Python Machine Learning Cookbook -: Over 100 recipes on neural networks, artificial intelligence, and machine learning techniques

Recipe-based guide to take you from smart data analytics to deep learning using real-world data sets About 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 implement Who This Book Is For This 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 Mobilenet In Detail This 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.

About the Author :
Srikanth Vidapanakal is an Artificial Intelligence practitioner and a Data Scientist. He has over 17 years of experience in range of technologies such as System software, Virtualization, Cloud Computing, Big Data, Data Science and Deep Learning. He is a Principal Data Scientist and part of Analytics – COE at Happiest Minds. He is working on building Predictive Models in various domains such as E-commerce, Retail, EduTech, Insurance. He has worked on building Analytic Solutions on Web Analytics, Marketing Analytics, Tv Ad Campaign Analytics in E-commerce and Retail domains. He has worked on several Machine Learning Algorithms and worked on Deep Learning techniques applied to Computer Vision and NLP. He also has strong interest in applying Deep Learning, Computer Vision, Control, Path planning to Self-Driving cars, Driver Assistance systems and connected car platform. Srikanth completed Executive Business Analytics Program from Indian School of Business, Hyderabad and BE Computer Science from Vijayanagar Engineering College, Bellary, Gulbarga University. Prateek Joshi is an Artificial Intelligence researcher, a published author of 8 books, and a TEDx speaker. He has been featured on Forbes 30 Under 30, CNBC, TechCrunch, Silicon Valley Business Journal, and many more publications. He is the founder of Pluto AI, a venture-funded Silicon Valley startup building an intelligence platform for water facilities. His work in this field has led to patents, demos, and research papers at major IEEE conferences. He has been an invited speaker at technology and entrepreneurship conferences including TEDx, Global Big Data Conference, Machine Learning Developers Conference, and Sensors Expo. His tech blog (prateekjoshi) has received 1.6M+ page views from 200+ countries and has 7,400+ followers. He frequently writes on topics such as Artificial Intelligence, Python programming, and abstract mathematics. He is an avid coder and has won many hackathon prizes, including at Facebook. He graduated from University of Southern California with a Masters degree specializing in Artificial Intelligence. He has previously worked at Nvidia and Microsoft Research.