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Introduction to Machine Learning

Introduction to Machine Learning

INR₹4,237.00 + GST

SKU: cid_336020 Category:
Learning Outcomes

After completing this course, you will be able to:

  • Understand the fundamental concepts in machine learning and popular machine learning algorithms.
  • Understand the fundamental issues and challenges of machine learning such as data, model selection, model complexity, etc.
  • Understand the strengths and weaknesses of many popular machine learning approaches.
  • Understand the underlying mathematical relationships within and across Machine Learning algorithms and the paradigms of supervised and unsupervised learning.
  • Design and implement various machine learning algorithms in a range of real-world applications.
  • Boost your hireability through innovative and independent learning.
Target Audience

The course can be taken by:

Students: All students who are pursuing professional graduate/post-graduate courses related to computer science and engineering or data science.

Teachers/Faculties: All computer science and engineering teachers/faculties.

Professionals: All working professionals from computer science / IT / Data Science domain.

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.

  1. The access to the course can be extended 3 months at a time (for upto 4 times) just by sending a mail requesting for an extension to the email id in the footer.
  2. The hard copy of the certificate shall be shipped to your registered address or your college
  3. There is no soft copy of the certificate.
  4. To get access to the certificate - you need to take the online exam at the end of the course

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