Choose Cost-Effective ML Algorithms Fast teaches you how to evaluate and compare machine learning algorithms based on their resource utilization—not just accuracy. In real ML pipelines, training time, memory footprint, and compute cost determine whether a model can run reliably at scale. In this short, practical course, you’ll examine how algorithm design affects efficiency, learn how to benchmark models fairly, and interpret logs to uncover cost patterns. You’ll complete a hands-on lab comparing XGBoost and Random Forest on a large dataset, charting training time and memory usage, and making a clear recommendation for the most cost-effective option. By the end of the course, you’ll know how to select algorithms that meet performance goals while staying efficient, predictable, and production-ready.
Choose Cost-Effective ML Algorithms Fast teaches you how to evaluate and compare machine learning algorithms based on their resource utilization—not just accuracy. In real ML pipelines, training time, memory footprint, and compute cost determine whether a model can run reliably at scale. In this sh…
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Coursera is in Tier 4: commercial training companies and platform-made courses of our institution ranking (56/100). Made by the platform, often short guided projects.
This course provides an introduction to mathematical modeling of computational problems. It covers the common algorithms, algorithmic paradigms, and data structures used to solve these problems. The course emphasizes the relationship between algorithms and programming, and introduces basic performa…
Instructor: Prof. Erik Demaine, Dr. Jason Ku, Prof. Justin Solomon View the complete course: https://ocw.mit.edu/6-006S20 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63EdVPNLG3ToM6LaEUuStEY This course is an introduction to mathematical modeling of computational problems, as…
This course teaches techniques for the design and analysis of efficient algorithms, emphasizing methods useful in practice. Topics covered include: sorting; search trees, heaps, and hashing; divide-and-conquer; dynamic programming; amortized analysis; graph algorithms; shortest paths; network flow;…
Instructor: Dennis Freeman, Kendra Pugh This course provides an integrated introduction to electrical engineering and computer science, including modern software engineering, linear systems analysis, electronic circuits, and decision-making. The lecture videos provide an overview of each topic, whi…
View the complete course: http://ocw.mit.edu/6-046JS15 Instructors: Erik Demaine, Srinivas Devadas, Nancy Ann Lynch 6.046 introduces students to the design of computer algorithms, as well as analysis of sophisticated algorithms. License: Creative Commons BY-NC-SA More information at http://ocw.mit.…
View the complete course: http://ocw.mit.edu/6-851S12 Instructor: Erik Demaine Videos of 6.851 Lectures Spring 2012 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
