Applied AI using Deep Learning in Python

Applications of Deep Learning to Computer Vision, Sentiment Analysis, Text Classification, Time Series Analysis etc.


Eligibility Criteria: Participants of this program are expected to have a background in any branch of Engineering and/or Statistics. Some prior coding skills (in any other language will help, although not necessary). Students from non-maths or non-Engineering backgrounds, please DO NOT Enroll. 

Program Highlights:

  • Detailed workshop contents are given in the "Curriculum" tab.
  • Workshop duration: 30 hours of live training sessions.
  • This certified program contains 10 LIVE Instructor-led Training (ILT) sessions, each of 3-hour duration.
  • The workshop includes 5 end-to-end projects.
  • Please note that the video recordings of all the sessions and course materials in the LMS will be available for offline view for till August-end. 

A detailed schedule is given below under course enrolment.

Weekend Evening Batch:  10 sessions of 3 hours each, from July 4th to August 2nd .  

Training session time: 8:30 PM - 11:30 PM IST . Please check the actual session details below.

Welcome aboard, Cheers!

Prashant Sahu (Chief Instructor for the LIVE Sessions)
(B.Tech., PhD at IIT Bombay)
Program Organizer & Chief Instructor
WhatsApp: +91 8169 543 099.
LinkedIn profile:

Classes are conducted on Zoom calls with 24*7 unlimited access to recordings for 9 months

During the training phase, we share solved case studies.

Our curriculum is continuously monitored, reviewed and updated by Industry expert

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Early Bird Offer

Early Bird Offer: Rs. 999/- only (excl. taxes and payment gateway charges) until June 10th. Hurry up!.

Level of Course

We'll start from scratch and go up to more than Intermediate level, but familiarity with Python will be assumed. 


Students and Working Professionals who have an interest in Machine Learning & Artificial Intelligence and their Applications. This workshop is open for ALL (please read the course prerequisite carefully).

Module 1: Introduction to Artificial Neural Networks

  • - Introduction to Neuron & Artificial ArNeural Networks (ANN)
  • - Activation Functions, Loss Functions & Optimizers in DL
  • - ANN Training by Gradient Descent & Back-Propagation
  • - Building Feed-Forward NN using Python & Keras with TensorFlow Background
  • - Using Tensorboard

Module 2: Convolutional Neural Networks

  • - CNN Basics and mathematics.
  • - Implementing Convolutional Neural Networks using TF-Keras
  • - CNN using PyTorch
  • - Optimization of NN (Regularization, Batch Norm, Dropout etc.) & Hyperparameter Tuning
  • - Image Data Augmentation
  • - Transfer Learning in CNN (Resnet50, VGG16 etc.)
  • - Computer Vision Applications using Advanced CNN (R-CNN, Mask CNN, Fast R-CNN etc)
  • - Convolution Filter Visualizations using Saliency Maps etc.

Module 3: Recurrent Neural Networks (RNN)

  • - Recurrent Neural Networks and Vanishing Gradient Problem
  • - Sequence Learning through LSTMs & GRU Cells
  • - Applications LSTMs & GRUs to Sentiment Analysis & Text Classification using BoW and Word Embedding models
  • - Time Series Analysis 

Module 4: Model Deployment

  • - Introduction to Flask REST APIs
  • - Deployment of Deep Learning Models using Flask

I am completely new to Python. Can I join the course?

Simple Answer: NO. Please understand that this assumes that you have a decent familiarity with Python programming language. 

I am from a non-engineering background, but have interest in AI. Can I join the course?

Simple Answer: NO. There will be some mathematics involving Linear Algebra and matrix calculus in a few sessions. Normally engineering students study these concepts in their engineering 1st and 2nd semesters. People from a non-engineering background, BUT GOOD IN PYTHON programming can join, but also remember that there might be some difficulty in understanding the maths part. If you don't care about this, you can go ahead.

What are the prerequisites?

This assumes that you have a decent familiarity with Python programming language. Some knowledge about Machine Learning will be helpful, although not necessary, we'll revise the important relevant concepts anyway.

What is not included in the course?

Some of the Advanced Topics in Deep Learning like unsupervised Deep Learning, Variational Autoencoders, GANs, other Encoder-decoder models, seq-to-seq models, advanced NLP embeddings like BERT, large-scale deployment of Deep Learning models etc.. are excluded. This is strongly advised NOT to go for these topics unless you have understood this course completely, which is the bare minimum requirement. We have a separate advanced Deep Learning course covering the above topics.

Cinque Terre
Prashant Sahu
Freelance Corporate Trainer for Data Analytics | AI & Machine Learning | Python | PhD @ IIT-Bombay,