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Artificial Intelligence Training in Hyderabad

Become master in data scientist with our Prime Training.
753 ratings
Course Duration

60 Days

Training Options


Course Overview

Tools Covered: R, Python, Jupyter, Spark, H2O, AzureML, Keras, IBM Watson

Pre-requisites: 1.Understanding of AI Tools 2. Solid understanding of Machine learning

What you will learn

  • Data Analytics
  • Data Visualization
  • ML foundation
  • AI Methods: Image data, Speech data, Text Data
  • Deep learning with Keras, Tenserflow & IBM Watson
  • Machine Learning Tools In demand by MNCs
  • Machine Learning Methods with real world case studies

Training provided in both classroom and online training, for classroom training, our institute in ameerpet hyderabad location for best artificial intelligence training.

5 Reasons to Join


Customized syllabus based on industry needs.

Real time

Real time industry experienced trainers.


Limited students per batch.


Resume building Interview preparation.


Dedicated placement support.

Training Options

Online Training

  • Live demonstration of of features and practicals.
  • Get LMS access of each artificial intelligence online training session that you attend through GotoMeeting.
  • Gain guidance on certification.
  • Attend a Free Demo before signing up.

Section 1:

  • Why AI?
  • Introduction
  • How to get data from practice
  • Some Additional Resources!!

Section 2: Reinforcement Learning

  • Fundamentals of Reinforcement Learning

Section 3: Q-Learning Intuition

  • Plan of Attack
  • What is reinforcement learning?
  • The Bellman Equation
  • The “Plan”
  • Markov Decision Process
  • Policy vs. Plan
  • Adding a “Living Penalty”
  • Q-Learning Intuition
  • Temporal Difference
  • Q-Learning Visualization

Section 4: Self-Driving Car (Deep Q-Learning) part 1

  • Part 1 – An Introduction how Self-Driving Car Works (Deep Q-Learning)

Section 5: Deep Q-Learning Intuition

  • Plan of Attack
  • Deep Q-Learning Intuition – Learning
  • Deep Q-Learning Intuition – Acting
  • Experience Replay
  • Action Selection Policies

Section 6: Installation for Part 1

  • Plan of Attack (Practical Tutorials)
  • Where to get the Materials
  • Windows Option 1: End-to-End installation steps
  • Windows Option 2 – Part A: Installing Ubuntu on Windows
  • Windows Option 2 – Part B: Installing PyTorch and Kivy on your Ubuntu VM
  • Mac or Linux: Installing Anaconda
  • Mac or Linux: Installing PyTorch and Kivy
  • Common Debug Tips
  • Getting Started

Section 7: Creating the environment

  • Self Driving Car – (Step 1 to Step 2)

Section 8: Building an AI

  • Self Driving Car – (Step 3 to Step 16)

Section 9: Playing with the AI

  • Self Driving Car – (Level 1 to Level 4)
  • Challenge Solutions

Section 10: Doom (Deep Convolutional Q-Learning)

  • What are convolutional neural networks and Convolutional Q-Learning?

Section 11: Deep Convolutional Q-Learning Intuition

  • Plan of Attack
  • Deep Convolutional Q-Learning Intuition
  • Eligibility Trace

Section 12: Installation for Part 2

  • Where to get the Materials
  • Installing Open AI Gym and ppaquette
  • Installing Open AI Gym Walk through (Mac Version)
  • Installing Open AI Gym Walk through (Ubuntu Version)
  • Common Debug Tips

Section 13: Building an AI

  • Doom – (Step 1 to Step 17)

Section 14: Playing with the AI

  • Watching our AI play Doom

Section 15:  A3C

  • Introduction

Section 16: A3C Intuition

  • Plan of Attack
  • The three A’s in A3C
  • Actor-Critic
  • Asynchronous
  • Advantage
  • LSTM Layer

Section 17: Installation for Part 3

  • Installing OpenCV

Section 18: Building an AI

  • Breakout – (Step 1 to Step 15)

Section 19:  Artificial Neural Networks

  • What is Deep Learning?
  • Plan of Attack
  • The Neuron
  • The Activation Function
  • How do Neural Networks work?
  • How do Neural Networks learn?
  • Gradient Descent
  • Stochastic Gradient Descent
  • Backpropagation

Section 20: Convolutional Neural Networks

  • Plan of Attack
  • What are convolutional neural networks?
  • Step 1 – Convolution Operation
  • Step 1(b) – ReLU Layer
  • Step 2 – Pooling
  • Step 3 – Flattening
  • Step 4 – Full Connection
  • Summary
  • Softmax & Cross-Entropy

-) More and more you will learn in Deep learning Course.
-) Last two sections, are just how much is required prerequisites for Artificial Intelligence.

Upcoming Batch Schedules

Data Science06-Dec-202106:00 PM - 07:00 PM120 DaysMr. RanadeepOnline_Course

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