Home | Amazing | Today | Tags | Publishers | Years | Account | Search 
Machine Learning Algorithms: Popular algorithms for data science and machine learning, 2nd Edition

Buy

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

  1. A Gentle Introduction to Machine Learning
  2. Important Elements in Machine Learning
  3. Feature Selection and Feature Engineering
  4. Regression Algorithms
  5. Linear Classification Algorithms
  6. Naive Bayes and Discriminant Analysis
  7. Support Vector Machines
  8. Decision Trees and Ensemble Learning
  9. Clustering Fundamentals
  10. Advanced Clustering
  11. Hierarchical Clustering
  12. Introducing Recommendation Systems
  13. Introducing Natural Language Processing
  14. Topic Modeling and Sentiment Analysis in NLP
  15. Introducing Neural Networks
  16. Advanced Deep Learning Models
  17. Creating a Machine Learning Architecture
(HTML tags aren't allowed.)

Python Data Science Essentials: A practitioner's guide covering essential data science principles, tools, and techniques, 3rd Edition
Python Data Science Essentials: A practitioner's guide covering essential data science principles, tools, and techniques, 3rd Edition

Gain useful insights from your data using popular data science tools

Key Features

  • A one-stop guide to Python libraries such as pandas and NumPy
  • Comprehensive coverage of data science operations such as data cleaning and data manipulation
  • Choose scalable...
Modern Python Standard Library Cookbook: Over 100 recipes to fully leverage the features of the standard library in Python
Modern Python Standard Library Cookbook: Over 100 recipes to fully leverage the features of the standard library in Python

Build optimized applications in Python by smartly implementing the standard library

Key Features

  • Strategic recipes for effective application development in Python
  • Techniques to create GUIs and implement security through cryptography
  • Best practices for...
Python Deep Learning Projects: 9 projects demystifying neural network and deep learning models for building intelligent systems
Python Deep Learning Projects: 9 projects demystifying neural network and deep learning models for building intelligent systems

Insightful projects to master deep learning and neural network architectures using Python and Keras

Key Features

  • Explore deep learning across computer vision, natural language processing (NLP), and image processing
  • Discover best practices for the training of deep neural...

Learn TensorFlow 2.0: Implement Machine Learning and Deep Learning Models with Python
Learn TensorFlow 2.0: Implement Machine Learning and Deep Learning Models with Python
Learn how to use TensorFlow 2.0 to build machine learning and deep learning models with complete examples. 

The book begins with introducing TensorFlow 2.0 framework and the major changes from its last release. Next, it focuses on building Supervised Machine Learning models using TensorFlow 2.0.
...
Learn Data Analysis with Python: Lessons in Coding
Learn Data Analysis with Python: Lessons in Coding
Get started using Python in data analysis with this compact practical guide. This book includes three exercises and a case study on getting data in and out of Python code in the right format. Learn Data Analysis with Python also helps you discover meaning in the data using analysis and shows you how to visualize...
Python for Data Science For Dummies (For Dummies (Computer/Tech))
Python for Data Science For Dummies (For Dummies (Computer/Tech))

The fast and easy way to learn Python programming and statistics

Python is a general-purpose programming language created in the late 1980s?and named after Monty Python?that's used by thousands of people to do things from testing microchips at Intel, to powering Instagram, to building video games with the...

©2020 LearnIT (support@pdfchm.net) - Privacy Policy