Training & Internship - Data Science and Machine Learning using Python

3 Months


Intensive training and Internship program for Data Science and Machine Learning for 3 months. Starting with the elementary lessons in programming languages like Python used for Data Science and Machine Learning and the Foundation courses in Statistics needed for Machine Learning, we progress thoroughly into the area of Machine Learning and its applications.





 3 Months   


 Start date 

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 Training   Options   



 Timings for online class


 3 hours Class room sessions on Weekdays 


      Session #1: 9.30 am - 12.30 pm (3 hrs)


      Session #2: 1:30 pm - 4.30 pm (3 hrs)





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 Please register using the form at the end of this page.



 Target   Audience   



 Bachelors or Masters graduates in Computer Science/IT or Working professionals who are looking to start their career   in Artificial Intelligence, Machine Learning & Data Science.

 Final Year students who would like to do their Training & Internship on Machine & Data Science.


Course Contents:

Module #1: Python for Machine Learning & Data Science 


  • Python Basic Operations, Variables, Data Types, Functions, Important Packages, Control flow, Conditional programming
  • Plotting with Matplotlib
  • List & Dictionaries
  • Solving world of Arrays with powerful Numpy
  • Empowering Data Analysis with Pandas

Module #2: Mathematics for Machine Learning & Data Science  - Applied Statistics


  • Statistics of Datasets - Mean, Variance, Positive definite Matrices
  • Covariance
  • Inner Products
  • Orthogonal Projections
  • PCA
  • Bayesian Statistics


Module #3: Machine Learning (1 Month)


Topics in Machine Learning module include:


It will make you understand not only the theoretical underpinnings of the machine learning algorithms, but also the practical know-how of building and applying these techniques yourself. Application problems will be using capstone projects in major application fields like text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas. At the end of the course, you would be expected to do an internship project in which you would be taking up a relevant industry problem in ML and finding solutions for it just as would do in a company. This is for making you industry ready and get you the feel of working as a ML scientist/engineer. Throughout the course, you would get excellent mentoring and technical helps if required, from the mentors, who can also help you find the relevant industrial applications.


  • Introduction to Machine Learning
  • Steps in ML- data preparation, learning, testing, prediction
  • Introduction to Supervised learning and Unsupervised Learning
  • Model Parameters and Cost Function Optimization
  • Linear Regression with single and multiple variables to address predictive problem of ML
  • Logistic Regression- Classification
  • K-Nearest Neighbours 
  • Naive Bayes Classifier
  • Support Vector Machine (SVM)
  • Decision Trees
  • K-means Clustering
  • Splitting dataset and Cross-Validation 
  • Hyper-parameter Tuning
  • Discussion on Ensemble Methods - Random Forest, Bagging and Boosting 
  • Applications : Image Processing/Computer Vision, Text Processing/NLP, Recommender Systems (one or more example problems from the list)

Module #4: Internship Projects ( 1 Month)  - Capstone Project


Our mentors will give students an opportunity to develop an AI, ML solution to solve a real life problem. We will provide the datasets to choose from the Machine Learning application fields such as pattern recognition, image processing, computer vision, text processing, natural language processing etc. Our mentors will provide the necessary guidance and mentoring for application development.

Here is the tentative list of internship projects that students will be doing:

  • Expense reimbursement document verification 
  • Music genre classification
  • Self driving car - object detection
  • Teleco/Banking customer churn prediction
  • Fraud detection
  • Network intrusion detection
  • Medical image processing
  • Automated support in banking using chatbots
  • Recommendation system - Movie recommendation
  • Recommendation system - E-commerce product recommendation
  • Cancer detection
  • Autism detection
  • AI based personal assistant & organizer
  • Image duplication detection
  • Resume matching, recruitment assistant
  • Character recognition using ocr
  • NLU Chatbot for websites
  • Automated medicine stock maintenance and delivery module
  • Movie review, rating module
  • Soil and air quality monitoring system
  • Botanical name identifier
  • Predict estimated lifespan of a person
  • Speaking accent/nativity identification


Recorded video of online training session: