Artificial Intelligence and Machine Learning Fundamentals : Develop Real-World Applications Powered by the Latest AI Advances

Create AI applications in Python and lay the foundations for your career in data science

Key Features

• Practical examples that explain key machine learning algorithms
• Explore neural networks in detail with interesting examples
• Master core AI concepts with engaging activities

Book Description

Machine learning and neural networks are pillars on which you can build intelligent applications. Artificial Intelligence and Machine Learning Fundamentals begins by introducing you to Python and discussing AI search algorithms. You will cover in-depth mathematical topics, such as regression and classification, illustrated by Python examples.

As you make your way through the book, you will progress to advanced AI techniques and concepts, and work on real-life datasets to form decision trees and clusters. You will be introduced to neural networks, a powerful tool based on Moore's law.

By the end of this book, you will be confident when it comes to building your own AI applications with your newly acquired skills!

What you will learn

• Understand the importance, principles, and fields of AI
• Implement basic artificial intelligence concepts with Python
• Apply regression and classification concepts to real-world problems
• Perform predictive analysis using decision trees and random forests
• Carry out clustering using the k-means and mean shift algorithms
• Understand the fundamentals of deep learning via practical examples

Who this book is for

Artificial Intelligence and Machine Learning Fundamentals is for software developers and data scientists who want to enrich their projects with machine learning. You do not need any prior experience in AI. However, it's recommended that you have knowledge of high school-level mathematics and at least one programming language (preferably Python).

Table of Contents

1. Principles of Artificial Intelligence
2. AI with Search Techniques and Games
3. Regression
4. Classification
5. Using Trees for Predictive Analysis
6. Clustering
7. Deep Learning with Neural Networks