Python Machine Learning Cookbook - : Over 100 recipes on neural networks, artificial intelligence, and machine learning techniques (2 Revised edition)

Recipe-based guide to take you from smart data analytics to deep learning using real-world data setsAbout This Book* Understand the ML algorithms to use in variety of real-life scenarios *Cover tasks such as recommendation engine, NLP, neural networks, GANs, and more *An ideal recipe-based book with easy to follow code solutions to implementWho This Book Is ForThis book is for data scientists, machine learning developers, deep learning practitioners and Python programmers who are looking to bring machine-learning algorithms to best use to create real-world applications using the best-integrated libraries from Python ecosystem.

If you are stuck at work and want ready-to-use code solutions to cover key tasks in machine learning and deep learning domain, then this book is what you need.

Familiarity with Python programming and machine learning concepts will certainly be useful to play around with the code. What You Will Learn* Use predictive modeling and apply it to real-world problems*Use grid search to find the optimal set of hyperparameters*Implement supervised and unsupervised learning algorithms in ML*Learn how to apply Deep Neural Networks to Computer Vision problems*Learn the concept of perceptrons and how to build neural networks *Analyze stock market data using Conditional Random Fields*Reinforcement learning paradigm to understand the working of AI agents*Develop Hand gesture recognition Mobile application using MobilenetIn DetailThis book is an update to our successful title "Python Machine Learning Cookbook" with a fresh approach to deal with real-world machine learning and deep learning tasks.

We will learn to build powerful machine learning applications using the best libraries support from Python ecosystem.

We will have new fresh content covering trending areas in the world of machine learning such as reinforcement learning, GANs, capsule networks and more with a wide range of libraries such as TensorFlow, Keras, Pytorch, Caffe and more.

You will learn to perform various machine learning scenarios using real-world datasets, thus addressing key building blocks of ML.

Also you will implement various machine learning algorithms to cover examples such as classification, clustering, recommendation engines, NLP, Autoencoders and more using the recipe-based approach.

You will learn to apply supervised and unsupervised learning techniques to real-world problems.

Later, you will learn the concept of hyperparameters and how they affect the performance of support vector machines.

Then you will delve into how perceptrons work and how they are used to build advanced neural networks. By the end of this book, you will be all ready to solve common challenges and key tasks in machine learning and deep learning domain using ready code solutions.