Artificial Intelligence Training Courses Institute in Hyderabad:

Artificial Intelligence training at Genius IT  is the best in Hyderabad with its focus on hand-on training sessions. The course focuses on the basic and advanced concepts of artificial intelligence such as Deep Networks, Structured Knowledge, Machine Learning, Hacking, Natural Language Processing, Artificial and Conventional Neural Network, Recurrent Neural Network, Self-Organizing Maps, Boltzmann Machines, Auto Encoders, PCA, LDA, Dimensionality Reduction, Model Selection Training institute in Hyderabad and Boosting.

Artificial Intelligence Training in Hyderabad is the simulation of human intelligence by machines and it includes learning, reasoning, self-correction, speech recognition, and machine vision. Artificial intelligence is clearly the future of technology and every company is taking a step ahead to use artificial intelligence to make their products better. It is extensively used in diverse sectors such as healthcare, business, education, finance, law, and manufacturing.

Career Options With AI Training:

  • Game Programmer
  • Algorithm specialists
  • Robotic Scientist
  • Computer Scientist
  • Software Engineer

Welcome to the course!

  • Applications of Machine Learning
  • Why Machine Learning is the Future

Data Preprocessing

  • Python and Data preprocessing (Crash Course – Self paced)

    1. Python Fundamentals
    2. Numpy
    3. Pandas
    4. Data Visualization
    5. Scikit Learn
    6. Data Preprocessing

Using Git and GitHub

  • Setting up Your GitHub Account
  • Configuring Your First Git Repository
  • Making Your First Git Commit
  • Pushing Your First Commit to GitHub
  • Git and GitHub Workflow Step-by-Step

Machine Learning


  1. Simple Linear Regression
  2. Multiple Linear Regression
  3. Bias-Variance trade-off


  1. Logistic Regression
  2. K-Nearest Neighbors (K-NN)
  3. SVM
  4. Decision Trees
  5. Random Forest


  1. K-means
  2. Hierarchical

Dimensionality Reduction

  1. Linear discriminant analysis
  2. Principal component analysis

Natural Language Processing

Natural Language Processing


  1. NLP with NLTK
  2. NLTK extensions and exploration

Sentiment Analyzer

  1. Description of Sentiment Analyzer
  2. Preprocessing: Tokenization
  3. Preprocessing: Tokens to Vectors
  4. Sentiment Analysis using Logistic Regression
  5. Sentiment Lexicons
  6. Regular Expressions
  7. Twitter Sentiment Analysis
  8. Twitter Sentiment Analysis – Regular Expressions
  9. Twitter Sentiment Analysis – KNN, Decision trees, Random forests and Sentiwordnet

Latent Semantic Analysis

  1. Intro to Latent Semantic Analysis
  2. PCA and SVD – The underlying math behind LSA
  3. Latent Semantic Analysis in Python
  4. Advanced LSA

Article spinner

  1. Article Spinning Introduction and Markov Models
  2. Trigram Model
  3. Article spinner in Python

Tensorflow and Neural Networks


  1. Introducing TF
  2. Computation Graph
  3. Tensors
  4. Placeholders and Variables
  5. Neural Networks
  6. Perceptron
  7. Activation Functions
  8. Cost Functions
  9. Gradient Descent Backpropagation

Artificial Neural Networks

  1. Regression in TF
  2. Regression in TF and NN using Estimator
  3. Regression in TF and NN using Keras
  4. Classification in TF
  5. Classification in TF and NN using Estimator
  6. Classification in TF and NN using Keras

Computer Vision

Convolutional Neural Networks

  1. Intro to CNN
  2. Convolution Operation
  3. Activation Layer
  4. Pooling
  5. Flattening
  6. Full Connection
  7. Softmax, Argmax & Cross-Entropy

Basics of Computer Vision and OpenCV

  1. Image Formation
  2. Getting Started with OpenCV
  3. Understanding Color Spaces
  4. Histogram representation of Images
  5. Image Manipulations
  6. Live Sketch App
  7. Identifying Shapes
  8. Counting Circles and Ellipses

Object Detection

  1. Object Detection Overview
  2. How SSD is different
  3. The Multi-Box Concept
  4. Predicting Object Positions
  5. The Scale Problem
  6. Feature Description Theory
  7. Finding Corners
  9. Histogram of Oriented Gradients
  10. Hands-on Object Detection

Face Detection

  1. Face and Eye Detection
  2. Viola-Jones Algorithm
  3. Haar-like Features
  4. Integral Image
  5. Training Classifiers
  6. Adaptive Boosting (Adaboost)
  7. Cascading
  8. Merging Faces (Face Swaps)
  9. Yawn Detector and Counter
  10. Facial Recognition

Motion Analysis and Object Tracking (Ball Tracking)

  1. Filtering by Color
  2. Background Subtraction and Foreground Subtraction
  3. Using Meanshift for Object Tracking
  4. Using CAMshift for Object Tracking
  5. Optical Flow