## 7 Game-Changing Strategies for Using Cold Emails in Your Data Science Job Search

Do you find yourself drowning in a sea of competition, desperately trying to land your dream job as a data scientist? The job market for

## Probability Recursion Question for DS/ML Interviews (Step-by-Step Simple Solution)

What is a Recursion Relationship? The recursion relationship is very common in both probability and expected value questions for ML or DS interviews. The

## How To Crack the Probability Interview Questions from FAANG Company (with 3 Examples)

As you may know, probability questions are frequently tested in the technical interviews for Data Scientist, Data Analyst even ML Engineer. The test

## NLP Tutorial: Named Entity Recognition using LSTM and CRF

Introduction Named Entity Recognition (NER) is a very classic natural language processing (NLP) problem. The task is to identify the words in a sentence that

## NLP: Word Representation and Model Comparison Tree

The landscape of the NLP (Natural Language Processing) is evolving quickly with new ways to represent text such as word embedding. I would like to

## NLP: how does autocomplete work?

We use text autocomplete everyday, from search engine to writing email. How does the computer know what to suggest as the next word? We will

## NLP: Part-of-speech (POS) tagging with HMM

Part-of-speech (POS) taggin with Hidden Markov Model(HMM) What is POS tagging? Part of Speech (POS) tagging is the process of assigning a part of speech

## What Exactly is the Normal Distribution

The normal distribution is one of the most important concepts in statistics and machine learning/data science space. You may encounter the definition through wikipedia or

## Better than KNN: Approximate Nearest Neighbor (Introduction)

Goal I assume you have heard of the k-Nearest Neighbor algorithm for classification problem (see Tutorial: K-Nearest Neighbor Model). It’s one of the simplest classification algorithm

## Basic Reinforcement Learning Algorithm Decision Tree

How do you choose what algorithms to use in a reinforcement learning settings? The answer can be complicated as it depends on so many factors.