Home | Amazing | Today | Tags | Publishers | Years | Account | Search 
Machine Learning in Action

Buy

After college I went to work for Intel in California and mainland China. Originally my plan was to go back to grad school after two years, but time flies when you are having fun, and two years turned into six. I realized I had to go back at that point, and I didn’t want to do night school or online learning, I wanted to sit on campus and soak up everything a university has to offer. The best part of college is not the classes you take or research you do, but the peripheral things: meeting people, going to seminars, joining organizations, dropping in on classes, and learning what you don’t know.

Summary

Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification.

About the Book

A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interesting or useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many.

Machine Learning in Action is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you'll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You'll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification.

Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful.

What's Inside
  • A no-nonsense introduction
  • Examples showing common ML tasks
  • Everyday data analysis
  • Implementing classic algorithms like Apriori and Adaboos

===================================

Table of Contents
PART 1 CLASSIFICATION
PART 2 FORECASTING NUMERIC VALUES WITH REGRESSION
PART 3 UNSUPERVISED LEARNING
PART 4 ADDITIONAL TOOLS
  1. Machine learning basics
  2. Classifying with k-Nearest Neighbors
  3. Splitting datasets one feature at a time: decision trees
  4. Classifying with probability theory: naïve Bayes
  5. Logistic regression
  6. Support vector machines
  7. Improving classification with the AdaBoost meta algorithm
  8. Predicting numeric values: regression
  9. Tree-based regression
  10. Grouping unlabeled items using k-means clustering
  11. Association analysis with the Apriori algorithm
  12. Efficiently finding frequent itemsets with FP-growth
  13. Using principal component analysis to simplify data
  14. Simplifying data with the singular value decomposition
  15. Big data and MapReduce

 

(HTML tags aren't allowed.)

Enterprise Cybersecurity: How to Build a Successful Cyberdefense Program Against Advanced Threats
Enterprise Cybersecurity: How to Build a Successful Cyberdefense Program Against Advanced Threats

Enterprise Cybersecurity empowers organizations of all sizes to defend themselves with next-generation cybersecurity programs against the escalating threat of modern targeted cyberattacks. This book presents a comprehensive framework for managing all aspects of an enterprise cybersecurity program. It enables an enterprise to...

Introduction to Evolutionary Algorithms (Decision Engineering)
Introduction to Evolutionary Algorithms (Decision Engineering)
Evolutionary algorithms are becoming increasingly attractive across various disciplines, such as operations research, computer science, industrial engineering, electrical engineering, social science and economics. Introduction to Evolutionary Algorithms presents an insightful, comprehensive, and up-to-date treatment of evolutionary algorithms....
Artificial Intelligence and Machine Learning Fundamentals: Develop real-world applications powered by the latest AI advances
Artificial Intelligence and Machine Learning Fundamentals: Develop real-world applications powered by the latest AI advances

Create AI applications in Python and lay the foundations for your career in data science

Key Features

  • Practical examples that explain key machine learning algorithms
  • Explore neural networks in detail with interesting examples
  • Master core AI concepts with...

Unity 5.x Shaders and Effects Cookbook
Unity 5.x Shaders and Effects Cookbook

Master the art of Shader programming to bring life to your Unity projects

About This Book

  • This book will help you master the technique of physically based shading in Unity 5 to add realism to your game quickly through precise recipes
  • From an eminent author, this book offers you the fine...
Markov Chains: Models, Algorithms and Applications (International Series in Operations Research & Management Science)
Markov Chains: Models, Algorithms and Applications (International Series in Operations Research & Management Science)
Markov chains are a particularly powerful and widely used tool for analyzing a variety of stochastic (probabilistic) systems over time. This monograph will present a series of Markov models, starting from the basic models and then building up to higher-order models. Included in the higher-order discussions are multivariate models, higher-order...
Scalability Patterns: Best Practices for Designing High Volume Websites
Scalability Patterns: Best Practices for Designing High Volume Websites

In this book, the CEO of Cazton, Inc. and internationally-acclaimed speaker, Chander Dhall, demonstrates current website design scalability patterns and takes a pragmatic approach to explaining their pros and cons to show you how to select the appropriate pattern for your site. He then tests the patterns by deliberately...

©2019 LearnIT (support@pdfchm.net) - Privacy Policy