Home | Amazing | Today | Tags | Publishers | Years | Account | Search 
Mastering Machine Learning Algorithms: Expert techniques to implement popular machine learning algorithms and fine-tune your models

Buy

Explore and master the most important algorithms for solving complex machine learning problems.

Key Features

  • Discover high-performing machine learning algorithms and understand how they work in depth
  • One-stop solution to mastering supervised, unsupervised, and semi-supervised machine learning algorithms and their implementation
  • Master concepts related to algorithm tuning, parameter optimization, and more

Book Description

Machine learning is a subset of AI that aims to make modern-day computer systems smarter and more intelligent. The real power of machine learning resides in its algorithms, which make even the most difficult things capable of being handled by machines. However, with the advancement in the technology and requirements of data, machines will have to be smarter than they are today to meet the overwhelming data needs; mastering these algorithms and using them optimally is the need of the hour.

Mastering Machine Learning Algorithms is your complete guide to quickly getting to grips with popular machine learning algorithms. You will be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and will learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this book will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries such as scikit-learn. You will also learn how to use Keras and TensorFlow to train effective neural networks.

If you are looking for a single resource to study, implement, and solve end-to-end machine learning problems and use-cases, this is the book you need.

What you will learn

  • Explore how a ML model can be trained, optimized, and evaluated
  • Understand how to create and learn static and dynamic probabilistic models
  • Successfully cluster high-dimensional data and evaluate model accuracy
  • Discover how artificial neural networks work and how to train, optimize, and validate them
  • Work with Autoencoders and Generative Adversarial Networks
  • Apply label spreading and propagation to large datasets
  • Explore the most important Reinforcement Learning techniques

Who This Book Is For

This book is an ideal and relevant source of content for data science professionals who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model. A basic knowledge of machine learning is preferred to get the best out of this guide.

Table of Contents

  1. Machine Learning Model Fundamentals
  2. Introduction to Semi-Supervised Learning
  3. Graph-based Semi-Supervised Learning
  4. Bayesian Networks and Hidden Markov Models
  5. EM algorithm and applications
  6. Hebbian Learning
  7. Advanced Clustering and Feature Extraction
  8. Ensemble Learning
  9. Neural Networks for Machine Learning
  10. Advanced Neural Models
  11. Auto-Encoders
  12. Generative Adversarial Networks
  13. Deep Belief Networks
  14. Introduction to Reinforcement Learning
  15. Policy estimation algorithms
(HTML tags aren't allowed.)

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.
...
Python Projects for Beginners: A Ten-Week Bootcamp Approach to Python Programming
Python Projects for Beginners: A Ten-Week Bootcamp Approach to Python Programming

Immerse yourself in learning Python and introductory data analytics with this book’s project-based approach. Through the structure of a ten-week coding bootcamp course, you’ll learn key concepts and gain hands-on experience through weekly projects.

Each chapter in this book is presented as a full week of...

Python Artificial Intelligence Projects for Beginners: Get up and running with Artificial Intelligence using 8 smart and exciting AI applications
Python Artificial Intelligence Projects for Beginners: Get up and running with Artificial Intelligence using 8 smart and exciting AI applications

Build smart applications by implementing real-world artificial intelligence projects

Key Features

  • Explore a variety of AI projects with Python
  • Get well-versed with different types of neural networks and popular deep learning algorithms
  • Leverage popular...

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...
Artificial Intelligence Basics: A Non-Technical Introduction
Artificial Intelligence Basics: A Non-Technical Introduction

Artificial intelligence touches nearly every part of your day. While you may initially assume that technology such as smart speakers and digital assistants are the extent of it, AI has in fact rapidly become a general-purpose technology, reverberating across industries including transportation, healthcare, financial services, and...

Docker on Windows: From 101 to production with Docker on Windows, 2nd Edition
Docker on Windows: From 101 to production with Docker on Windows, 2nd Edition

Learn how to run new and old applications in Docker containers on Windows - modernizing the architecture, improving security and maximizing efficiency.

Key Features

  • Run .NET Framework and .NET Core apps in Docker containers for efficiency, security and portability
  • Design...
©2020 LearnIT (support@pdfchm.net) - Privacy Policy