Project Machine Learning
Hello there! So this repository is basically the road i'm taking to learn data science, i'll post here all the content from the courses i have taken/will take in the future, as well as a general plan (that may be changed as time passes by) of what subjects i must learn. This will consist of mainly data science courses with a focus on machine learning.
I hope that this repo will help those wanting to learn data science and machine learning, having a more clear path to follow, because i know it all too well how confusing choosing what to do next can be for a newcomer (as i'm still one myself). Enjoy!
More experienced data scientists/ programmers can also come here and contribute to this readme, of what i should change in my plan.
The Path
So, this path will be divided into three sections: The foundations, which i call "Meat and Potatoes" (MAP) of data science, this section cover all the mathematics (calculus, linear algebra), computer science and statistics/probability. The second section will be about an introduction to data science and machine learning (INTRO DATA). And finally the last section will cover advanced topics about data science and machine learning (ADVANCED).
MAP - Meat and Potatoes
1. Mathematics:
M1.1. Single Variable Calculus - MIT ocw
M1.2. Linear algebra - MIT ocw
M1.3. Multi-variable Calculus - MIT ocw
2. Programming:
M2.1. 101 Intro do CS with python by MIT on edx.org
M2.2. Automate the Boring Stuff with Python by Al Sweigart
M2.3. Introduction to Computational Thinking and Data Science by MIT on edx.org
M2.4. Algorithm Design and Analysis
M2.5. Data Structures and Software Design
M2.6. Object-Oriented Programming using python
3. Statistics/Probability:
M3.1. Stat 110: Introduction to Probability: Joe Blitzstein - Harvard University
M3.2. Think statistics in python
M3.3. Think Bayes
INTRO DATA - Introduction to Data Science and Machine Learning
1. Data Science:
I1.1. The Analytics Edge
I1.2. Python for Data Science
I1.3. Data Science CS109
2. Machine Learning:
I2.1. Learning From Data (Introductory Machine Learning) - caltech
I2.2. Stanford's Machine Learning Course
Important Note: By the time i finish all, or almost all these courses, i should be able to tackle the easiest problems on Keggle.
ADVANCED
This section is still under construction, but i'll put some courses that i might take:
1. Distributed and Parallel computing:
A1.1. Intro to Hadoop and MapReduce
A1.2. Introduction to Big Data with Apache Spark
2. Convex Optimization:
A2.1. Convex Optimization
3. Databases:
A3.1. Stanford's Database course
4. Deep Learning:
A 4.1. Deep Learning on udacity
Credits
And last bue not least, some of the places where i got this course list from: