This course is for people who are enthusiastic about Machine Learning. This course would benefit you if you have Machine Learning as a part of your curriculum or even if you are interested in building Machine Learning Projects.

The course will cover theoretical concepts as well as practical implementations of various Machine Learning Algorithm to give a strong foundation to the basics of Machine Learning.

Topics covered would include:

Hypothesis Space - Find-S Algorithm/Candidate Elimination Algorithm

Decision Trees

Artificial Neural Networks

Bayesian Classifiers

Regression

 

Practical Experiments:

Basics of Python and use if Python to work with CSV files for Data Science. Familiarize with basic ML libraries of Python.

Find-S Algorithm

Candidate Elimination Algorithm

Decision Trees(without API)

Decision Trees(API)

Artificial Neural Networks(without API)

Convolution Neural Networks(using API)

Naive Bayes Classifier(without API)

Naive Bayes Classifier(using API)

K-Nearest Neighbor Algorithm (using API)

K-Means Clustering and EM Algorithm (using API)

Locally Weighted Regression

Artificial Neural Networks(using API)

*Student must bring a laptop with internet connectivity.