Artificial Intelligence (AI) and Deep Learning Advance Training

Overview
Course Content
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The Artificial Intelligence Course has been designed to develop the insight of the candidates on Data Science. In this training of Artificial Intelligence - Learn How To Build An AI the candidates would learn how to optimize the AI to reach its maximum potential in the real world and in the live scenarios. The training modules will definitely make the candidates understand the theory behind Artificial Intelligence and helps them to understand how to resolve the Real World Problems with AI. Moreover, the candidates will also get the chance to create an an AI to beat games along with the virtual self driving cars. The training open enormous gates of job opportunities for the candidates.

The Artificial Intelligence Training helps the candidates in:

Understanding the concepts behind AI.
How AI can be optimized so that the maximum potential could be obtained.
The beginners learn how the codes come is combined and what lines mean?
Understanding how to create the environment for self driving Car
Understanding the procedure of building the AI
Understanding how could a trainee provide support to the Data Scientist
Earning fame in the workplace with handsome salary
Learn how to build AI that is adaptable to any environment in real life
How to build AI with no previous coding experience using Python

Target audience

Anyone, who is interested in Artificial Intelligence, Machine Learning or Deep Learning would opt for this course.

Prerequisites

The candidates should have well in High School Maths.

AI Basics (Theory)

Introduction and scope
Understanding Intelligent agents
Problem Solving e.g Adversarial Search
Knowledge representation
Probabilistic reasoning

Machine Learning foundation

Supervised Learning
Unsupervised Learning - SVM
Decision trees - Clustering
Artificial Neural network
Practical ML (hands on)

Deep Learning foundation

Introduction, motivation for deep learning
Set up Anaconda, Jupyter Notebooks
Applying Deep learning
Regression

Neural networks

Maths refresher, Introduction to NumPy, Tensorflow - introduction
Introduction to Neural Network : Covers in details Perceptron, Gradient descent, multilayered perceptron, Back propagation
Build your first neural network
Model evaluation and validation
Project : Sentiment Analysis of movie reviews
Develop a mini version of neural network library like Tensorflow

Convolutional Neural Networks

Intro to Tensorflow
Using Cloud computing hardware for computations
Deep Neural networks
CNN - theory, how and why does it works ?
Project : Build an Image classifier
Image generation

Recurrent Neural Networks

Introduction to RNN
Long Short Term memory
LSTM Cell - detailed walkthrough and implementation
Build a RNN
Mini Project : Stock price prediction (self proctored)
Embeddings and Word2Vec
Sentiment prediction RNN
Text summarization - using Keras
Project : Generate TV Scripts
Designing a Chatbot
Buid a language translator

Generative Adversarial Networks (advanced)

Introduction to GAN
How GANs work
Games and Equilibria
Build and train a GAN
Project: Generate faces : Build 2 NNs to compete with each other to generate realisitic human faces