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Machine Learning with Python Cover

Machine Learning with Python

Unlocking AI Potential with Python and Machine Learning

Publisher:Packt Publishing Limited
By:   
Paid access
|Sep 2024
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Description

Unlock the secrets of data science and machine learning with our comprehensive Python course, designed to take you from basics to complex algorithms effortlessly

Key Features

  • Navigate through Python's machine learning libraries effectively
  • Learn exploratory data analysis and data scrubbing techniques
  • Design and evaluate machine learning models with precision

Book Description

The course starts by setting the foundation with an introduction to machine learning, Python, and essential libraries, ensuring you grasp the basics before diving deeper. It then progresses through exploratory data analysis, data scrubbing, and pre-model algorithms, equipping you with the skills to understand and prepare your data for modeling.

The journey continues with detailed walkthroughs on creating, evaluating, and optimizing machine learning models, covering key algorithms such as linear and logistic regression, support vector machines, k-nearest neighbors, and tree-based methods. Each section is designed to build upon the previous, reinforcing learning and application of concepts.

Wrapping up, the course introduces the next steps, including an introduction to Python for newcomers, ensuring a comprehensive understanding of machine learning applications.

What you will learn

  • Analyze datasets for insights
  • Scrub data for model readiness
  • Understand key ML algorithms
  • Design and validate models
  • Apply Linear and Logistic Regression
  • Utilize K-Nearest Neighbors and SVMs

Who this book is for

This course is ideal for aspiring data scientists and professionals looking to integrate machine learning into their workflows. A basic understanding of Python and statistics is beneficial.

Table of Contents

  1. FOREWORD
  2. DATASETS USED IN THIS BOOK
  3. INTRODUCTION
  4. DEVELOPMENT ENVIRONMENT
  5. MACHINE LEARNING LIBRARIES
  6. EXPLORATORY DATA ANALYSIS
  7. DATA SCRUBBING
  8. PRE-MODEL ALGORITHMS
  9. SPLIT VALIDATION
  10. MODEL DESIGN
  11. LINEAR REGRESSION
  12. LOGISTIC REGRESSION
  13. SUPPORT VECTOR MACHINES
  14. K-NEAREST NEIGHBORS
  15. TREE-BASED METHODS
  16. NEXT STEPS
  17. APPENDIX 1: INTRODUCTION TO PYTHON
  18. APPENDIX 2: PRINT COLUMNS
PDF ISBN: 978-1-83546-207-2
Publisher: Packt Publishing Limited
Copyright owner: © 2024 Packt Publishing Limited
Publication date: 2024
Language: English
Pages: 146
Related subjects:
Computer sciences,
Computer sciences, other
BISAC subjects:
COM051360,
COM004000,
COM051010
Thema subjects:
UMX,
UYQ

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