Python Machine Learning: A Beginner’s Guide to Python Programming for Machine Learning

Length: 125 pages
Edition: 1
Language: English
Publication Date: 2021-10-22
ISBN-10: B09K57T46L
Sales Rank: #0 (See Top 100 Books)

Python Machine Learning

A Beginner’s Guide to Python Programming for Machine Learning

Learn the essential tools every beginner should know about Python. Get the methods that will help you complete your projects successfully like the pros. This is the book every aspiring programmer needs to have. Learn how to try fresh ideas and learn problem-solving, improve your programming skills, but above all, boost your confidence. Imagination and creativity will open the door to new projects you never thought possible.

Here’s what you will love about this book:

What is Python Machine Learning, anyway? Here’s how to get started.
Find out the “Whys” and “Hows” of Python
The One Proven Way for Effective Implementation of Machine Learning Algorithms
Find Out the EASIEST Way for Mastering Machine Learning with Python.
Learn Importance of Learning Data Analysis in Python.
The truth about Deep Learning vs Machine Learning
The Secret to Machine Learning with Scikit-Learn
Discover Deep Learning with TensorFlow.
The Essential Key Tips & Tricks for Deep Learning with PyTorch and Keras.
Find out The Role of Machine Learning in the Internet of Things (IoT)
Looking to the Future with Machine Learning. The Business Angle.
A beginners’ friendly book with easy-to-follow tips.
And much more, this is truly a must-have guide!

Introduction
Chapter 1: Machine Learning: A Brief History
Donald Hebb - The Organization of Behavior
Samuel Arthur - Neural Networks, Checkers and Rote Learning
Rosenblatt’s Perceptron
Marcello Pelillo - The Nearest Neighbor Algorithm
Perceptrons and Multilayers
Going Separate Ways
Robert Schapire - The Strength of Weak Learnability
Advancing into Speech and Facial Recognition
Present Day Machine Learning
Chapter 2: Fundamentals of Python for Machine Learning
What is Python?
Why Python?
Other Programming Languages
Effective Implementation of Machine Learning Algorithms
Mastering Machine Learning with Python
Chapter 3: Data Analysis in Python
Importance of Learning Data Analysis in Python
Building Predictive Models in Python
Python Data Structures
Python Libraries for Data Analysis
Chapter 4: Comparing Deep Learning and Machine Learning
Deep Learning vs Machine Learning
Problem Solving Approaches
Different Use Cases
Chapter 5: Machine Learning with Scikit-Learn
Representing Data in Scikit-Learn
Features Matrix
Target Arrays
Estimator API
Supervised Learning in Scikit-Learn
Unsupervised Learning in Scikit-Learn
Chapter 6: Deep Learning with TensorFlow
Brief History of TensorFlow
The TensorFlow Platform
TensorFlow Environments
TensorFlow Components
Algorithm Support
Creating TensorFlow Pipelines
Chapter 7: Deep Learning with PyTorch and Keras
PyTorch Model Structures
Initializing PyTorch Model Parameters
Principles Supporting Keras
Getting Started
Keras Preferences
Keras Functional API
Chapter 8: Role of Machine Learning in the Internet of Things (IoT)
Fusing Machine Learning and IoT
Machine Learning Challenges in IoT
Chapter 9: Looking to the Future with Machine Learning
The Business Angle
AI in the Future
Conclusion