Tools to crack your data science Interviews
Bayesian Logistic Regression In this video, we try to understand the motivation behind Bayesian Logistic regression and how it can be implemented. Recap of Logistic Regression Logistic Regression is one of the most popular ML models used for classification. It is a generalized linear model where the probability of success can be expressed as a…
Do we need optimization for Machine Learning ? The answer is No and Yes. If you are a beginner, just getting started, you can build a fair bit of stuff without in depth knowledge of an advanced topic such as optimization. However, if you are looking to be an expert, where you write custom models,…
A lot of things we do in the ML pipeline involve vectors and matrices Linear Algebra helps us understand how these vectors interact with each other, how to perform vector & Matrix operations. This video talks about whether we need Linear Algebra for Machine Learning.
How do you free unwanted memory in python ? Often, our programs run out of memory when we are trying to build models. How can we free some of the unwanted memory to get our programs running again? This video talks about ways to free unwanted memory in python. The video briefly explores garbage collection…
Many of us have heard of bagging and boosting, commonly used ensemble learning techniques. This video describes ways to combine multiple dissimilar ML models through voting, averaging and stacking to improve the predictive performance.
This video explains Bayesian Modeling : Why do we need Bayesian Modeling? What is Bayesian Modeling? What are some examples where we can practically use Bayesian Modeling ? Check out https://www.tensorflow.org/probability for code examples. We will have more videos and articles explaining Bayesian extensions of popular models shortly… bayesian_complete_short
Here is a brief video on why we need to learn Probability and Statistics for a career in Machine Learning.
Probabilistic Models help us capture the inherant uncertainity in real life situations. Examples of probabilistic models are Logistic Regression, Naive Bayes Classifier and so on.. Typically we fit (find parameters) of such probabilistic models from the training data, and estimate the parameters. The learnt model can then be used on unseen data to make predictions….
Machine Learning models often give us unexpected and biased outcomes if the underlying data is biased. Very often, our process of collecting data is incomplete or flawed leading to data often not being representative of the real world. In this article, we see why we need to measure fairness for ML models and how we…
Python is compiled into bytecode (pyc file) and then interpreted- making it slower than languages such as C/C++ that are directly compiled. Cython is a tool to write compiled C extension for python to speed up code. Cython is easy to use and often leads to significant improvement in runtime. In this article, lets walk…