Applied Machine Learning: Foundations

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Created by: Derek Jedamski

Produced in 2019

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Course Description

Anyone who can write basic Python is capable of fitting a simple machine learning model on a clean dataset. The competitive edge comes in the ability to customize and optimize those models for specific problems. The workflow used to build effective machine learning models and the methods used to optimize those models are typically not algorithm or problem specific. In this course, the first installment in the two-part Applied Machine Learning series, instructor Derek Jedamski digs into the foundations of machine learning, from exploratory data analysis to evaluating a model to ensure it generalizes to unseen examples. Instead of zeroing in on any specific machine learning algorithm, Derek focuses on giving you the tools to efficiently solve nearly any kind of machine learning problem.

Beginner Level

Time: 2h 38m

Instructor Details

Derek Jedamski

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Derek Jedamski is a skilled data scientist specializing in machine learning.

Derek has experience with regression and classification modeling, natural language processing, statistical analysis, quality control, business analytics, and communicating technical results to audiences with various backgrounds. He also has a thorough understanding of Python, R, SQL, Apache Spark, and other computing frameworks and languages. Currently, Derek works at GitHub as a data scientist.