Machine Learning Algorithms in Depth you own this product

Vadim Smolyakov
  • July 2024
  • ISBN 9781633439214
  • 328 pages
  • printed in black & white

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ePub + liveBook available Dec 23, 2024
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Learn how machine learning algorithms work from the ground up so you can effectively troubleshoot your models and improve their performance.

Fully understanding how machine learning algorithms function is essential for any serious ML engineer. In Machine Learning Algorithms in Depth you’ll explore practical implementations of dozens of ML algorithms including:

  • Monte Carlo Stock Price Simulation
  • Image Denoising using Mean-Field Variational Inference
  • EM algorithm for Hidden Markov Models
  • Imbalanced Learning, Active Learning and Ensemble Learning
  • Bayesian Optimization for Hyperparameter Tuning
  • Dirichlet Process K-Means for Clustering Applications
  • Stock Clusters based on Inverse Covariance Estimation
  • Energy Minimization using Simulated Annealing
  • Image Search based on ResNet Convolutional Neural Network
  • Anomaly Detection in Time-Series using Variational Autoencoders

Machine Learning Algorithms in Depth dives into the design and underlying principles of some of the most exciting machine learning (ML) algorithms in the world today. With a particular emphasis on probabilistic algorithms, you’ll learn the fundamentals of Bayesian inference and deep learning. You’ll also explore the core data structures and algorithmic paradigms for machine learning. Each algorithm is fully explored with both math and practical implementations so you can see how they work and how they’re put into action.

about the technology

Learn how machine learning algorithms work from the ground up so you can effectively troubleshoot your models and improve their performance. This book guides you from the core mathematical foundations of the most important ML algorithms to their Python implementations, with a particular focus on probability-based methods.

about the book

Machine Learning Algorithms in Depth dissects and explains dozens of algorithms across a variety of applications, including finance, computer vision, and NLP. Each algorithm is mathematically derived, followed by its hands-on Python implementation along with insightful code annotations and informative graphics. You’ll especially appreciate author Vadim Smolyakov’s clear interpretations of Bayesian algorithms for Monte Carlo and Markov models.

what's inside

  • Monte Carlo stock price simulation
  • EM algorithm for hidden Markov models
  • Imbalanced learning, active learning, and ensemble learning
  • Bayesian optimization for hyperparameter tuning
  • Anomaly detection in time-series

about the reader

For machine learning practitioners familiar with linear algebra, probability, and basic calculus.

about the author

Vadim Smolyakov is a data scientist in the Enterprise & Security DI R&D team at Microsoft.

I love this book! It shows you how to implement common ML algorithms in plain Python with only the essential libraries, so you can see how the computation and math works in practice.

Junpeng Lao, Senior Data Scientist at Google

I highly recommend this book. In the era of ChatGPT real knowledge of algorithms is invaluable.

Vatsal Desai, InfoDesk

Explains algorithms so well that even a novice can digest it.

Harsh Raval, Zymr

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monthly
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$49.99
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only $33.33 per month
  • five seats for your team
  • access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
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  • choose twelve free products per year
  • exclusive 50% discount on all purchases
  • Machine Learning Algorithms in Depth ebook for free