Artificial Intelligence Online Training

Artificial Intelligence Online Training Course Content :

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  • 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 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

  • 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

  • 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

Auto Encoders

  • 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

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