Machine Learning Algorithms: Popular algorithms for data science and machine learning, 2nd Edition

Description

An easy-to-follow, step-by-step guide for getting to grips with the real-world application of machine learning algorithms Key Features Explore statistics and complex mathematics for data-intensive applications Discover new developments in EM algorithm, PCA, and bayesian regression Study patterns and make predictions across various datasets Book Description Machine learning has gained tremendous popularity for its powerful and fast predictions with large datasets. However, the true forces behind its powerful output are the complex algorithms involving substantial statistical analysis that churn large datasets and generate substantial insight. This second edition of Machine Learning Algorithms walks you through prominent development outcomes that have taken place relating to machine learning algorithms, which constitute major contributions to the machine learning process and help you to strengthen and master statistical interpretation across the areas of supervised, semi-supervised, and reinforcement learning. Once the core concepts of an algorithm have been covered, you’ll explore real-world examples based on the most diffused libraries, such as scikit-learn, NLTK, TensorFlow, and Keras. You will discover new topics such as principal component analysis (PCA), independent component analysis (ICA), Bayesian regression, discriminant analysis, advanced clustering, and gaussian mixture. By the end of this book, you will have studied machine learning algorithms and be able to put them into production to make your machine learning applications more innovative. What you will learn Study feature selection and the feature engineering process Assess performance and error trade-offs for linear regression Build a data model and understand how it works by using different types of algorithm Learn to tune the parameters of Support Vector Machines (SVM) Explore the concept of natural language processing (NLP) and recommendation systems Create a machine learning architecture from scratch Who this book is for Machine Learning Algorithms is for you if you are a machine learning engineer, data engineer, or junior data scientist who wants to advance in the field of predictive analytics and machine learning. Familiarity with R and Python will be an added advantage for getting the best from this book. Table of Contents A Gentle Introduction to Machine Learning Important Elements in Machine Learning Feature Selection and Feature Engineering Regression Algorithms Linear Classification Algorithms Naive Bayes and Discriminant Analysis Support Vector Machines Decision Trees and Ensemble Learning Clustering Fundamentals Advanced Clustering Hierarchical Clustering Introducing Recommendation Systems Introducing Natural Language Processing Topic Modeling and Sentiment Analysis in NLP Introducing Neural Networks Advanced Deep Learning Models Creating a Machine Learning Architecture Review: Wider coverage of more up to date machine learning techniques - I read the 1st edition of this book and last week I grab and read the 2nd edition too. The 2nd edition has so many improvements and extension with even wider coverage of more up to date machine learning techniques, which will help you to strengthen and master statistical interpretation across the areas of supervised, semi-supervised, and reinforcement learning. Similar to the 1st edition, the author has covered ML algorithms and their use cases in a bottom-up fashion (from classical approaches to deep learning). Again similar to 1st edition, the 2nd edition also has wider coverage of different Python libraries -e.g. scikit-learn, NLTK, TensorFlow, and Keras. Using these libraries, the author has explained principal component analysis (PCA), independent component analysis (ICA), Bayesian regression, discriminant analysis, advanced clustering, and Gaussian mixture with many hands-on examples. In my opinion, to grasp all these machine learning techniques and algorithms, you have to have a strong and thorough understanding of mathematics and statistics.