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A great base for getting started on Machine Learning theory and learning how to use Python tools to create models.
Many libraries and services treat machine learning like a black box—you just plug in your data and trust that the answer is correct. To really understand machine learning you need to know what’s going on inside the system. How Machine Learning Works is an introduction to core ML techniques and algorithms with a focus on understanding the underlying theory and mathematics. With this invaluable guide, you’ll acquire the competitive edge that comes from knowing what to do and why it works.
about the technology
Machine learning is the general term for a collection of data analysis techniques that accurately and efficiently identify patterns and relationships in data and then use those models to make predictions about new data. ML drives many features of modern applications, such as tailored product recommendations, social media feeds, forecasting consumer trends, customized therapy for people with developmental challenges, and other world-changing innovations. To understand, create, and apply new ML models you need both practical skills using ML tools and libraries and a deep understanding of the theory and math under the hood.
about the book
How Machine Learning Works
gives you an in-depth look at the mathematical and theoretical foundations of machine learning. Seasoned practitioner Mostafa Samir Abd El-Fattah
takes you step by step through a real-world ML projects. In it, you’ll learn the components that make up a machine learning problem and explore supervised and unsupervised learning. Blending theoretical foundations with practical ML skills, you’ll learn to read existing datasets using pandas, a fast and powerful Python library for data analysis and manipulation. Then, you’ll move on to choosing and implementing ML models with scikit-learn, a popular Python framework that provides a diverse range of ML models and algorithms.
Along the way, you’ll be practicing important math skills, including working with probability, random variables, mean, variance, vectors, matrices, linear algebra, and statistics. You’ll also discover similarity-based methods like K-nearest neighbor and K-means clustering; decision tree-based methods like classification and regression trees; and linear methods like regularization and logical regression. Instead of simply applying black-box methods and techniques to ML problems, you’ll grok their underlying structure and apply a robust mathematical understanding alongside your practical skills. By the end of this comprehensive guide, you’ll be able to comfortably explore and understand the latest ML research as well as identify and tackle novel ML problems!
- Understanding machine learning problems
- A review of probability and statistics
- Similarity-based, tree-based, and linear ML methods
- Working with neural networks
- An introduction to deep learning
- Probabilistic models
about the reader
For programmers with basic Python skills, average math skills, and a keen interest in the fundamentals of machine learning.
about the author
Mostafa Samir Abd El-Fattah
has a BSc. in Computer Science and is currently working as a Senior Machine Learning Research Engineer at Mawdoo3, with a focus on developing solutions for Arabic Natural Language Processing and Understanding (NLP & NLU). He also blogs about AI and ML his blog https://mostafa-samir.github.io