Artificial Intelligence Training in Pune

Artificial Intelligence Training in Pune

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Artificial Intelligence Training in Pune

About the Course

Artificial Intelligence training at ETLhive is the best in Pune 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, AutoEncoders, PCA, LDA, Dimensionality Reduction, Model Selection and Boosting. Further, the artificial intelligence course at ETLhive will help you target the best jobs in the market in the field and not only this you can certainly increase your chances of employability by gaining more knowledge on the associated technologies such as IoT and Robotics. As a fact of the matter, Artificial Intelligence 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.

Intended Audience for Artificial Intelligence Training Course

This course is ideal for individuals having interest in Artificial Intelligence, Machine Learning, or Deep Learning. In other words, this course is meaningful for Python Developers, Robotics Engineer, and Fresh Graduates.

Prerequisites for Artificial Intelligence 

As such, there are no technical prerequisites for artificial intelligence. However, knowledge of python and mathematical aptitude will be beneficial. 

Our Courses


Artificial Intelligence Course Content

 

Artificial Intelligence

  • An Introduction to Artificial Intelligence
  • History of Artificial Intelligence
  • Future and Market Trends in Artificial Intelligence
  • Intelligent Agents – Perceive-Reason-Act Loop
  • Search and Symbolic Search
  • Constraint-based Reasoning
  • Simple Adversarial Search (Game-Playing)
  • Neural Networks and Perceptrons
  • Understanding Feedforward Networks
  • Boltzmann Machines and Autoencoders
  • Exploring Backpropagation

 

Deep Networks and Structured Knowledge

  • Deep Networks/Deep Learning
  • Knowledge-based Reasoning
  • First-order Logic and Theorem
  • Rules and Rule-based Reasoning
  • Studying Blackboard Systems
  • Structured Knowledge: Frames, Cyc, Conceptual Dependency
  • Description Logic
  • Reasoning with Uncertainty
  • Probability & Certainty-Factors
  • What are Bayesian Networks? 
  • Understanding Sensor Processing
  • Natural Language Processing
  • Studying Neural Elements
  • Convolutional Networks
  • Recurrent Networks
  • Long Short-Term Memory (LSTM) Networks 

 

 

Machine Learning and Hacking

  • Machine learning
  • Reprise: Deep Learning
  • Symbolic Approaches and Multiagent Systems
  • Societal/Ethical Concerns
  • Hacking and Ethical Concerns
  • Behaviour and Hacking
  • Job Displacement & Societal Disruption
  • Ethics of Deadly AIs
  • Danger of Displacement of Humanity 
  • The future of Artificial Intelligence

 

 

Natural Language Processing

  • Natural Language Processing 
  • Natural Language Processing in Python
  • Natural Language Processing in R
  • Studying Deep Learning
  • Artificial Neural Networks
  • ANN Intuition
  • Plan of Attack
  • Studying the Neuron
  • The Activation Function
  • Working of Neural Networks
  • Exploring Gradient Descent
  • Stochastic Gradient Descent
  • Exploring Backpropagation

 

 

 

Artificial and Conventional Neural Network

  • Understanding Artificial Neural Network
  • Building an ANN
  • Building Problem Description
  • Evaluation the ANN
  • Improving the ANN
  • Tuning the ANN
  • Conventional Neural Networks
  • CNN Intuition
  • Convolution Operation
  • ReLU Layer
  • Pooling and Flattening
  • Full Connection
  • Softmax and Cross-Entropy 
  • Building a CNN
  • Evaluating the CNN
  • Improving the CNN
  • Tuning the CNN

 

 

 

Recurrent Neural Network

  • Recurrent Neural Network
  • RNN Intuition
  • The Vanishing Gradient Problem
  • LSTMs and LSTM Variations
  • Practical Intuition
  • Building an RNN
  • Evaluating the RNN
  • Improving the RNN
  • Tuning the RNN

 

 

 

Self-Organizing Maps

  • Self-Organizing Maps
  • SOMs Intuition 
  • Plan of Attack
  • Working of Self-Organizing Maps
  • Revisiting K-Means
  • K-Means Clustering
  • Reading an Advanced SOM
  • Building an SOM
 

Boltzmann Machines

  • Energy-Based Models (EBM)
  • Restricted Boltzmann Machine
  • Exploring Contrastive Divergence
  • Deep Belief Networks
  • Deep Boltzmann Machines
  • Building a Boltzmann Machine
  • Installing Ubuntu on Windows
  • Installing PyTorch
 

AutoEncoders

  • AutoEncoders: An Overview
  • AutoEncoders Intuition
  • Plan of Attack
  • Training an AutoEncoder
  • Overcomplete hidden layers
  • Sparse Autoencoders
  • Denoising Autoencoders
  • Contractive Autoencoders
  • Stacked Autoencoders
  • Deep Autoencoders
 

PCA, LDA, and Dimensionality Reduction

  • Dimensionality Reduction
  • Principal Component Analysis (PCA)
  • PCA in Python
  • PCA in R
  • Linear Discriminant Analysis (LDA)
  • LDA in Python
  • LDA in R
  • Kernel PCA
  • Kernel PCA in Python
  • Kernel PCA in R
 

Model Selection and Boosting

  • K-Fold Cross Validation in Python
  • Grid Search in Python
  • K-Fold Cross Validation in R
  • Grid Search in R
  • XGBoost
  • XGBoost in Python
  • XGBoost in R

New Batches

Type Demo Session New Batches Location
Weekend 20-Oct-2018 12PM 21-Oct-2018 Pimple Saudagar
Weekend 20-Oct-2018 12PM 21-Oct-2018 Nal Stop
Weekend 20-Oct-2018 12PM 21-Oct-2018 Kharadi
Weekend 27-Oct-2018 12PM 28-Oct-2018 Pimple Saudagar
Weekend 27-Oct-2018 12PM 28-Oct-2018 Nal Stop
Weekend 27-Oct-2018 12PM 28-Oct-2018 Kharadi


Artificial Intelligence Course Features

 

 

One Stop Solution!

   Ability to attend missed sessions

    Complete documentation

   Resume preparation

   Certification preparation.

   Interview preparation

   Placement assistance

We Deliver What We Promise!

                                         

                                         

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20th Oct - Demo Session - P.Saudagar - Hadoop : 10.00 am | Informatica : 11.00 am | Digital Marketing : 10.00 am | Nal Stop - Hadoop : 1.30 pm | Data Science : 10.45 am | Python : 2.00 pm | Kharadi - Tableau : 11.00 am | Data Science : 3.00 pm | Python : 12.00 pm