## Q-learn: solving Knight and Princess problem from scratch with Q-learn algorithm

This is another post on Q-learning reinforcement algorithm. Here we will understand and implement the Knight & Princess problem from the scratch using Python language, where the Knight need to move to the place of Princess ignoring the enemies on its way. In this post I have not used any third party library such as gym. Read more…

## Understanding Q-learning algorithm with example using Python

Q-learning is a reinforcement learning technique in Machine learning used to answer the question “what action to take under what circumstances”. This model-free machine learning technique is used mainly in implementing any finite Markov Decision Process. The application of reinforcement learning technique can be found in traffic light control, robotics, recommendation system, gaming etc.
Before knowing Q-learning algorithm, let’s first understand the motivation behind this. What kind questions can be answered by Q-learning algorithm? Read more…

## Difference between Regression and Classification in Machine Learning

In machine learning, both regression and classification are all about making a prediction. In regression, the model predicts a quantity or a numerical value. On the other hand, in classification, the model predicts a label.

## Difference between Classification and Clustering in Machine Learning

 Classification Clustering What is it? Given a set of historical/old data along with their class name and a set of new data, classification is the process of assigning each new data with class name that is obtained from the old/historical data. Given a set of samples/data, clustering is the process of grouping the data based on their similarities and the patterns of the data. Type of input data Set of data with label/class name Only the data set. No labeling is required. Input A set of samples or data A set of classes A set of samples

## Basic Panda’s functions for dataset manipulation in Python

Handling a small amount of data is not a big deal. But how about if somebody gives you a large volume of data to reorganize. Data can be census data of all the countries, it can be astronomical data, or can be a large volume of medical image data which can have the size of around a few terabytes. For this Panda can be one of the most suitable option. it has the broader goal of becoming the most powerful and flexible open source data analysis/manipulation tool available in any language. Here are some of the most basic commands that you can use while handling your large dataset. Continue reading….

## Classification using Naive Bayes Algorithm using Python

Data Classification mainly refers to a way of organizing/categorizing the data by assigning a label/class to a set of data. For example, from the age, we can the class Infants, children, adolescents, adult, or older adult to a person. It is very easy to classify if the data is very small or the feature set (here it is only the age of the person) is very small. What will happen, if the amount of data is very large? We can use different ML algorithms to perform the classification task on a large dataset with hundreds of the features. In this post, we will learn how to classify a set of data using the popular supervised Naive Bayes algorithm using Python language. Continue reading…