Python: Beginner's Guide to Artificial Intelligence

Key Features

Gain real-world contextualization using deep learning problems concerning research and application
Get to know the best practices to improve and optimize your machine learning systems and algorithms
Design and implement machine intelligence using real-world AI-based examples

What You Will Learn

Use adaptive thinking to solve real-life AI case studies
Rise beyond being a modern-day factory code worker
Understand future AI solutions and adapt quickly to them
Master deep neural network implementation using TensorFlow
Predict continuous target outcomes using regression analysis
Dive deep into textual and social media data using sentiment analysis

About

This Learning Path offers practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis. You will be introduced to various machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. You will learn to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open-source Python libraries.

Throughout the Learning Path, you’ll learn how to develop deep learning applications for machine learning systems. Discover how to attain deep learning programming on GPU in a distributed way.

By the end of this Learning Path, you know the fundamentals of AI and have worked through a number of case studies that will help you apply your skills to real-world projects.

This Learning Path includes content from the following Packt products:

• Artificial Intelligence By Example by Denis Rothman

• Python Deep Learning Projects by Matthew Lamons, Rahul Kumar, and Abhishek Nagaraja

• Hands-On Artificial Intelligence with TensorFlow by Amir Ziai, Ankit Dixit