AI Training in Pune

Artificial Intelligence Training in Pune

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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.

Course Features

One Stop Solution

Ability to attend missed sessions

Certification preparation

Complete documentation

Interview preparation

Resume preparation

Placement assistance

FAQ

There are no technical prerequisites for this course.

Anyone interested in Bitcoin technology can undergo this training.

You can get the recored sessions for the same lecture, so that you can revise and do not suffer loss

We have designed our syllabus in such a way that it fits the suitability for HortonWorks certified developer certification.

Yes, we have multiple centers.

Your profile would be evaluated by experts, your resume would be rated and you would start getting calls after completion of your module.

Why choose Etlhive for Blockchain training near Kharadi

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Syllabus

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 Blockchain training institute near Kharadi

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 Blockchain Training

Python and Machine Learning

Syllabus

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

Natural Language Processing

  • Preparing data for Natural Language Processing 
  • Preprocessing data for Natural Language Processing
  • Vectorizers
  • Encoders
  • RNN Intuition
  • Studying the Neuron
  • The Activation Function
  • Working of Neural Networks for text data
  • Optimization

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

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

Recurrent Neural Networ

  • 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
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