Machine Learning Algorithms in Depth you own this product

Vadim Smolyakov
  • MEAP began December 2022
  • Publication in Spring 2023 (estimated)
  • ISBN 9781633439214
  • 325 pages (estimated)
  • printed in black & white
filed under

placing your order...

Don't refresh or navigate away from the page.
print + eBook Receive a print copy shipped to your door + the eBook in Kindle, ePub, & PDF formats + liveBook, our enhanced eBook format accessible from any web browser. $34.99 $79.99 you save $45 (56%)
Machine Learning Algorithms in Depth (print + eBook) added to cart
continue shopping
adding to cart

choose your plan

team

monthly
annual
$49.99
$499.99
only $41.67 per month
  • five seats for your team
  • access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
  • choose another free eBook every time you renew
  • choose twelve free eBooks per year
  • exclusive 50% discount on all purchases
  • Machine Learning Algorithms in Depth eBook for free
eBook Our eBooks come in DRM-free Kindle, ePub, and PDF formats + liveBook, our enhanced eBook format accessible from any web browser. $44.79 $63.99 you save $19 (30%)
Machine Learning Algorithms in Depth (eBook) added to cart
continue shopping
adding to cart

choose your plan

team

monthly
annual
$49.99
$499.99
only $41.67 per month
  • five seats for your team
  • access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
  • choose another free eBook every time you renew
  • choose twelve free eBooks per year
  • exclusive 50% discount on all purchases
  • Machine Learning Algorithms in Depth eBook for free
Look inside
Develop a mathematical intuition for how machine learning algorithms work so you can  improve model performance and effectively troubleshoot complex ML problems.

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 probability-based 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

Fully understanding how machine learning algorithms function is essential for any serious ML engineer. This vital knowledge lets you modify algorithms to your specific needs, understand the tradeoffs when picking an algorithm for a project, and better interpret and explain your results to your stakeholders. This unique guide will take you from relying on one-size-fits-all ML libraries to developing your own algorithms to solve your business needs.

about the book

Machine Learning Algorithms in Depth dives deep into the how and the why of machine learning algorithms. For each category of algorithm, you’ll go from math-first principles to a hands-on implementation in Python. You’ll explore dozens of examples from across all the fields of machine learning, including finance, computer vision, NLP, and more. Each example is accompanied by worked-out derivations and details, as well as insightful code samples and graphics. By the time you’re done reading, you’ll know how major algorithms work under the hood—and be a better machine learning practitioner for it.

about the reader

For intermediate 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. He is a former PhD student in AI at MIT CSAIL with research interests in Bayesian inference and deep learning. Prior to joining Microsoft, Vadim developed machine learning solutions in the e-commerce space.

FREE domestic shipping on orders of three or more print books

RECENTLY VIEWED