Part 1 – Machine Learning For Beginners – Basics
Part 2 – MI environment
Part 3 – Python Decision Tree (Theory)
Part 4 – Python Decision Tree (Coding)
Part 5 – Python Decision Tree (Graphiviz)
Part 6 – Knn(Friend Recommender)
Part 7- 5-Fold Cross Validation
If you want to have a full understanding of neural networks or some of the more convoluted statistical techniques, then I suggest you will need to be comfortable with linear algebra, differential calculus, and some more advanced statistics.
My personal recommendation is this: maths looks really scary, but often even the more alien looking formulae describe relatively simple concepts, especially for someone who knows logical constructs via programming. Don’t be afraid to look for more “human friendly” tutorials on some of the trickier concepts – you might be surprised.
There’s also nothing wrong with starting (or even staying) with statistical learning like decision trees. Most real-world data science can be completed better with well-crafted statistical techniques than with neural networks