Artificial Intelligence / Machine Learning

Artificial Intelligence Training in Pune

Artificial Intelligence (AI) is a field that has a long history but is still constantly and actively growing and changing. In this course, you’ll learn the basics of modern AI as well as some of the representative applications of AI. Along the way, we also hope to excite you about the numerous applications and huge possibilities in the field of AI, which continues to expand human capability beyond our imagination.

Prerequisites for Artificial Intelligence

There are no technical prerequisites for Machine Learning & artificial intelligence.  knowledge of python and Numpy, Sklearn, Pandas and Matplotlib.

• Course Duration: 50 hours class room program, 8 Weekends
• Lab: Execises on Multiple Algorithims in ML, DL and NN using Python and Tensor Flow

Who can learn Machine Learning/ Artificial Intelligence training?

  • Robotics Engineer
  • Data Scientist
  • Business Analysts
  • Hadoop Developers
  • Python for Data Science
  • College Graduates

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

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