Build 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 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.
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 Learn
- 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.
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.
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.
Table of contents
- A Gentle Introduction to Machine Learning
- Important Elements in Machine Learning
Feature Selection and Feature Engineering
- scikit-learn toy datasets
- Creating training and test sets
- Managing categorical data
- Managing missing features
- Data scaling and normalization
- Feature selection and filtering
- Principal component analysis
- Atom extraction and dictionary learning
- Linear Regression
- Logistic Regression
- Naive Bayes
- Support Vector Machines
Decision Trees and Ensemble Learning
- Binary decision trees
- Decision tree classification with scikit-learn
- Ensemble learning
- Clustering Fundamentals
- Hierarchical Clustering
Introduction to Recommendation Systems
- Naive user-based systems
- Content-based systems
- Model-free (or memory-based) collaborative filtering
- Model-based collaborative filtering
- Introduction to Natural Language Processing
- Topic Modeling and Sentiment Analysis in NLP
A Brief Introduction to Deep Learning and TensorFlow
- Deep learning at a glance
- A brief introduction to TensorFlow
- A quick glimpse inside Keras
- Creating a Machine Learning Architecture
- Title: Machine Learning Algorithms
- Release date: July 2017
- Publisher(s): Packt Publishing
- ISBN: 9781785889622
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