Machine Learning With Python Programming : 2023 A Beginners Guide

✍ Scribed by James Harrison

• Publisher: Orchid Publishing
• Year: 2024
• Tongue: English
• Leaves: 431
• Category: Library

✦ Synopsis

Are you ready to dive into the fascinating world of Machine Learning and Artificial Intelligence? Do you want to understand the technology that powers everything from personalized recommendations to self-driving cars? If so, "Machine Learning With Python Programming : 2023 A Beginners Guide" is the book you've been waiting for.

This comprehensive guide takes you on an exciting journey from the basics of Python programming to the depths of neural networks and deep learning. It demystifies the complex world of machine learning, making it accessible and understandable, regardless of your background.

James begins by discussing machine learning and what it can do; introducing key mathematical and computational topics in an approachable manner; and walking you through the first steps in building, training, and evaluating learning systems. Step by step, you'll fill out the components of a practical learning system, broaden your toolbox, and explore some of the field's most sophisticated and exciting techniques. Whether you're a student, analyst, scientist, or hobbyist, this guide's insights will be applicable to every learning system you ever build or use.

Understand machine learning algorithms, models, and core machine learning concepts
Classify examples with classifiers, and quantify examples with regressors
Realistically assess performance of machine learning systems
Use feature engineering to smooth rough data into useful forms
Chain multiple components into one system and tune its performance
Apply machine learning techniques to images and text
Connect the core concepts to neural networks and graphical models
Leverage the Python scikit-learn library and other powerful tools
And much more!

✦ Table of Contents

Machine Learning With Python Programming : 2023 A Beginners Guide

Chapter 1 Overview of Artificial Intelligence.............................16

Chapter 2 Python Machine Learning Ecosystem.........................45

Chapter 3 A Quick Course on SciPy and Python.........................15

Chapter 4 How to Import Data for Machine Learning..................27

Chapter 5 Use Descriptive Statistics to Gain Understanding of Your Data 31

Chapter 6 Understand Your Data With Visualization...................38

Chapter 7 Get Ready for Machine Learning with Your Data...........47

Chapter 8 Choosing Features for Machine Learning....................52

Chapter 9 Analyze Machine Learning Algorithms' Performance Using Resampling 57

Chapter 10 Performance Measures for Algorithms in Machine Learning 64

Chapter 11 Spot-Check Classification Algorithms.......................70

Chapter 12 Algorithms for Spot-Check Regression.....................76

Chapter 13 Compare Machine Learning Algorithms....................84

Chapter 14 Use Pipelines to Automate Machine Learning Workflows87

Chapter 15 Boost Performance in Group Settings......................91

Chapter 16 Boost Efficiency via Algorithm Adjustment................98

Chapter 17 Store and Import Deep Learning Models..................101

Chapter 18 Template for Predictive Modeling Projects...............105

Chapter 19 Your First Machine Learning Project in Python Step-By-Step 111

Chapter 20 Regression Machine Learning Case Study Project......124

Chapter 21 Binary Classification Machine Learning Case Study Project 144

Chapter 22 More Predictive Modeling Projects.........................165

INTRODUCTION

Chapter 1 Overview of Artificial Intelligence

Chapter 2 Python Machine Learning Ecosystem

Chapter 3 A Quick Course on SciPy and Python

Chapter 4 How to Import Data for Machine Learning

Chapter 5 Use Descriptive Statistics to Gain Understanding of Your Data

Chapter 6 Understand Your Data With Visualization

Chapter 7 Get Ready for Machine Learning with Your Data

Chapter 8 Choosing Features for Machine Learning

Chapter 9 Analyze Machine Learning Algorithms' Performance Using Resampling

Chapter 10 Performance Measures for Algorithms in Machine Learning

Chapter 11 Spot-Check Classification Algorithms

Chapter 12 Algorithms for Spot-Check Regression

Chapter 13 Compare Machine Learning Algorithms

Chapter 14 Use Pipelines to Automate Machine Learning Workflows

Chapter 15 Boost Performance in Group Settings

Chapter 16 Boost Efficiency via Algorithm Adjustment

Chapter 17 Store and Import Deep Learning Models

Chapter 18 Template for Predictive Modeling Projects

Chapter 19 Your First Machine Learning Project in Python Step-By-Step

Chapter 20 Regression Machine Learning Case Study Project

Chapter 21 Binary Classification Machine Learning Case Study Project

Chapter 22 More Predictive Modeling Projects