Hello everyone, welcome to the semicolon. This tutorial is the part of the series data analytics with python. Till now we have learnt about data exploration and data pre-processing. In this video we will be learning about some machine learning models which can help us make predictions with our data. When we have a data set we have generally three types of problems. Regression, classification and clustering. When we have the target variables in continuous space we use regression. Regression is more like finding the equation a curve which fits the data and once we have the equation we can predict the output as desired. some famous algorithms which can be used for regression are linear regression, perceptrons and neural networks. There are many other algorithms as well. Regression is mainly supervised. Some examples of regression are housing prices prediction and stock market predictions. If you want to find which category your data belongs to then it is a classification problem. The classification algorithm mostly find curves which separate the data points into different categories. Labeling sentiment on twitter as positive or negative is a classification problem. Some famous classification algorithms that can be used are support vector machines, neural networks, naive bayes classifiers, logistic regression and KNN. The k-nearest neighbor algorithm. If you just want to group your data without labels it is then a clustering problem. Clustering algorithms work on grouping similar data points together based on the different definitions of similarity. Some famous algorithms are K-means clustering and Agglomerative clustering. Regression and classification are supervised machine learning algorithms. whereas, clustering is unsupervised. Now, with our data and the task we can choose the algorithms accordingly. In this series we have implemented various examples which fall into different categories of regression, clustering and classification. If you found this video useful hit the like button and subscribe for more of these. Thank you.