MOOCs | Mario Filho | Data Scientist | Machine Learning Consultant | Kaggle Grandmaster


MOOCs são cursos online gratuitos oferecidos por universidades renomadas. Qualquer pessoa pode fazer, e hoje existem centenas deles disponíveis nas mais diversas áreas de estudo.

Nesta página você encontra os MOOCs que completei, separados por área de interesse e plataforma.

Machine Learning/Data Science

Computing for Data Analysis, Johns Hopkins University, Prof. Roger Peng

Mathematical Biostatistics Bootcamp 1, Johns Hopkins University, Prof. Brian Caffo

Introduction to Data Science, University of Washington, Prof. Bill Howe

Machine Learning, Stanford University, Prof. Andrew Ng

Coding the Matrix: Linear Algebra through CS Applications, Brown University, Prof. Philip Klein

Computational Neuroscience, University of Washington, Profs. Adrienne Fairhall, Rajesh P. N. Rao

General Game Playing, Stanford University, Prof. Michael Genesereth

Mathematical Biostatistics Boot Camp 2, Johns Hopkins University, Prof. Brian Caffo

Neural Networks for Machine Learning, University of Toronto, Prof. Geoffrey Hinton

Probabilistic Graphical Models, Stanford University, Prof. Daphne Koller

Social and Economic Networks: Models and Analysis, Stanford University, Prof. Matthew O. Jackson

Mining Massive Datasets, Stanford University, Profs. Jure Leskovec, Anand Rajaraman, Jeff Ullman

Computational Methods for Data Analysis, University of Washington, Prof. Nathan Kutz

Applied Regression Analysis, Ohio State University, Prof. Stanley Lemeshow

Discrete Optimization, The University of Melbourne, Prof. Pascal Van Hentenryck

Detección de objetos, Universitat Autònoma de Barcelona, Profs. Antonio López Peña, Ernest Valveny, Maria Vanrell

Algorithms: Design and Analysis, Part 1, Stanford University, Prof. Tim Roughgarden


6.00.2x Introduction to Computational Thinking and Data Science, MITx, Profs. Eric Grimson, John Guttag, Ana Bell

Introduction to Theoretical Computer Science, Udacity, Prof. Sebastian Wernicke

Introduction to Artificial Intelligence, Udacity, Profs. Sebastian Thrun, Peter Norvig

15.071x The Analytics Edge, MITx, Profs. Dimitris Bertsimas, Allison O’Hair

Convex Optimization, Stanford University, Prof. Stephen Boyd

Statistical Learning, Stanford University, Profs. Trevor Hastie, Rob Tibshirani

M101P: MongoDB for Developers, MongoDB University, Prof. Andrew Erlichson

Learning from Data, California Institute of Technology, Prof. Yaser S. Abu-Mostafa

Engineering Software as a Service, Part 1, University of California, Berkeley, Profs. Armando Fox, David Patterson

Implementation of Developmental Learning, Université Claude Bernard Lyon 1, Prof. Olivier Georgeon

CS100.1x Introduction to Big Data with Apache Spark, University of California, Berkeley, Prof. Anthony D. Joseph

CS190.1x Scalable Machine Learning, University of California, Berkeley, Prof. Ameet Talwalkar



Principles of Macroeconomics, University of Melbourne, Prof. Nilss Olekalns

Microeconomics Principles,University of Illinois at Urbana-Champaign, Prof. José J. Vázquez-Cognet

Economics of Money and Banking, Part One, Columbia University, Prof. Perry G Mehrling

Introduction to Neuroeconomics: how the brain makes decisions, Higher School of Economics – National Research University, Prof. Vasily Klucharev

Economics of Money and Banking, Part Two, Columbia University, Prof. Perry G Mehrling

An Introduction to Marketing, University of Pennsylvania (Wharton), Profs. David Bell, Peter Fader, Barbara E. Kahn

Options, Futures, and Other Derivatives, University of Toronto, Prof. John Hull

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