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Data Science - Supervised Machine Learning in Python
Data Science - Supervised Machine Learning in Python

Data Science - Supervised Machine Learning in Python: Master Supervised Machine Learning Techniques in Python with KNN, Naive Bayes, and Decision Trees

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Profile Icon The Lazy Programmer
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Can$166.99
Video Apr 2026 3hrs 51mins 1st Edition
Video
Can$166.99
Video + Subscription
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Arrow left icon
Profile Icon The Lazy Programmer
Arrow right icon
Can$166.99
Video Apr 2026 3hrs 51mins 1st Edition
Video
Can$166.99
Video + Subscription
Free Trial
Video
Can$166.99
Video + Subscription
Free Trial

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Key benefits

  • Implementation of popular machine learning algorithms (KNN, Naive Bayes, Decision Trees)
  • Hands-on coding exercises using real datasets like MNIST
  • Learn model evaluation techniques, including cross-validation and hyperparameter tuning

Description

This comprehensive course will guide you through the core techniques of supervised machine learning using Python. You will begin with an introduction to essential concepts and then dive into hands-on coding exercises, where you’ll implement key algorithms such as K-Nearest Neighbor (KNN), Naive Bayes, and Decision Trees. Working with real datasets like MNIST, you'll gain practical experience in solving problems and understanding how these algorithms perform in the real world. As you progress, you'll develop practical skills in hyperparameter tuning, cross-validation, and feature extraction, which are critical for optimizing machine learning models. You'll explore advanced topics like Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), and non-Naive Bayes methods, adding depth to your knowledge of more complex machine learning techniques. The course culminates in learning how to deploy machine learning models as web services, providing you with a complete understanding of machine learning application in real-world. Targeted at aspiring data scientists and machine learning engineers, this course combines theory and practical exercises to ensure you build strong, deployable machine learning models. While no prior machine learning experience is required, familiarity with Python programming is recommended.

Who is this book for?

This course is ideal for aspiring data scientists and machine learning engineers who wish to deepen their understanding of supervised machine learning algorithms. It is also perfect for developers looking to transition into the field of data science and those familiar with Python programming who want to enhance their machine learning skills.

What you will learn

  • Understand and implement K-Nearest Neighbor (KNN) algorithm
  • Master Naive Bayes for both continuous and discrete data
  • Build and optimize decision trees for classification
  • Apply advanced techniques like LDA, QDA, and non-Naive Bayes models
  • Deploy machine learning models as web services
  • Gain practical experience in model evaluation and improvement

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Last updated date : Jun 30, 2026
Publication date : Apr 20, 2026
Length: 3hrs 51mins
Edition : 1st
Language : English
ISBN-13 : 9781807785796
Category :
Concepts :

What do you get with a Packt Premium Subscription?

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Product feature icon Weekly additions on emerging tech and exclusive early access to books as they are being written.
Product feature icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Product feature icon A monthly credit and 50% off any future digital purchases.

Product Details

Last updated date : Jun 30, 2026
Publication date : Apr 20, 2026
Length: 3hrs 51mins
Edition : 1st
Language : English
ISBN-13 : 9781807785796
Category :
Concepts :

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Table of Contents

8 Chapters
Introduction and Review Chevron down icon Chevron up icon
K-Nearest Neighbor Chevron down icon Chevron up icon
Naive Bayes and Bayes Classifiers Chevron down icon Chevron up icon
Decision Trees Chevron down icon Chevron up icon
Perceptrons Chevron down icon Chevron up icon
Practical Machine Learning Chevron down icon Chevron up icon
Building a Machine Learning Web Service Chevron down icon Chevron up icon
Conclusion Chevron down icon Chevron up icon
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