DATA SCIENCE RESOURCES: Machine Learning and Algorithms

Machine Learning and Algorithms

• Books
• A first encounter with Machine Learning: An introduction to machine learning concepts focusing on the intuition and explanation behind whythey work.
• A Programmer’s Guide to Data Mining: A web based book complete with code samples (in Python) and exercises.
• Data Structures and Algorithms: An introduction to computer science with code examples in Python — covers algorithm analysis, data structures, sorting algorithms, and object oriented design.
• An Introduction to Data Mining: An interactive Decision Tree guide (with hyperlinked lectures) to learning data mining and ML.
• Elements of Statistical Learning: One of the most comprehensive treatments of data mining and ML, often used as a university textbook.
• An Introduction to Information Retrieval: Textbook from a Stanford course on NLP and information retrieval with sections on text classification, clustering, indexing, and web crawling.

• Courses
• Coursera: Machine Learning: Stanford’s famous machine learning course taught by Andrew Ng.
• Coursera: Computational Methods for Data Analysis: Statistical methods and data analysis applied to physical, engineering, and biological sciences.
• MIT: Data Mining: MIT: Data Mining: An introduction to the techniques of data mining and how to apply ML algorithms to garner insights.
• edx: Introduction to Artificial Intelligence: The first half of Berkeley’s popular AI course that teaches you to build autonomous agents to efficiently make decisions in stochastic and adversarial settings.
• edx: Introduction to Computer Science and Programming: MIT’s introductory course to the theory and application of Computer Science.