Data Science: Supervised Machine Learning in Python

Full Guide to Implementing Classic Machine Learning Algorithms in Python and with Scikit-Learn

4.76 (3535 reviews)

Udemy

platform

English

language

Data Science

What you will learn

Understand and implement K-Nearest Neighbors in Python

Understand the limitations of KNN

User KNN to solve several binary and multiclass classification problems

Understand and implement Naive Bayes and General Bayes Classifiers in Python

Understand the limitations of Bayes Classifiers

Understand and implement a Decision Tree in Python

Understand and implement the Perceptron in Python

Understand the limitations of the Perceptron

Understand hyperparameters and how to apply cross-validation

Understand the concepts of feature extraction and feature selection

Understand the pros and cons between classic machine learning methods and deep learning

Use Sci-Kit Learn

Implement a machine learning web service

Course Gallery

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Rating & Reviews

Enrollment Distribution

Comidoc Review

Our Verdict

This course excels in providing hands-on experience with classic machine learning algorithms while incorporating essential theory and intuition. However, some students may find content lacking or certain topics moving too quickly without properly explaining statistical concepts. As such, this course best serves those with prior knowledge looking to advance their understanding of these Python-based models.

What We Liked

Covers the implementation of several classic machine learning algorithms in Python and with Scikit-Learn
Clear explanations of theory related to K-Nearest Neighbors, Naive Bayes, Decision Trees, and Perceptron
Code implementations made readily available and approachable for learners
Focus on understanding the intuition behind these algorithms instead of just coding them

Potential Drawbacks

Some reviewers mention feeling that some content is missing or not covered in sufficient depth, such as regression cases for Decision Trees or tree pruning
Concerns about pacing and assuming prior statistical knowledge
Implementations of algorithms mentioned to be slower than alternative solutions
Minimal coverage on building a machine learning web service compared to expectations