I am a Computer Engineer , who is interested in understanding and properly interpreting the mathematics behind the ML and DL algorithms
I have worked with some classification algorithms but regression and neural networks greatly confuse me
We learned the basics of some AI algorithms in my B.Engg degree but otherwise there wasn't too much focus on the maths and my probability isn't great either - so i had a lot of problems understanding things like Bayes' Theorem etc
So basically what i am looking for :
To do a proper revision of the relevant topics of Probability and in other areas of Math, which are directly involved in Machine and Deep Learning - so i understand the relevant math first Secondly, i want Understand how Regression, Neural Nets, Deep Learning and Classification algorithms actually work under the hood - how is math helping these algos do what they do For instance, i would like to know what is going on with the Softmax function, used in LGBM and also in Neural Networks OR how does a Decision tree determine the best split OR even how do we know which features offer us the best information gain etc
I hope you get the point
So, can anyone suggest any easy to understand resources - esp for someone who's not a math genius ?
I would be sincerely grateful !
P.S : i am kind of terrible in focusing on books - if there is a video course or interactive course , i would very much appreciate that !
