06.09.2023

This course gives an introduction to ML algorithms. It is great to get an idea about algorithms, but not to learn in detail. One of the main drawbacks for me is that this course has no lecture notes.

Learn the intuition behind the most popular ML algorithms, understand the pros and cons of each, and choose the best one for the problems you need to solve.

5 hours 190 lessons

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190 High Quality Lessons

0 Practical Tasks

5 Hours of Content

Certificate of Achievement

Looking to break into machine learning? Need to review the ins and outs of each algorithm? Preparing for an interview? Curious to see how these algorithms are applied to business? As ML practitioners, the true value of ML is not in memorizing complicated formulas. It’s not using the latest, greatest deep learning architecture. It’s knowing when to use an algorithm and how to maximize the impact of that model. It’s knowing how to use them to solve REAL business problems. The true value of ML is not ML. It’s solving important business problems. Whether you need to build a forecasting model that forms the backbone of the ads business, builld a recommender system powering millions of purchases or build a fraud detection system that catches bad apples, this course will arm you with both the ML knowledge AND the know-how on how to apply it to your business problem. We don’t want you to leave this course just knowing ML. We want you to leave this course to leave as ML practitioners.

You will learn the ins and outs of each algorithm and we’ll walk you through examples of the world’s biggest tech companies using these algorithms to apply to their problems. This course touches on several key aspects a practitioner needs in order to be able to aply ML to business problems:

ML Algorithms intuition

The pros and cons of different ML algorithms

When to use and when not to use ML

How the ML process works for every algorithm

Assumptions behind ML algorithms

Real-world examples on situations when specific types of ML algorithms can be used

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“Most courses only explain the concept itself, and they’re done. Not only do we teach you the concept, but we also lay out its pros and cons and when you should use the concept to solidify your intuition. ”

Works at Large music streaming platform

The Machine Learning Algorithms A-Z

with Jeff Li and Ken Jee