Tools to crack your data science Interviews
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.
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
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…
Often in data science, we want to understand how one variable is related to another. These variables could be features for an ML model, or sometimes we might want to see how important afeature is in determining the target we are trying to predict. Both covariance and correlation can be used to measure the direction…
K-means Clustering Recap Clustering is the process of finding cohesive groups of items in the data. K means clusterin is the most popular clustering algorithm. It is simple to implement and easily available in python and R libraries. Here is a quick recap of how K-means clustering works. Choose a value of K Initialize K…
What are Word Embeddings? Word embeddings are vector representation of words that can be used as input (features) to other downstream tasks and ML models. Here is an article that explains popular word embeddings in more detail. They are used in many NLP applications such as sentiment analysis, document clustering, question answering, paraphrase detection…
What is the Dartboard Paradox ? Assume your are throwing a dart at dartboard such that it hits somewhere on the dartboard. The dartboard paradox: The probability of hitting any specific point on the dartboard is zero. However the probability of hitting somewhere on the dartboard is 1. How can this be ? ( How…
What is Locality Sensitive Hashing (LSH) ? Locality Sensitive hashing is a technique to enable creating a hash or putting items in buckets such similar items are in the same bucket (same hash) with high probability Dissimilar items are in different buckets – i.e dissimilar items are in the same bucket with low probability. Where…