generation of Data Scientists.

Jonathan first discovered his love of all things data while studying Computer Science and Physics at UC Berkeley. In a former life, he worked for Alpine Data Labs developing distributed machine learning algorithms for predictive analytics on Hadoop.

Jonathan has always had a passion for sharing the things he has learned in the most creative ways he can. He has been a mentor at Dev Bootcamp, taught classes at General Assembly, and is an instructor at Hack Reactor where he gets to combine his two favorite things: humans and code.

Ryan is fascinated by data in all its forms, binary and biological. Trained in Genetics and Genomics at UC Berkeley, he plumbed the depths of plant immunity at the Plant Gene Expression Center, building big data applications targeting next-generation sequencing technologies.

As a Sr. Systems Engineer at Nutanix, he worked on scale-out distributed computing solutions for virtualized environments. When not trying to pull beauty out of data, you can find Ryan hacking on 3D printers, DIY Bio, and open-source hardware.