Machine learning

Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.

Broadly, there are 3 types of Machine Learning Algorithms..

Supervised Learning

How it works: This algorithm consist of a target / outcome variable (or dependent variable) which is to be predicted from a given set of predictors (independent variables). Using these set of variables, we generate a function that map inputs to desired outputs. The training process continues until the model achieves a desired level of accuracy on the training data.

Examples of Supervised Learning

  1. Linear Regression
  2. Logistic Regression
  3. Random Forest

Unsupervised Learning

How it works: In this algorithm, we do not have any target or outcome variable to predict / estimate.  It is used for clustering population in different groups, which is widely used for segmenting customers in different groups for specific intervention.

Examples of Unsupervised Learning

K-means Clustering  

Reinforcement Learning:

How it works:  Using this algorithm, the machine is trained to make specific decisions. It works this way: the machine is exposed to an environment where it trains itself continually using trial and error. This machine learns from past experience and tries to capture the best possible knowledge to make accurate business decisions.

Example of Reinforcement Learning

Markov Decision Process

The aim of building this website is to create one-stop shop for all the data science topics which are discussed in the present machine learning community.

This website will be launched in a phased manner. Firstly, I will focus on the topics helpful in building regression & classification models for tabular data.

  1. Linear algebra for machine learning
  2. Feature Engineering Guide
  3. Miscelaneous topics in conventional machine learning

In the next phase, we will shift our focus to the Deep Learning related topics and cover

  1. Convolutional Neural networks
  2. Intuition behind neural networks
  3. Sequence models
  4. Attention and Transformers