Training for AI Professionals : draft

Dr. Srinivas Padmanabhuni

Dr.Srinivas Padmanabhuni is the Past president of ACM India. Prior to co-founding Tarah Technologies, he was Associate Vice President heading research at Infosys till Oct. 2015. He has rich experience of more than 15 years in IT Industry.

Neelima Vobugari

Neelima Vobugari is the CEO of Tarah Technologies, https://www.tarahtech.com. She is a certified CRM consultant and a certified Data Scientist. She is an alumnus of John Hopkins University, Maryland, where she finished her specialization in Data Sciences.

Nimish Sanghi

Nimish Sanghi is a serial entrepreneur with a passion to learn and put in practice emerging technologies. His current passion is Artificial Intelligence and how to put theory into use as a practitioner. He has helped many a mid level career professional start AI journey through his unique hands-on AI sessions.

1. Week One:

1. Brief Introduction to AI
  - What is AI
  - Use cases
  - Tech Stack
  - AI vs ML vs DL
2. Setting up the development Environment
  - Anaconda
  - Jupyter notebooks
  - Refresher on Python
  - Intro to numpy and Pandas
3. In class coding assignment

2. Week Two:

1. Basics of ML
  - Unsupervised, Supervised and Reinforcement
2. Unsupervised ML at a glance
  - Clustering, Recommendation Systems
  - Code walkthroughs using SkLearn
3. Supervised ML at a glance
  - Classification (Logistic Regression, Decision Trees)
  - Linear Regression
  - Code walkthroughs using SKLearn
4. In class coding assignment

3. Week Three:

1. Basics of Deep Learning
  - Introduction to Neural networks
  - Coding a simple neural network in Keras/Tensorflow
2. Deep dive on Training, Loss functions, gradient descent and back Propagation
3. Strategies to handle Overfitting and Underfitting
4. In class coding assignment( Auto-encoders)

4. Week Four:

1. Computer vision and Deep Learning
  - Introduction to image processing
  - Use cases
2. Convolutional Neural Networks (CNN)
  - Introduction
  - Using OpenCV framework
  - Walkthrough of an image processing example using CNN
3. In class coding assignment on CNN
4. Natural Language Processing (NLP) and Deep Learning
  - Introduction
  - Use cases
5. Recurrent Neural Networks (RNN)
  - Introduction
  - Walkthrough of an NLP use case using RNN
6. In class coding assignment on RNN (Sentiment Analysis)

5. Week Five:

1. Practical Considerations of Machine Learning
  - Overfitting vs underfitting
  - Weight Initialization
  - Early stopping
  - hyperparameter tuning
  - Normalization
  - Dropouts
  - Dataset design and understanding biases in data
  - Training on GPUs vs CPUs
2. Conversational AI
  - Introduction
  - Walkthrough on chatbot code example
3. In class coding assignment – write your own chatbot
4. Emerging areas
  - Attention networks
  - Generative Adversarial Networks (GANs)
5. Introduction to Amazon SageMaker
  - Setting up Amazon Account
  - Development to Deployment workflow
  - Walkthrough of a sample model deployment
6. In class coding project

6. Week Six: Capstone Project , Guided Remotely

Take Home Capstone – Review Offline Over Email – Five Weeks After Training

Post Graduate Program Without Quitting Your Job
Cutting-Edge Curriculum Designed by Industry Experts
Alumni Status from IIIT Bangalore
Career Transition with up to 200% Salary Hike
Hiring Opportunities from Uber, Microsoft, PwC, Genpact, and More

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Program Overview

Key Highlights

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Who Is This Program For?

Job Opportunities

Minimum Eligibility

Instructors

Dr. Srinivas Padmanabhuni

Neelima Vobugari

Nimish Sanghi

Syllabus

1. Week One:

2. Week Two:

3. Week Three:

4. Week Four:

5. Week Five:

6. Week Six: Capstone Project , Guided Remotely

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