Mastering Probability for ML: Joint | Marginal | Conditional Probability and Bayes' Theorem

My name is Nitish and you are welcome to my YouTube channel. So two-three days ago I had uploaded a video on my YouTube channel. The name of the video was 'Crush Course on Probability on Machine Learning' and in that video I had said that I will complete this topic on Probability on Machine Learning

in two sessions. So the video I had uploaded was the first session and this video is going to be the second session on this topic. So the goal is that we have to learn probability for machine learning. So in the future, if you ever study anything in machine learning, if you see any concept related to probability, then you should

not face any problem. So in the previous video we had covered the basics like whatever is the basic terminology, there are random variables, we had covered all these topics. In this video, we will go a little deeper and we will cover a very important topic. We will cover types of probability, join probability, marginal

probability, etc. Conditional Probability Then we will study a very important theorem which we call base theorem and then we will also do some other small topics like what are Veda diagrams or what are contingency tables so if you have seen this video and if you have seen the previous video then I am Preeti

show that you will not have any confusion related to probability in the future when you are studying machine learning okay so here that's it let's start the video so let's do a little revision of what we did in the first part and then I will tell you what is our plan of attack today what are we going to study today

okay so in the last session we are basically doing basics we studied some important terms like what is a random experiment what is a trial what is an outcome