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Python Machine Learning Crash Course for Beginners
Python Machine Learning Crash Course for Beginners

Python Machine Learning Crash Course for Beginners: A complete machine learning step-by-step course, mastering regression, classification and clustering

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Profile Icon AI Sciences
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$237.99
Video Apr 2021 9hrs 27mins 1st Edition
Video
$237.99
Video + Subscription
$24.99 Monthly
Arrow left icon
Profile Icon AI Sciences
Arrow right icon
$237.99
Video Apr 2021 9hrs 27mins 1st Edition
Video
$237.99
Video + Subscription
$24.99 Monthly
Video
$237.99
Video + Subscription
$24.99 Monthly

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Key benefits

  • Build a face recognition application from scratch
  • Prepare and train your data for your projects
  • Learn real-world applications of your algorithms

Description

Machine learning is a field of computer science through which you can create complex models that perform multiple functions using mathematical input. Python is a popular choice to create machine learning models due to a plethora of libraries easily accessible. This course takes you through this impressive combination of Python and machine learning, teaching you the basics of machine learning to create your own projects. You’ll begin learning about different types of machine learning models and how to choose the relevant ones for your project. You’ll learn to optimize this model and apply performance metrics to track its performance. You’ll also learn topics like regression, classification, and clustering to improve the performance of your model. You’ll learn the basics of neural networks and use scikit-learn to perform calculations in your project. By the end of this course, you’ll have created a face recognition application using everything you’ve learned in this course. The code bundle for this course is available at https://github.com/PacktPublishing/Python-Machine-Learning-Crash-Course-for-Beginners

Who is this book for?

This course is for Python developers who are new in the field of machine learning. No prior knowledge or experience of machine learning is required. A basic understanding of Python programming will be needed here.

What you will learn

  • Train different types of machine learning models for your project
  • Prepare and clean data for your project
  • Optimize your machine learning model to best suit your project needs
  • Build your own machine learning model from scratch
  • Apply performance metrics to track the performance of your model
  • Use scikit-learn to perform calculations in your project

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Last updated date : Jul 03, 2026
Publication date : Apr 23, 2021
Length: 9hrs 27mins
Edition : 1st
Language : English
ISBN-13 : 9781801077453
Category :
Languages :

What do you get with a Packt Premium Subscription?

Subscribe today for full access to all titles. After checkout, you’ll receive an eBook credit that you can manually redeem on any title — including this one.
Product feature icon Access to the largest independent learning library in tech. Access this title and thousands more!
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 : Jul 03, 2026
Publication date : Apr 23, 2021
Length: 9hrs 27mins
Edition : 1st
Language : English
ISBN-13 : 9781801077453
Category :
Languages :

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Table of Contents

13 Chapters
Introduction to the Course Chevron down icon Chevron up icon
Why Machine Learning Chevron down icon Chevron up icon
Process of Learning from Data Chevron down icon Chevron up icon
Machine Learning Models Chevron down icon Chevron up icon
Data Preparation and Preprocessing Chevron down icon Chevron up icon
Machine Learning Models and Optimization Chevron down icon Chevron up icon
Building a Machine Learning Model from Scratch Chevron down icon Chevron up icon
Overfitting, Underfitting, and Generalization Chevron down icon Chevron up icon
Machine Learning Model Performance Metrics Chevron down icon Chevron up icon
Dimensionality Reduction Chevron down icon Chevron up icon
Deep Learning Overview Chevron down icon Chevron up icon
Hands-On Machine Learning Project Using Scikit-Learn Chevron down icon Chevron up icon
Mathematics Wrap-Up Chevron down icon Chevron up icon
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