Machine Learning

Task Checklist for Almost Any Machine Learning Project

A cheat sheet of tasks and things to take care of for every end-to-end ML projects. In this, I write down a check list...

Data Science Roadmap – How to become a Data Scientist? (6 month self study plan)

Today, I discuss the Data Science Roadmap, the missing guide to self study machine learning. I’ll discuss what exactly you need to know and...

Why learn the math behind Machine Learning and AI?

Why learn the math behind machine learning algorithms when you can readily implement it using the python libraries like scikit-learn, h2o, statsmodels etc? This...

Mistakes programmers make when starting machine learning

Today, I want to discuss some of the common mistakes that programmers make when starting to learn machine learning. But first, let me speak...

Machine Learning Use Cases – The Big List of Real World Applications by Vertical and Industry

The use cases of machine learning to real world problems keeps growing as ML/AI sees increased adoption across industries. However, there are certain core...

Main Pitfalls in Machine Learning Projects

Not all Data Science projects that get kicked-off see the light of day. While this is true for any project, there are common reasons...

Regression Coefficient Formula

  Let’s understand the formula for the linear regression coefficients. That is the formula for both alpha and the beta. Now, if you have...

Linear Regression in Machine Learning – Clearly Explained

    In this lesson, I introduce what Linear regression is all about. Linear Regression is a foundational algorithm for machine learning and statistical...

Linear Regression in Machine Learning – Clearly Explained

Understanding linear regression. Let’s understand what linear regression is all about from a non-technical perspective, before we get into the details, we will first...

Nebullvm – Tutorials and benchmarks on Nebullvm, the open-source deep learning inference accelerator

Nebullvm is an open-source library that takes a deep learning model as input and outputs an optimized version that runs 5-20 times faster on...

Simulated Annealing Algorithm Explained from Scratch (Python)

An Introduction to Gradient Boosting Decision Trees

Gradient Boosting is a machine learning algorithm, used for both classification and regression problems. It works on the principle that many weak learners(eg: shallow...

Bias Variance Tradeoff – Clearly Explained

Bias Variance Tradeoff is a design consideration when training the machine learning model. Certain algorithms inherently have a high bias and low variance and...

Gradient Boosting – A Concise Introduction from Scratch

Gradient Boosting is a machine learning algorithm, used for both classification and regression problems. It works on the principle that many weak learners (eg:...

Portfolio Optimization with Python using Efficient Frontier with Practical Examples

Portfolio optimization in finance is the technique of creating a portfolio of assets, for which your investment has the maximum return and minimum risk....

TensorFlow vs PyTorch – A Detailed Comparison

Compare the popular deep learning frameworks: Tensorflow vs Pytorch. We will go into the details behind how TensorFlow 1.x, TensorFlow 2.0 and PyTorch compare...

Principal Component Analysis – How PCA algorithms works, the concept, math and implementation

Principal Components Analysis (PCA) is an algorithm to transform the columns of a dataset into a new set of features called Principal Components. By...

Feature Selection – Ten Effective Techniques with Examples

In machine learning, Feature selection is the process of choosing variables that are useful in predicting the response (Y). It is considered a good...

Caret Package – A Practical Guide to Machine Learning in R

Caret Package is a comprehensive framework for building machine learning models in R. In this tutorial, I explain nearly all the core features of...