Introduction to Machine Learning (Demo)

Introduction to Machine Learning (Demo)


There is no certificate in the demo course

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

18% GST Extra

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SKU: cid_133566 Category:
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.


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


No prerequisite

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