Intermediate Machine Learning

Handle missing values, non-numeric values, data leakage, and more.

Review what you need for this course.
Missing values happen. Be prepared for this common challenge in real datasets.
There's a lot of non-numeric data out there. Here's how to use it for machine learning.
A critical skill for deploying (and even testing) complex models with pre-processing.
A better way to test your models.
The most accurate modeling technique for structured data.
Find and fix this problem that ruins your model in subtle ways.

Alexis Cook

Reviews

5.0 rating, based on 2 Class Central reviews

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Mohammadreza

This course stands out for its clear explanations of concepts and its structured, step-by-step practical exercises, enabling you to strengthen your foundational knowledge and enhance your coding skills effectively.
Victor Mutai

The course provided a comprehensive introduction to machine learning, covering both foundational concepts and advanced topics. Starting with the basics of supervised and unsupervised learning, it laid a solid groundwork in understanding algorithms like linear regression, decision trees, and clustering techniques. As I progressed, the course delved into more complex areas like neural networks and deep learning, which were particularly intriguing.