courses for researchers

Module Introduction

  1. Introduction to the Course
  2. Defining and Learning Important Vocabulary
  3. Demystifying Data Science, Decision Science, AI, ML and DL            
  4. Overview of the tools

Module 2 Basic of python

  1. Python Quick Start and Setting Up Python
  2. General Syntax        
  3. Variables Objects and Values           
  4. Conditionals & Loops           
  5. Operators & Regular Expressions  
  6. Exceptions
  7. Functions & Classes              
  8. String Methods       
  9. Containers 
  10. File IO         
  11. Debugging
  12. Web Applications development      

 

Module 3 Machine Learning

  1. Introduction to Machine Learning
  2. Supervised Machine Learning
  3. Unsupervised Machine Learning
  4. Reinforcement learning
  5. Evaluating Machine Learning models
  6. Regularization and Hyperparameter tuning
  7. Ensemble Modelling
  8. Exploratory Data Analysis & Feature Engineering
  9. Approach for model development, evaluation and optimization
  10. Mathematical Optimization

Module 4 Student Projects Agenda

  1. Trainee Project use-case overview
  2. Defining the problem statement
  3. Solution blueprint development
  4. Explore & define the machine learning use-case
  5. Course Project (Initial) Agenda
  6.  Course Project (Final) Agenda

Module 5 Leveraging ML to Related Technologies

  1. Blockchain
  2. IoT
  3. Multidisciplinary Research

Module 6 Deep Learning

  1. Convolutional Neural Network (CNN)
  2. Recurrent Neural Networks (RNNs)
  3. Long Short-Term Memory Networks (LSTMs)
  4. Stacked Auto-Encoders
  5. Deep Boltzmann Machine (DBM)
  6. Deep Belief Networks (DBN)
  7. Generative Adversarial Networks (GANs)
  8. Deep Reinforcement Learning

Module 7 Disseminating project results

  1. Disseminating project results through Research Paper
  2. Research methodology and Vocabulary
  3. Writing Research Paper
  4. Deploying as software application