Target Audience

The course can be taken by:

Students: All students who are pursuing professional graduate/post-graduate courses related to computer science / Information Technology.

Teachers/Faculties: All computer science teachers/faculties who wish to acquire new skills.

Professionals: All working professionals, who wish to enhance their skills.

Course Features

• 24X7 Access: You can view lectures as per your own convenience.
• Online lectures: ~8 hours of online lectures with high-quality videos.
• Hands-on practice: Includes source code files for hands-on practice.
• Updated Quality content: Content is latest and gets updated regularly to meet the current industry demands.

Test & Evaluation

1. During the program, the participants will have to take all the assignments given to them for better learning.

2. At the end of the program, a final assessment will be conducted.

Certification

1. All successful participants will be provided with a certificate of completion (except for demo courses).

2. Students who do not complete the course / leave it midway will not be awarded any certificate.

Topics to be covered

Supervised Learning:

1. Introduction

   This is only a demo course. The full course can be bought here

2. Linear regression
   • Maximum likelihood estimation
   • Regularization/Maximum a posteriori estimation
3. Logistic regression/ Classification
   • Gradient Descent
   • Multiclass classification
4. Support Vector Machine
   • Duality
   • Hard/Soft margin SVM

Unsupervised Learning:

1. Clustering
   • K-means Hard / Soft
   • Expectation Maximization
2. Principal Component Analysis
   • Singular value decomposition

Non-linear methods:

1. Decision trees, Nearest Neighbours (on transformed features)
2. Neural networks
   • Backpropagation
   • Dropout
   • CNN, RNN
3. Kernel learning
   • regression, SVM, k-means, k-NN

Ensemble methods:

• Boosting and Bagging
• Adaboost, Random Forest, Gradient boosting