Artificial Intelligence course Training Institute in Hyderabad

Best Artificial Intelligence Course Training in Hyderabad with Real-Time Experts. We Provide Artificial Intelligence Online Training and Classroom Training in Hyderabad. AI specialists can draw salaries in the range of whopping $300,000 to $500,000. By the end of 2020, 62% of the business will be depending on AI experts for a much better customer engagement and customized chat bots.

✓ Interview Guidance

✓ Mentorship

✓ Experienced Trainers

Dedicated Portal

✓ Practical Sessions

✓ Certifications

✓ Advanced curriculum

✓ Weekly Assignments

✓ Mock Interviews

Enroll here & Get Offer !

“Master Data Science with AI”

“4 Million jobs to be created by 2020”

Artificial Intelligence is a way of making a computer, a computer-controlled robot, or a software think intelligently, in the similar manner the intelligent humans think. It  is a field of Computer Science that gives computers the ability to learn without being explicitly programmed.

Artificial intelligence is a science and technology based on disciplines such as Computer Science, Biology, Psychology, Linguistics, Mathematics, and Engineering.

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AI Job Roles Available

Machine learning engineer
Data scientist
Research scientist
Business intelligence developer
Computer vision engineer

Who to Join AI

Post Graduates
IT Professionals
Data Analysts, Business Analysts
Python Professionals
Also, anyone having interest to learn Artificial Intelligence

AI Course Content

Course Overview

  • Python for Data Analysis
  • Statistics Essentials
  • Machine Learning
  • Deep Learning
  • Robotics
  • Natural Language Processing
  • Reinforcement Learning
  • Artificial Neural Network
  • Expert Systems
  • Fuzzy Systems
  • Computer Vision

Python for Data Analysis

Get acquainted with

  • Data Structures
  • Object Oriented Programming
  • Data Manipulation
  • Data Visualization in Python

Statistics Essentials

Inferential Statistics: Learn Probability Distribution Functions, Random Variables, Sampling Methods, Central Limit Theorem and more to draw inferences
Hypothesis Testing: Understand how to formulate and test hypotheses to solve business problems
Exploratory Data Analysis: Learn how to summarize data sets and derive initial insights


  • Boosting and Bagging
  • Random Forests


  • Value-based methods (e.g. Q-learning)
  • Policy-based methods


  • Neural Network Basics
  • Deep Neural Networks
  • Recurrent Neural Networks (RNN)
  • Deep Learning applied to Images using CNN
  • Tensor Flow for Neural Networks & Deep Learning


  • Clustering (k-means, hierarchical, high-dimensional)
  • Expectation Maximization

Neural Networks

  • Introduction To Machine Learning & Neural Nets
  • Neural Network Architecture
  • Object Recognition With Neural Nets
  • Recurrent Neural Networks

Expert Systems

  • User Interface
  • Inference Engine
  • Knowledge Base


  • Fuzzification Module
  • Knowledge Base
  • Inference Engine
  • Defuzzification Module

Computer Vision

  • Convolutional Neural Networks
  • Keras library for deep learning in Python
  • Pre-processing Image Data
  • Object & face recognition using techniques above


  • Handwriting Detection
  • Sentiment Analysis on Amazon Food Review
  • Image Identification
  • Employee Exit Prediction

Course Highlights

  1. A Dedicated Portal For Practicing.
  2. 1-1 Mentorship
  3. Internship Offers for Freshers.
  4. Weekly Assignments.
  5. Weekly Doubt Sessions
  6. Advanced Curriculum
  7. Certificates On successful Completion of Project .
  8. Resume Preparation Tips
  9. Interview Guidance And Support.
  10. Dedicated HR Team for Job Support And Placement Assistance.

Why Artificial Intelligence ?

  1. To Create Expert Systems: The systems which exhibit intelligent behavior, learn, demonstrate, explain, and advice its users.
  2. To Implement Human Intelligence in Machines: Creating systems that understand, think, learn, and behave like humans.
  3. The goal of AI is to develop computers that can simulate the ability to think, as well as see, hear, walk, talk, and feel.

Real Life Applications of AI

  1. Expert Systems

The expert systems are the computer applications developed to solve complex problems in a particular domain, at the level of extra-ordinary human intelligence and expertise.

Examples: Flight-tracking systems, Clinical systems

  1. Natural Language Processing

Natural Language Processing (NLP) refers to AI method of communicating with an intelligent systems using a natural language such as English.

Examples: Google Now feature, speech recognition, Automatic voice output

  1. Neural Networks Examples

Yet another research area in AI, neural networks, is inspired from the natural neural network of human nervous system.

Examples: Pattern recognition systems such as face recognition, character recognition, handwriting recognition.

  1. Robotics

Robotics is a branch of AI, which is composed of Electrical Engineering, Mechanical Engineering, and Computer Science for designing, construction, and application of robots.

Examples: Industrial robots for moving, spraying, painting, precision checking, drilling, cleaning, coating, carving etc.

5. Fuzzy Logic

Fuzzy Logic (FL) is a method of reasoning that resembles human reasoning. The approach of FL imitates the way of decision making in humans that involves all intermediate possibilities between digital values YES and NO.

Examples: Consumer electronics, automobiles, etc

“Combine the power of Data Science, Machine Learning and Deep Learning to create powerful AI for Real-World applications”

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