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Develop a mathematical intuition for how Machine Learning (ML) 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.
PART 1: INTRODUCING ML ALGORITHMS
1 Machine Learning Algorithms
2 Markov Chain Monte Carlo
3 Variational Inference
4 Software Implementation
PART 2: SUPERVISED LEARNING
5 Classification Algorithms
6 Regression Algorithms
7 Selected Supervised Learning Algorithms
PART 3: UNSUPERVISED LEARNING
8 Fundamental Unsupervised Learning Algorithms
9 Selected Unsupervised Learning Algorithms
PART 4: DEEP LEARNING
10 Fundamental Deep Learning Algorithms
11 Advanced Deep Learning Algorithms
APPENDIXES