Loose path:
Understanding Math is pivotal. You can never be a good Machine Learning Scientist by skipping the Math.
Probability & Statistics Basic Probability and Stats will be helpful in understanding ML algorithms like Naive Bayes.
Statistics 101 - Udacity Taught by the founder of GoogleX it's full of exercises in Python so you won't get bored.
MIT 18.06 Linear Algebra Prof. Strang is terrific! Not only he'll make you fall in love in Linear Algebra but you'll learn important concepts like SVD and matrix algebra. You might wanna grab this PDF as well. Be sure to also solve the exam question papers from here: link
MIT Single Variable Calculus This is my personal favorite book, use it for SVC + MVC link Amazing course but it gets quite tedious in the middle, you might wanna skim some geometry, but the key is to understand how optimization works. Be sure to solve questions from here: link
MIT Multi Variable Calculus Understanding vector calculus is necessary for algorithms like SVM, you might wanna skim some parts which are purely theoretical. Be sure to solve questions from here: link
(Optional) Stanford Convex Optimization WARNING: Do this course only if you're very good at math. Convex Optimization will teach you numerous functions used in Machine Learning. But this course is extremely heavy on Math!
Python - Any one, both courses are equally good
Algorithms
Since you'll be coding a lot of algorithms yourself basic understanding is necessary
In case you want to go deeper
Complete one out of two:
Machine Learning A-Z Introductory course on ML focusing on not only Python but also R, one of the best sellers on Udemy.
Introduction to Machine Learning - Udacity Sebastian Thrun does an awesome job explaining various approaches in ML. It gets a little boring in the middle but overall it's very good.
Two quick courses on applying the theory you learnt. They're short so I recommend doing both of them.
Deep Learning
Neural Networks by Geofrrey Hinton This guy is the creator of backpropagation algorithm! Warning: very heavy on Math.
Must read book on Deep Learning: Free HTML book
Deep Learning course by Andrew Ng It has 5 courses, search them and enroll if you want to audit all the 5 courses for free.
Big Data & Large Scale Machine Learning
Natural Language Processing
Self Driving Car
Scientific Computing
I have curated a collection of Jupyter Notebooks which can be used as a quick refreseher for various Machine Learning & Deep Learning concepts. Bookmark it for daily use: Machine Learning Notebooks