Applied Machine Learning: Algorithms

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Created by: Derek Jedamski

Produced in 2019

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Course Description

In the first installment of the Applied Machine Learning series, instructor Derek Jedamski covered foundational concepts, providing you with a general recipe to follow to attack any machine learning problem in a pragmatic, thorough manner. In this course - the second and final installment in the series - Derek builds on top of that architecture by exploring a variety of algorithms, from logistic regression to gradient boosting, and showing how to set a structure that guides you through picking the best one for the problem at hand. Each algorithm has its pros and cons, making each one the preferred choice for certain types of problems. Understanding what actually drives each algorithm, as well as their benefits and drawbacks, can give you a significant competitive advantage as a data scientist.

Beginner Level

Time: 2h 24m

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.