Machine learning algorithms: popular algorithms for data science and machine learning

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.

Previous edition published: 2017.

Machine learning algorithms : popular algorithms for data science and machine learning -- Dedication -- Packt Upsell -- Contributors -- Table of Contents -- Preface -- Chapter 1: A Gentle Introduction to Machine Learning -- Chapter 2: Important Elements in Machine Learning -- Chapter 3: Feature Selection and Feature Engineering -- Chapter 4: Regression Algorithms -- Chapter 5: Linear Classification Algorithms -- Chapter 6: Naive Bayes and Discriminant Analysis -- Chapter 7: Support Vector Machines -- Chapter 8: Decision Trees and Ensemble Learning -- Chapter 9: Clustering Fundamentals -- Chapter 10: Advanced Clustering -- Chapter 11: Hierarchical Clustering -- Chapter 12: Introducing Recommendation Systems -- Chapter 13: Introducing Natural Language Processing -- Chapter 14: Topic Modeling and Sentiment Analysis in NLP -- Chapter 15: Introducing Neural Networks -- Chapter 16: Advanced Deep Learning Models -- Chapter 17: Creating a Machine Learning Architecture -- Other Books You May Enjoy -- Index.