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About this book is a comprehensive guide towards step into the amazing world of intelligent apps using the world of Artificial Intelligence, this book will help you create your own applications work through simple and comprehensive examples that will get you up and helps you with Artificial Intelligence .
This book is for developers particularly coding in python language who want to build real-world Artificial Intelligence applications. Being familiar with Python would be very useful to play around with the codes. It will also be very useful for experienced Python programmers who are looking to use AI techniques in their existing technological stacks. You will learn about different regression techniques, classification and understand the core concept of clustering and how to use it to automatically segment data. You will learn how to build an intelligent recommender system, Understand logic behind its programming and how to implement it in building automatic speech recognition systems. Understand the basics of heuristic search and genetic programming Develop games using AI, Learn working of reinforcement learning, discover how to build intelligent applications centered on, text, images and time series data see how to build applications based on deep learning algorithms In detail. 
Move on to designing AI solutions in a simple manner rather than get confused by complex techniques and architecture with the help of this amazing book on Artificial intelligence with the help of easy to understand examples. This comprehensive guide on AI will be a starter kit for you to develop applications based on AI and Machine Learning.
Understood the fundamentals of AI and worked through a number of case studies that will help you develop your business vision with this book. Artificial Intelligence is becoming increasingly relevant and has various applications in the modern world. By harnessing the power of algorithms, you can create apps which interact with the world around you based on the input feed; you can build intelligent recommender systems, automatic speech recognition systems and much more. Starting with basics of Artificial Intelligence you'll move on to learn how to develop building blocks using data mining techniques. Learn how to make informed decisions, and how to apply them to real-world scenarios. This book covers nearly all range of topics including predictive analytics and deep learning with suitable the examples. What you will learn • Use adaptive thinking and modern knowledge to solve real-life AI case studies and problems • Acquire advanced AI, deep learning and machine learning designing skills • Learn about quantum computing, cognitive NLP chatbots, IoT and blockchain technology • Understand future AI solutions and develop skills to adapt them quickly • Develop out-of-the-box thinking to face any challenge related to present market senerios 
Hands-On Machine Learning with Scikit-Learn and TensorFlow is a series of Deep Learning breakthroughs that have boosted the field of Artificial Intelligence and machine learning over the last few decades. Now that machine learning is flroushing, even programmers who know close to nothing about this modern computer technology can use simple, efficient tools and existing language knowledge to implement programs capable of learning from existing data.
This practical book shows you how Develop application on Artificial Intelligence using different approaches and examples, with minimal theory, and two production-ready Python frameworks— TensorFlow and scikit-learn —author Sir Aurélien Géron develops an intuitive understanding of the concepts and tools for building intelligent systems and applications. You’ll learn a range of new and different techniques, starting with simple linear progressing and regression to deep neural networks. With exercises and examples in each chapter to help you apply what you’ve learned, all you need is programming experience in any language to get started. What you will learn • Use scikit-learn to track an example of end-to-end project in machine-learning • Explore several training models, including random forests, support vector machines, ensemble methods and decision trees • Explore different machine learning landscape, particularly neural nets • build and train neural nets from TensorFlow library • Dive into, convolution nets including neural net architectures, recurrent nets, and deep reinforcement learning • Learn techniques for scaling and training deep neural nets • Apply practical code examples without acquiring excessive machine learning/Artificial Intelligence theory or algorithm in details
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