AI & Deep Learning

Instructor-led Live Classes

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New Batch starts from 9th January 2021

Course brief

In this course, we will learn about, Machine Learning and algorithms, machine learning scalable on Big data using Apache Spark, Deep Learning and Neural Networks using Keras, Deep learning models with TensorFlow, Deep Learning and Computer Vision and Fundamentals of Reinforcement learning. During this instructor-led Training, you will be having live interaction with the instructor, and at the end of the course, you will be dealing with a real-time project. Based on the performance you will be issued a course completion certificate and also you will get access to our classroom portal(LMS) where you can access course content, class recordings and quizzes.

7 weeks
Basics of Python


₹20000 13%

Why learn AI & Deep Learning?

The importance of Artificial Intelligence and Deep Learning has been increasing as a growing number of companies are using these technologies to improve their products and services, estimate their business models, and intensify their decision-making process. There are many job opportunities. Many companies like Google, Microsoft and Apple rely on Artificial Intelligence and Deep Learning. AI and deep learning is a shining star of the movement.

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What you will learn

Lesson 1
What is Deep Learning?
  • Need for Data Scientists
  • Foundation of Data Science
  • What is Business Intelligence
  • What is Data Analysis
  • What is Data Mining
Lesson 2
What is Machine Learning?
Analytics vs Data Science
  • Value Chain
  • Types of Analytics
  • Lifecycle Probability
  • Analytics Project Lifecycle
  • Reasons for Deep Learning
  • Real-Life use cases of Deep Learning
  • Review of Machine Learning
Lesson 3
Deep Learning & AI using Python
  • Deep Learning & AI
  • Case Study
  • Deep Learning Overview
  • The Brain vs Neuron
  • Introduction to Deep Learning
Lesson 4
Introduction to Artificial Neural Networks
  • The Detailed ANN
  • The Activation Functions
  • How do ANNs work & learn
  • Gradient Descent
  • Stochastic Gradient Descent
  • Backpropagation
  • Understand limitations of a Single Perceptron
  • Understand Neural Networks in Detail
  • Illustrate Multi-Layer Perceptron
  • Backpropagation – Learning Algorithm
  • Understand Backpropagation – Using Neural Network Example
  • MLP Digit-Classifier using TensorFlow
  • Building a multi-layered perceptron for classification
  • Why Deep Networks
  • Why Deep Networks give better accuracy?
  • Use-Case Implementation
  • Understand How Deep Network Works?
  • How Back propagation Works?
  • Illustrate Forward pass, Backward pass
  • Different variants of Gradient Descent
Lesson 5
Convolutional Neural Networks
  • Convolutional Operation
  • Relu Layers
  • What is Pooling vs Flattening
  • Full Connection
  • Softmax vs Cross Entropy
  • ” Building a real world convolutional neural network
  • for image classification”
Lesson 6
What are RNNs – Introduction to RNNs
  • Recurrent neural networks rnn
  • LSTMs understanding LSTMs
  • long short term memory neural networks lstm in python
Lesson 7
Restricted Boltzmann Machine (RBM) and Autoencoders
  • Restricted Boltzmann Machine
  • Applications of RBM
  • Introduction to Autoencoders
  • Autoencoders applications
  • Understanding Autoencoders
  • Building a Autoencoder model
Lesson 8
Tensorflow with Python
  • Introducing Tensorflow
  • Introducing Tensorflow
  • Why Tensorflow?
  • What is tensorflow?
  • Tensorflow as an Interface
  • Tensorflow as an environment
  • Tensors
  • Computation Graph
  • Installing Tensorflow
  • Tensorflow training
  • Prepare Data
  • Tensor types
  • Loss and Optimization
  • Running tensorflow programs
Lesson 9
Building Neural Networks using Tensorflow
  • Tensors
  • Tensorflow data types
  • CPU vs GPU vs TPU
  • Tensorflow methods
  • Introduction to Neural Networks
  • Neural Network Architecture
  • Linear Regression example revisited
  • The Neuron
  • Neural Network Layers
  • The MNIST Dataset
  • Coding MNIST NN
Lesson 10
Deep Learning using Tensorflow
  • Deepening the network
  • Images and Pixels
  • How humans recognise images
  • Convolutional Neural Networks
  • ConvNet Architecture
  • Overfitting and Regularization
  • Max Pooling and ReLU activations
  • Dropout
  • Strides and Zero Padding
  • Coding Deep ConvNets demo
  • Debugging Neural Networks
  • Visualising NN using Tensorflow
  • Tensorboard
Lesson 11
Transfer Learning using Keras and TFLearn
  • Transfer Learning Introduction
  • Google Inception Model
  • Retraining Google Inception with our own data demo
  • Predicting new images
  • Transfer Learning Summary
  • Extending Tensorflow
  • Keras
  • TFLearn
  • Keras vs TFLearn Comparison


Can I attend this course online?

Yes. This course is completely online

Will I get a certificate after completing the course?

Yes. On successful completion of this course, we will be issuing you a Course Completion Certificate

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