Introduction to Hidden Markov Model article provided basic understanding of the Hidden Markov Model. We also went through the introduction of the three main problems of HMM (`Evaluation`

, `Learning`

and `Decoding`

). In this **Understanding Forward and Backward Algorithm in Hidden Markov Model **article we will dive deep into the **Evaluation Problem**. We will go through the mathematical understanding & then will use Python and R to build the algorithms by ourself.

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## Introduction to Hidden Markov Model

Hidden Markov Model is an Unsupervised* Machine Learning Algorithm which is part of the Graphical Models. However Hidden Markov Model (HMM) often trained using supervised learning method in case training data is available. In this introduction to Hidden Markov Model we will learn about the foundational concept, usability, intuition of the algorithmic part and some basic examples. Only little bit of knowledge on probability will be sufficient for anyone to understand this article fully.

## Introduction to Coordinate Descent using Least Squares Regression

Coordinate Descent is another type of optimization algorithm used mainly for ‘strongly convex’ and Lasso Regression function. You are probably aware of Gradient Descent, for solving Least Square Regression. In this Introduction to Coordinate Descent using Least Squares Regression tutorial we will learn more about Coordinate Descent and then use this to solve Least Square Regression.

## How to visualize Gradient Descent using Contour plot in Python

Linear Regression often is the introductory chapter of Machine Leaning and Gradient Descent probably is the first optimization technique anyone learns. Most of the time, the instructor uses a Contour Plot in order to explain the path of the Gradient Descent optimization algorithm. I used to wonder how to create those Contour plot. Today I will try to show how to visualize Gradient Descent using Contour plot in Python.

## Univariate Linear Regression using Octave – Machine Learning Step by Step

**Univariate Linear Regression** is probably the most simple form of Machine Learning. Understanding the theory part is very important and then using the concept in programming is also very critical.In this **Univariate Linear Regression using Octave – Machine Learning Step by Step** tutorial we will see how to implement this using Octave.Even if we understand something mathematically, understanding the implementation can be tedious. Since many professor/researcher uses Octave/MatLab for teaching Machine Learning, it could be very well the the first tool you might be using to understand machine learning.This tutorial will help you to get familiar with Octave/MATLAB and understand the implementation in much easier way, without spending lot of time in Octave/MATLAB.