AI Training

AI Training Course

A digital label that certifies your expertise in AI is called an AI certificate.

37 Enrolled

5 month

Course Overview

The Skyper show you how to turn into an AI Expert by building and sending industry arranged projects. Find the capability of Artificial Intelligence through our courses and projects. We have situation organizations with top organizations like IBM, Infosys, Wipro, Syntel, and Cognizant. Because of our long history of achievements around here, we offer upwards of thirty assignments each week that will set up our understudies to succeed at AI, very much like experts.

Key Features

  • Course Duration : 5 months

  • Real-Time Projects : 2

  • Project Based Learning

  • EMI Option Available

  • Certification & Job Assistance

  • 24 x 7 Lifetime Support

Course Content

  • 1. 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 Perceptions
      • Understanding Feedforward Networks
      • Boltzmann Machines and Autoencoders
      • Exploring Backpropagation

    2. 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 

    3. 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

    4. 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

    5. 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

    6. 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

    7. 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

    8. 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

    9. 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

    10. 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

    11. 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

Course Enroll

Frequently ask question

Our training programs are apt for the aspirants who are keen on excelling in a career in analytics. Our programs will also benefit working professionals who are planning to make a career switch into the analytics domain. We help you develop skills that are needed to land your dream job and help you succeed in your career.

Indeed, our course educational plan is exceptionally planned by specialists and it reverberates with the ongoing business norms. We likewise update our educational program on a regular premise to assist our understudies with remaining pertinent with the current examination industry patterns.

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