*Since i'm providing multiple links for each week, you may choose the best learning source, the one that best suits you among them.
Follow ML people on this twitter list
https://twitter.com/DL_ML_Loop/lists/deep-learning-loop/members
Read about ML projects, ideas and discussions on reddit
https://www.reddit.com/r/MachineLearning/
Join mench, slack and facebook groups to learn along with others
https://mench.co/LearnMachineLearningIn3Months
http://wizards.herokuapp.com/
https://www.facebook.com/groups/1991572177524785/
https://www.youtube.com/playlist?list=PLE7DDD91010BC51F8
https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab
https://www.khanacademy.org/math/linear-algebra
http://www.souravsengupta.com/cds2016/lectures/Savov_Notes.pdf
https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr
http://tutorial.math.lamar.edu/pdf/Calculus_Cheat_Sheet_All.pdf
----------------------------Khan series-------------------------------------
Differential calculus https://www.khanacademy.org/math/differential-calculus
Integral calculus https://www.khanacademy.org/math/integral-calculus
Multivariable calculus https://www.khanacademy.org/math/multivariable-calculus
Differential equations https://www.khanacademy.org/math/differential-equations
https://www.edx.org/course/introduction-probability-science-mitx-6-041x-2
https://www.udacity.com/course/intro-to-inferential-statistics--ud201
https://www.udacity.com/course/intro-to-descriptive-statistics--ud827
https://www.youtube.com/channel/UCRXuOXLW3LcQLWvxbZiIZ0w/playlists
http://goalkicker.com/AlgorithmsBook/
https://www.coursera.org/courses?languages=en&query=Algorithm%20design%20and%20analysis
https://www.youtube.com/watch?v=T5pRlIbr6gg&list=PL2-dafEMk2A6QKz1mrk1uIGfHkC1zZ6UU
http://ricardoduarte.github.io/python-for-developers/
https://www.youtube.com/watch?v=xRJCOz3AfYY&list=PL2-dafEMk2A7mu0bSksCGMJEmeddU_H4D
https://www.youtube.com/watch?v=2FmcHiLCwTU&list=PL2-dafEMk2A7EEME489DsI468AB0wQsMV
Intro to ML
https://www.coursera.org/learn/machine-learning
https://www.edx.org/course/introduction-computer-science-mitx-6-00-1x-11
https://www.udacity.com/course/intro-to-machine-learning--ud120
ML Project Ideas
https://github.com/NirantK/awesome-project-ideas
Intro to Deep Learning
https://www.youtube.com/watch?v=vOppzHpvTiQ&list=PL2-dafEMk2A7YdKv4XfKpfbTH5z6rEEj3
https://pt.coursera.org/specializations/deep-learning
Deep Learning by Fast.AI
http://course.fast.ai/
Re-implement DL projects from Siraj's github
https://github.com/llSourcell?tab=repositories
TwoMinutePapers Recommended Learning
https://www.youtube.com/watch?v=4h0uC9FPVMQ
Stanford Machine Learning summarized notes
http://www.holehouse.org/mlclass/
Free books
http://houseofbots.com/news-detail/2263-4-top-11-free-books-on-machine-learning-and-data-science-that'll-give-you-a-major-edge-over-your-competitors
An Introduction to Statistical Learning with Applications in R
http://www-bcf.usc.edu/~gareth/ISL/
The Elements of Statistical Learning
https://web.stanford.edu/~hastie/ElemStatLearn/