SDS 446: Getting Started in Machine Learning — with Jon Krohn

Learning Paths

Jon Krohn offers a structured approach to learning machine learning, emphasizing the importance of choosing the right learning path. He suggests exploring the four learning tracks available on SuperDataScience.com, which cater to different roles such as data analyst, data scientist, AI engineer, and data science manager. These paths are designed to guide learners from novice to expert levels, providing a comprehensive understanding of machine learning concepts.

You can create a free account on superdatascience.com and check out the four specific learning paths we have there, roughly ranked from novice to expert in machine learning.

For those seeking a standalone course, Jon recommends the "Machine Learning A to Z" course on Udemy, which offers extensive content at an affordable price 1.

   

Foundational Knowledge

Acquiring foundational knowledge is crucial for mastering machine learning, according to Jon Krohn. He highlights the importance of understanding subjects like linear algebra, calculus, and probability, which are essential for grasping advanced machine learning concepts. Jon's own "Machine Learning Foundations" course is recommended for those willing to invest time in these areas, offering a comprehensive introduction to the necessary prerequisites.

Once you have a firm grip on all the prerequisite linear algebra, calculus, and probability theory, a classic book to completely master the advanced theory of machine learning is a book called deep learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.

Additionally, Jon maintains a wealth of free resources on his personal website, providing learners with access to datasets, blog posts, and videos to further their understanding of machine learning 1.