*Since i'm providing multiple links for each week, you may choose the best learning source, the one that best suits you among them.

Learn Machine Learning in 3 Months

Everyday Links

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/

Month 1

Week 1 Linear Algebra

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

Week 2 Calculus

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

Week 3 Probability

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

Week 4 Algorithms

http://goalkicker.com/AlgorithmsBook/
https://www.coursera.org/courses?languages=en&query=Algorithm%20design%20and%20analysis

Month 2

Week 1

Learn python for data science

https://www.youtube.com/watch?v=T5pRlIbr6gg&list=PL2-dafEMk2A6QKz1mrk1uIGfHkC1zZ6UU
http://ricardoduarte.github.io/python-for-developers/

Math of Intelligence

https://www.youtube.com/watch?v=xRJCOz3AfYY&list=PL2-dafEMk2A7mu0bSksCGMJEmeddU_H4D

Intro to Tensorflow

https://www.youtube.com/watch?v=2FmcHiLCwTU&list=PL2-dafEMk2A7EEME489DsI468AB0wQsMV

Week 2

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

Week 3-4

ML Project Ideas
https://github.com/NirantK/awesome-project-ideas

Month 3 (Deep Learning)

Week 1

Intro to Deep Learning
https://www.youtube.com/watch?v=vOppzHpvTiQ&list=PL2-dafEMk2A7YdKv4XfKpfbTH5z6rEEj3
https://pt.coursera.org/specializations/deep-learning

Week 2

Deep Learning by Fast.AI
http://course.fast.ai/

Week 3-4

Re-implement DL projects from Siraj's github
https://github.com/llSourcell?tab=repositories

Other resources

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/