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Data Structures, Algorithms, and Machine Learning Optimization
1 online resource (1 video file, approximately 6 hr., 28 min.)
6+ Hours of Video Instruction Hands-On Approach to Learning the Essential Computer Science for Machine Learning Applications Overview Data Structures, Algorithms, and Machine Learning Optimization LiveLessons provides you with a functional, hands-on understanding of the essential computer science for machine learning applications About the Instructor Jon Krohn is Chief Data Scientist at the machine learning company untapt. He authored the book Deep Learning Illustrated , an instant #1 bestseller that was translated into six languages. Jon is renowned for his compelling lectures, which he offers in-person at Columbia University and New York University, as well as online via O'Reilly, YouTube, and the SuperDataScience podcast. Jon holds a PhD from Oxford and has been publishing on machine learning in leading academic journals since 2010; his papers have been cited over a thousand times. Skill Level Intermediate Learn How To Use "big O" notation to characterize the time efficiency and space efficiency of a given algorithm, enabling you to select or devise the most sensible approach for tackling a particular machine learning problem with the hardware resources available to you. Get acquainted with the entire range of the most widely-used Python data structures, including list-, dictionary-, tree-, and graph-based structures. Develop a working understanding of all of the essential algorithms for working with data, including those for searching, sorting, hashing, and traversing. Discover how the statistical and machine learning approaches to optimization differ, and why you would select one or the other for a given problem you're solving. Understand exactly how the extremely versatile (stochastic) gradient descent optimization algorithm works and how to apply it. Familiarize yourself with the "fancy" optimizers that are available for advanced machine learning approaches (e.g., deep learning) and when you should consider using them. Who Should Take This Course You use high-level software libraries (e.g., scikit-learn, Keras, TensorFlow) to train or deploy machine learning algorithms, and would now like to understand the fundamentals underlying the abstractions, enabling you to expand your capabilities You're a software developer who would like to develop a firm foundation for the deployment of machine learning algorithms into production systems You're a data scientist who would like to reinforce your understanding of the subjects at the c...
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