Your hands-on reference guide to develop, train and optimize your machine learning models
Key Features
- Your guide to learning efficient machine learning process from scratch
- Expert techniques and hacks on a variety of machine learning concepts
- Solutions to your problems with codes supporting R, Python, Scala and Apache Spark
Book Description
Learning about the unknowns and getting hidden insights from your datasets is possible via mastering many tools and techniques from machine learning. Machine Learning Quick Reference gives you access to this core practice in a very compact manner.
This book will prove to be a direct reference point for you while you develop your own machine learning models. It includes hands-on, easy to access techniques on a variety of topics such as model selection, performance tuning, training neural networks, time series analysis and a lot more. Get an in-depth understanding of the commonly used machine learning algorithms, as well as the performance measures and best practices to ensure optimum performance of your models. The book also includes the necessary theory and mathematical explanations wherever required to understand and apply the concepts in the best possible manner. Further, deep learning techniques like deep neural networks, Adversarial Networks: GAN, Bayesian, Deep Gaussian processes will take over your mind. Finally, you will have hands-on experience in dealing with the advanced methods like classification, clustering, imputation, and regression.
By the end, you will have mastered all the tips, tricks and hacks related to machine learning to ease your day to day tasks.
What you will learn
- Get a quick rundown of basics such as model selection, statistical modeling, and cross-validation
- Choose the best machine learning algorithm that suits a particular problem
- Explore kernel learning, neural networks, and time-series analysis
- Train deep learning models and optimize them for maximum performance
- Dive into bayesian techniques and sentiment analysis in your NLP solution
- Implement probabilistic graphical models and causal inference
- Measure and optimize the performance of your machine learning models
Who This Book Is For
This book aims at giving machine learning practitioners from different domains - such as data scientists, machine learning developers and engineers - a reference point in building machine learning solutions in practice. Intermediate machine learning developers and data scientists looking for a quick, handy reference to all the concepts of machine learning will find this book to be very useful. Some exposure to machine learning will be required to get the best out of the book.