Machine Learning Beginner’s Guide(Resources)
We all know there are tons of material on Machine Learning available, and sometimes it may be hard to find what fits your knowledge, budget and availability. I have tried many and would like to share some of the few resources when I was getting started with Machine learning.
Setting up Foundation :
mathematical concepts are the heart of machine learning.Mathematical topics includes linear equations, matrix rank, sub-spaces, regression, regularization, the singular value decomposition, and iterative optimization algorithms.
Linear Algebra :
1. Essence of linear algebra (3blue1brown)
2. Computational Linear algebra (Rachel Thomas)
3. Linear Algebra -Full College Course (Freecodecamp.org)Calculus :
Calculus helps us to explain the relationships between input and output variables. And Multivariate Calculus comes into the picture when you deal with a lot of features and huge data.
1. The essence of calculus (3Blue1Brown)
2. Calculus (Khan Academy)Probability:
Probability helps us to manage the uncertainty. Uncertainty means working with imperfect or incomplete information. And in Machine Learning, we build predictive models from uncertain data. But we can manage uncertainty using the tools of probability.
Probability and Statistics (Khan Academy)Books:
Mathematics For Machine Learning
Machine Learning Libraries:
Pandas:
documentation : https://pandas.pydata.org/Pandas for Data scienceNumpy:
documentation :https://numpy.org/Sklearn:
documentation :https://scikit-learn.org/stable/index.html
Courses:
After learning and enrolling in countless machine learning courses from various platforms, like Coursera & Udemy I’ve collected the best machine learning courses currently available.
Free:
1.Machine Learning (Coursera) by Andrew Ng.
2.Machine Learning Playlist (Krish Naik)
3.Machine Learning Tutorial (CodeBasics)
4.Machine Learning Tutorial (Freecodecamp)
5.MadewithMl (Goku Mohandas)Paid:
1.Complete Machine Learning & Data Science Bootcamp 2021 (Andrei Neagoie,Daniel Bourke)
2.2021 Python for Machine Learning & Data Science Masterclass (Jose Portilla)
Practice:
“Practice doesn’t make perfect.
Practice reduces the imperfection.”
― Toba Beta, Master of Stupidity.
Kaggle (Best community & large no. of datasets available)
Workera.ai