The course covers Machine Learning in exhaustive way. The presentations and hands-on practical are made such that it's made easy. The knowledge gained through this tutorial series can be applied to various real world scenarios.

UnSupervised learning does not require to supervise the model. Instead, it allows the model to work on its own to discover patterns and information that was previously undetected. It mainly deals with the unlabeled data. The machine is forced to build a compact internal representation of its world and then generate imaginative content.

Supervised learning deals with providing input data as well as correct output data to the machine learning model. The goal of a supervised learning algorithm is to find a mapping function to map the input with the output. It infers a function from labeled training data consisting of a set of training examples.

UnSupervised Learning and Supervised Learning are dealt in-detail with lots of bonus topics.

The course contents are given below:

• Introduction to Machine Learning

• Introductions to Deep Learning

• Installations

• Unsupervised Learning

• Clustering, Association

• Agglomerative, Hands-on

• (PCA: Principal Component Analysis)

• DBSCAN, Hands-on

• Mean Shift, Hands-on

• K Means, Hands-on

• Association Rules, Hands-on

• Supervised Learning

• Regression, Classification

• Train Test Split, Hands-on

• k Nearest Neighbors, Hands-on

• kNN Algo Implementation

• Support Vector Machine (SVM), Hands-on

• Support Vector Regression (SVR), Hands-on

• SVM (non linear svm params), Hands-on

• SVM kernel trick, Hands-on

• SVM mathematics

• Linear Regression, Hands-on

• Gradient Descent overview

• One Hot Encoding (Dummy vars)

• One Hot Encoding with Linear Regr, Hands-on

• Naive Bayes Overview

• Bayes' Concept , Hands-on

• Naive Bayes' Classifier, Hands-on

• Logistic Regression Overview

• Binary Classification Logistic Regression

• Multiclass Classification Logistic Regression

• Decision Tree

• ID3 Algorithm - Classifier

• ID3 Algorithm - Regression

• Info about Datasets