Introduction to Machine Learning with Python (10 hours)

Python and Machine Learning Mastery >> Introduction to Machine Learning with Python (10 hours)
How does Gmail’s spam classifier work? How does Google Photos automatically organize your photos? How do chatbots work? How can we make sales forecasts? How does Netflix recommend new shows for you to see? Can we write a chess program that learns on its own? All of these applications depend on Machine Learning. If you have wondered about how the modern AI and Machine Learning revolution has affected our lives and how you can use it for your own applications, our hands-on course “Introduction to Python and Machine Learning” is for you.  

As the world moves more and more towards digital applications, Artificial Intelligence will become an indispensable part of every company and product. Our course will introduce you to popular machine learning algorithms used in a variety of practical tasks. Not only will you learn about how these methods work, you will also learn how to code them on your own and use them in your applications.  

At the end of the course, you will be able to write Python programs using Machine Learning frameworks for a range of practical problems -- House price prediction, Spam classifier, image classification, Customer clustering, Book recommender, etc. We will be introducing you to state-of-the-art algorithms for these in a practical manner. 

  1. What are Artificial Intelligence and Machine Learning? 
  2. Introduction to Python and important libraries -- Numpy, pandas, etc 
  3. Review of useful mathematics
  4. Basics of Machine Learning
    1. Learning Paradigm
    2. Handling Data
    3. The Machine Learning Pipeline
  1. Python Frameworks for Machine Learning --  scikit-learn, keras, Tensorflow, PyTorch
  2. Supervised Learning with Applications.
a.    Algorithms --  Linear regression, Logistic regression, Deep Neural Networks, Decision Trees, etc
b.    Numerical applications -- Price Prediction, Forecasting, etc
    1. Image Data applications -- Image recognition
    2. Text data applications -- Sentiment analysis, Spam classifier
  1. Unsupervised Learning with Applications
a.    Algorithms -- k-Means, DBSCAN, etc
b.    Clustering algorithms -- Customer clustering, etc
c.     Recommender systems -- Book and movie recommenders, etc
  1. A glimpse of more advanced topics 
Special features of the course
  1. The course will be offered in a mix of Tamil and English so that language is not a barrier to understanding. (If there is demand, a separate pure English version may also be created.)
  2. It will consist of 10+ hrs of lecture videos with practical code walkthroughs.
  3. There will be multiple unsolved exercises given for practice and to build your confidence.
  4. An intensive Final Project at the end of the course for your self-assessment.   
  5. An online Quiz to be taken at the end of the course for obtaining a certificate of completion. 
Instructors -- The instructors are Dr Ganapathy Krishnamurthi and Dr Balaji Srinivasan. Both of them are faculty at IIT-Madras and are co-founders of Inuaid.  

When -- The course will be released SOONThe course will be delivered via  recorded videos which you may watch at your own convenience.

Fee -- The course fee is Rs.3,000/-

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