Resolving and offering solutions to your machine learning problems with RAbout This BookImplement a wide range of algorithms and techniques for tackling complex dataImprove predictions and recommendations to have better levels of accuracyOptimize performance of your machine-learning systemsWho This Book Is ForThis book is for analysts, statisticians, and data scientists with knowledge of fundamentals of machine learning and statistics, who need help in dealing with challenging scenarios faced every day of working in the field of machine learning and improving system performance and accuracy. It is assumed that as a reader you have a good understanding of mathematics. Working knowledge of R 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 in 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 has become the new black. The challenge in today's world is the explosion of data from existing legacy data and incoming new structured and unstructured data. The complexity of discovering, understanding, performing analysis, and predicting outcomes on the data using machine learning algorithms is a challenge. This cookbook will help solve everyday challenges you face as a data scientist. The application of various data science techniques and on multiple data sets based on real-world challenges you face will help you appreciate a variety of techniques used in various situations.The first half of the book provides recipes on fairly complex machine-learning systems, where you'll learn to explore new areas of applications of machine learning and 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 machine learning case studies, all based on real-world data, and offers solutions and solves specific machine-learning issues in each one.Style and approachFollowing a cookbook approach, we'll teach you how to solve everyday difficulties and struggles you encounter.