Machine learning is a core sub-area of artificial intelligence as it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, computer programs, are enabled to learn, grow, change, and develop by themselves.

Artificial intelligence (AI, also machine intelligence, MI) is intelligence displayed by machines, in contrast with the natural intelligence (NI) displayed by humans and other animals. In computer science AI research is defined as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of success at some goal. 

Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using the rules to reach approximate or definite conclusions), and self-correction. Particular applications of AI include expert systems, speech recognition and machine vision.

Training Highlights

  • What “the Internet of Things” means and how it relates to Cloud computing concepts 
  • How open platforms allow you to store your sensor data in the Cloud 
  • The basic usage of the Arduino, RaspberryPi & Nodemcu  environment for creating your own embedded projects at low cost 
  • How to connect your Arduino & RaspberryPi with your Android phone.
  • Basic usage of RaspberryPi.
  • Use of Arduino & RaspberryPi in IoT
  • How to create your own Android App using MIT App Inventor.
  • How to send data to the Internet and talk to the Cloud.
  • How to update sensor readings on Twitter (Social Networking Sites).
  • How to control any device from anywhere across the world.
  • How to connect to cloud ready IoT Server using MQTT.
  • Python, Embedded C, Node.js etc will be covered. 

Topics to be covered in Workshop

Artificial Intelligence

Problem Solving

  • Solving Problems by Searching
  • Beyond Classical Search
  • Adversarial Search
  • Constraint Satisfaction Problems

Knowledge and Reasoning

  • Logical Agents
  • First-Order Logic
  • Inference in First-Order Logic
  • Classical Planning
  • Planning and Acting in the Real World
  • Knowledge Representation

Uncertain Knowledge and Reasoning

  • Quantifying Uncertainty
  • Probabilistic Reasoning
  • Probabilistic Reasoning over Time
  • Making Simple Decisions
  • Making Complex Decisions


  • Learning from Examples
  • Knowledge in Learning
  • Learning Probabilistic Models
  • Reinforcement Learning

Machine Learning

Introduction and ANN Structure

  • Biological neurons and artificial neurons
  • Model of an ANN
  • Activation functions used in ANNs
  • Typical classes of network architectures

Mathematical Foundations and Learning mechanisms

  • Re-visiting vector and matrix algebra
  • State-space concepts
  • Concepts of optimization
  • Error-correction learning
  • Memory-based learning
  • Hebbian learning
  • Competitive learning

Single layer perceptrons

  • Structure and learning of perceptrons
  • Pattern classifier – introduction and Bayes & classifiers
  • Perceptron as a pattern classifier
  • Perceptron convergence
  • Limitations of a perceptrons

Feedforward ANN

  • Structures of Multi-layer feedforward networks
  • Back propagation algorithm
  • Back propagation – training and convergence
  • Functional approximation with back propagation
  • Practical and design issues of back propagation learning

Competitive Learning and Self organizing ANN

  • General clustering procedures
  • Self organizing feature maps
  • Properties of feature maps

Fuzzy Neural Networks

  • Neuro-fuzzy systems
  • Background of fuzzy sets and logic
  • Design of fuzzy stems
  • Design of fuzzy ANNs


  • Bias – Variance tradeoff
  • Regularisation
  • Over-fitting
  • Support Vector Machines
  • Kernel Trick
  • PCA and Kernel PCA
  • K Means Clustering
  • Self-Organization Maps (SOM)
  • Kernel induced vector space
  • Mercer Kernels and Kernel – induced similarity metrics
  • Reinforcement Learning

This will be taught in relation to above topics covered.

  • Logistic and Softmax Regression
  • Sparse Autoencoders
  • Vectorization, PCA and Whitening
  • Self-Taught Learning
  • Deep Networks
  • Linear Decoders
  • Convolution and Pooling
  • Sparse Coding
  • Independent Component Analysis
  • Canonical Correlation Analysis
  • Demos and Applications

Applications & Projects using Python Libraries
A few examples of Neural Network applications, their advantages and problems will be discussed

  • OR Logic, AND Logic & XOR Logic Example using ANN
  • Housing Prizes Prediction
  • Single Line Hypothesis Training
  • Share Market Prediction
  • Marks Prediction
  • Cancer Detection
  • Character Recognition using SVM
  • Automatic Product Classification & Clustering based on Retails Context
  • Predictive Analysis based on Business Housing values
  • Using Datasets available on UCI, github and other opensource platforms 


  • 2 Weeks  (60-70 Hours)
  • 4 Weeks ( In 4 Weeks Training, 2 Weeks (60-70 Hours) will be of Classroom Technical Training same like two week training program & 2 Weeks Time Participants will get for completion of a Project/Research Work).


  • Registration Fee of Rs 1000 to be paid after filling Registration Form through our online payment gateway or through Paytm.
  • Course Fee is Rs 6000 /-. (For More Discounts & Offers please check our Discounts & Offers Page).

 Download Full Course Content in PDF

Our Clients