Resolve and offer solutions to your machine learning problemsAbout This BookThis book will show you how to improve the efficiency of your systemsImprove predictions and recommendations to have better levels of accuracyOptimize performance of your Machine learning systemsWho This Book Is ForThis book is for statisticians, analysts, and competent data scientists with knowledge of big data and ML, who need help dealing with problematic scenarios in ML and improving system performance and accuracy. A good background in mathematics is assumed. Working knowledge of R and Python is expected.What you will learnGet equipped with a deeper understanding of how to apply machine learning techniquesImplement each of the advanced machine learning techniquesSolve real-life problems that are encountered in order to make your applications produce improved resultsGain hands-on experience of problem solving for your Machine learning systemsUnderstand the methods of collecting data, preparing data for usage, training the model, evaluating the model's performance, and improving the model's performanceIn DetailMachine learning (ML) became the new black and is in constant demand by many organizations who work with huge amounts of data all the time. The complexity of finding, understanding, and predicting outcomes makes ML very difficult. This cookbook will solve the everyday difficulties and struggles you face as a data scientist. As a concept, machine learning has a single goal for data scientists to achieve—predictive analytics, but to reach that goal, data scientists have to be prepared for all types of data, no matter how good or bad. This is the focus of the book.As you already know about machine learning techniques with specific languages or tools, we'll show you how to improve the efficiency of your systems so your predictions and recommendations have better levels of accuracy and better performance.The first half of the book provides recipes on a fairly complex machine learning systems where you'll learn to improve its efficiency. That includes recipes on: classifications, neural networks, unsupervised and supervised learning, deep learning, reinforcement learning, and more.The second half of the book focuses on three different ML case studies all based on real-world data and offer solutions and solve specifics ML issues in each one.