Machine Learning Algorithms: A reference guide to popular algorithms for data science and machine learning

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

• Get started in the field of Machine Learning with the help of this solid, concept-rich, yet highly practical 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.

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

In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. A few famous algorithms that are covered in this book are 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.

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

• Acquaint yourself with important elements of Machine Learning
• Understand the feature selection and 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
• Implement clusters to a dataset
• Explore the concept of Natural Processing Language and Recommendation Systems
• Create a ML architecture from scratch.

1. A Gentle Introduction to Machine Learning
2. Important Elements in Machine Learning
3. Feature Selection and Feature Engineering
4. Linear Regression
5. Logistic Regression
6. Naive Bayes
7. Support Vector Machines
8. Decision Trees and Ensemble Learning
9. Clustering Fundamentals
10. Hierarchical Clustering
11. Introduction to Recommendation Systems
12. Introduction to Natural Language Processing
13. Topic Modeling and Sentiment Analysis in NLP
14. A Brief Introduction to Deep Learning and TensorFlow
15. Creating a Machine Learning Architecture