Introduction to Machine Learning

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Introduction to Machine Learning

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About the course

Introduction to Machine Learning is an introductory course to understand different machine learning models such as supervised learning, unsupervised learning, non-linear methods, and ensemble methods. You will also get to know how these models can be used in image recognition, medical diagnostics, translation, speech recognition, and text prediction, etc.

This course covers the standard and most popular supervised learning algorithms including linear regression, logistic regression, decision trees, k-nearest neighbor, support vector machines, and neural networks. It also covers the basic clustering algorithms such as K-means and Principal Component Analysis (PCA).

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 or Information Technology.

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

Professionals: All IT professionals, who wish to acquire new skills or improve their existing skills.

Why learn Machine Learning?

Machine Learning is an application of Artificial Intelligence. It is used to create data models to assist in pattern recognition, which helps organizations in the predictive analysis by using the collected data. As humans become more addicted to machines, we are experiencing a new revolution that is taking over the world. "Machine Learning is the future", according to Google, and it is going to be bright.

Studying machine learning opens up a world of opportunities for any industry that wishes to apply AI in their area. It can also be applied in different fields, for example, image recognition, speech recognition, cybersecurity, face recognition, and medicine, to develop cutting-edge machine learning applications. It is becoming the brain of business intelligence because a lot more and more ML engineers are being recruited. Just as people learn from experience, ML systems learn from data. Learning ML would, therefore, make you more knowledgeable in data science and therefore more competitive in the job market. This course will definitely give a boost to your career and make you confident about the future. If you want to be a highly requested ML specialist, it is worth learning this Machine Learning Course.

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.

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

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

 

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