This repo allows me to save and keep track of my progress in this MIT MOOC about Data Sciences and Machine Learning Techniques.
It contains all the homework assignments to do after each unit.
Here is the link to the MOOC, available on the edx platform for free now (July 2016).
✅ Completed.
- Initial evaluation (various knowledge about Data & Machine Learning).
- First lecture.
- Intro to R.
- Recitation: Understanding Food, Nutritional Education with Data.
- Assigment 1:
- An analytical detective.
- Stock Dynamics.
- Demographics and Employment in the United States.
- Internet privacy poll.
✅ Completed.
- Introduction to Linear Regression: the statistical sommelier.
- Lecture: Moneyball: The power of sports analytics.
- Recitation: Playing Moneyball in the NBA.
- Assignment 2:
- Climate change.
- Reading test scores (PISA).
- Detecting Flu Epidemics via Search Engine Query Data.
- State Data.
✅ Completed.
- Introduction to logistic regression: Modeling the expert (predicting good/bad care).
- Lecture: The Framingham Heart Study: Evaluating Risk Factors to Save Lives.
- Recitation : Election Forecasting: Predicting the Winner Before any Votes are Cast
- Assigment 3:
- Popularity of music records.
- Predicting parole violators.
- Predicting loan payment.
In Pause. Two tests to do before first assignment.
TODO.
TODO.
✅ Completed.
- Introduction to visualization: Visualizing the world.
- Lecture: The Analytical Policeman: Visualization for Law and Order.
- Recitation: The Good, the Bad, and the Ugly (examples of good and bad visualizations.
- Assigment 7 :
- Election Forecasting in the US.
- Visualizing Network Data on Facebook.
- Visualizing Text Data Using Word Clouds on Twitter.
TODO.
TODO.