Introduction to Machine Learning

Introduction to Machine Learning


Please register to enroll for this course.

SKU: cid_51750 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

Each lecture will have a quiz containing a set of multiple choice questions. Apart from that, there will be a final test based on multiple choice questions.

Your evaluation will include the overall scores achieved in each lecture quiz and the final test.

No prerequisite

Topics to be covered

Supervised Learning:

  1. Linear regression
    • Maximum likelihood estimation
    • Regularization/Maximum a posteriori estimation
  2. Logistic regression/ Classification
    • Gradient Descent
    • Multiclass classification
  3. 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


  1. Upto six weeks (or till submission of the final quiz) access to the course
  2. To get access to the certificate - you need to take the online exam at the end of the course