Simply, what are the bias and variance? Why is there a tradeoff between them? Listen to this episode to learn about this fundamental topic which is usually asked about during data science and machine learning job interviews.

What is design of experiments? Student t-test? ANOVA? These are really simple to explain. Listen to this episode to learn about them.

What hardware do I need to run ML deep learning models? For prototyping.

How is the computationally intensive deep learning accomplished on modern hardware computers ? A basic introduction.

How can we compare one topic model to another? How can we evaluate any one of them?

Now is time to discuss non linear classifiers, which provide flexibility in modeling more complex patterns in data. We start by a conceptual intro to decision trees in this short episode

The bird's eye view on classifiers. In this episode, we walk thru the landscape and start with linear classifiers. In the next episode, we will go thru non-linear classifiers isA.

We describe the 5 steps of data analysis. Speak of common steps and the pitfalls.

Topic modelling part 1, LSA

How do you build a NLP systems? What are the system level parameters you should consider ? How to choose one algorithm over another? And how to evaluate NLP systems. This episode is about answering these questions.