Build a strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide

About This Book

Get started in the field of Machine Learning with the help of this solid, concept-rich, yet efficient guide. Your one-stop solution for everything that matters in mastering the whats and whys of Machine Learning algorithms and their implementation. Get a solid foundation for your entry into Machine Learning by strengthening your roots (algorithms) with this comprehensive guide.

Who This Book Is For

This book is for IT professionals who want to enter the field of data science and are very new to Machine Learning. Familiarity with languages such as R and Python will be invaluable here.

What You Will LearnAcquaint yourself with essential elements of Machine learning, the feature selection and feature engineering process performance and error trade-offs for Linear Regression, a data model, and understand how it works by using different types of algorithms learn to tune the parameters of Support Vector machinesImplement clusters to a datasetExplore the concept of Natural Processing Language and Recommendation SystemsCreate an ML architecture from scratch.

In Detail

As the amount of data grows almost incomprehensible, understanding and processing data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars to spam detection, document search, trading strategies, and speech recognition. This makes machine learning well-suited to the present-day Big Data and Data Science era. The main challenge is how to transform data into actionable knowledge.

In this book, you will learn all the essential machine-learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised, unsupervised, reinforcement, and semi-supervised learning. This book covers a few famous algorithms: Linear regression, Logistic Regression, SVM, Naive Bayes, K-means, Random Forest, TensorFlow, and Feature engineering. In this book, you will also learn how these algorithms work and their practical implementation to resolve your problems. This book will also introduce you to the Natural Processing Language and Recommendation systems, which help you run multiple algorithms simultaneously.

After completing the book, you will have mastered selecting Machine Learning algorithms for clustering, classification, or regression based on your problem.

Style and approach

An easy-to-follow, step-by-step guide that will help you get to grips with real-world applications of Algorithms for Machine Learning.

You might also be interested in my Technical Posts, which contain addenda and related topics.