Machine Learning Algorithms in Depth

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 will learn the fundamentals of Bayesian inference and deep learning.

Machine Learning Algorithms in Depth dives deep into the 'how' and the 'why' of machine learning algorithms. For each category of an algorithm, you will go from math-first principles to hands-on implementation in Python. You will 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 will know how major algorithms work under the hood — and be a better machine learning practitioner.

About the reader

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

Develop a mathematical intuition around machine learning algorithms to improve model performance and effectively troubleshoot complex ML problems.

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

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 will learn the fundamentals of Bayesian inference and deep learning. You will also explore the core data structures and algorithmic paradigms for machine learning.

You will 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 Optimisation for Hyperparameter Tuning
• Dirichlet Process K-Means for Clustering Applications
• Stock Clusters based on Inverse Covariance Estimation
• Energy Minimisation using Simulated Annealing
• Image Search based on ResNet Convolutional Neural Network
• Anomaly Detection in Time-Series using Variational Autoencoders

Each algorithm is fully explored with both math and practical implementations so you can see how they work and 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 trade-offs 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.