Artificial Intelligence (AI ) and Deep Learning Advance Training

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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.

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

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