#291 overall
Introduction to Analytics Modeling
edX
2 recommenders · 2 threads · recommended in 1 year, 2020 · score 1.7
An introductory graduate-level course from Georgia Tech (ISYE 6501X) on edX that surveys analytics and machine learning methods, including how to choose a method for a problem and compare results. Topics named in comments include SVMs, clustering, PCA, decision trees, time-series models, Markov chains, and simulation in R, Python, and Arena.
Commenters call it a standout online course with an excellent lecturer and a useful overview of how to select and compare analytics methods.
Machine Learning & AIintermediatevideo lectures#machine learning#analytics#clustering#time series#sokol
Recommendations by year
20062026
I strongly recommend Georgia Tech's ISYE6501X course, "Introduction to Analytics Modeling" - available at edX.org[0] The professor, Joel Sokol, is a masterful lecturer with excellent pacing and a great intuition for guessing what the audience will be thinking at any given point in a lecture, and quickly addressing those thoughts.
The curriculum of the course covers foundational knowledge requisite for machine learning, data analytics, big data. The course does a fantastic job of breaking down how to select the most appropriate method for any given situation you might run into, and how to critically compare the results of various methods to see which worked best.
Personally I feel this material should be included in every STEM major's undergraduate coursework. And I'm usually displeased with the quality of online MOOC's; this particular course truly stands out.
The actual topics discussed include:
Support vector machines (SVM), classification, clustering, principal component analysis (PCA), Bayesian modeling, exponential smoothing (ARIMA, GARCH), decision trees, Markov chains, k-mean, k-nn, Q-Q plots, probability distributions, and graph analysis. Software used included R, Python, and Rockwell’s Arena Simulation Software.
0: https://www.edx.org/course/introduction-to-analytics-modelin...
I'm 6 classes through it. Classes vary wildly in difficulty and applicability.
Favorites so far for me were AI4R (Ariticial Intelligence for Robotics) which is a more rigorous version of Udacity's Self-Driving Car course, ML4T (Machine Learning for Trading) where you implement various algorithms to optimize trading strategies, and ISYE 6501 (Intro to Analysis), which is a survey of various analysis techniques and ML algorithms.
Still have to take the reputed hardest course in the program, the General Algorithms course, the ML course, and an elective.
Worth the money, though I have had to skip a couple of semesters due to burnout. Work and this program are hard (for me).