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Artificial Intelligence 1

Artificial Intelligence Course Reviews

AWS-Course-ReviewsAverage of 4.9 out of 5 based on 91 Votes

About the Course

Ethans training institute Pune provides you Articial Intelligence training in pune (pimple saudagar area). Our AI Training includes Machine Learning,Deep Learning and Neural Network Training

  • Duration: 50 hours class room program, 8 Weekends
  • Prerequisites: Very Good understanding of Python
  • Lab: Execises on AI

After the classes:

  • Students will Good understanding on Python and AI
  • Deep Learning Concepts are clear and ready towork indivisually

Who gets this training?

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

 

Syllabus

 Syllabus:

Welcome to the course!

  • Applications of Machine Learning
  • Why Machine Learning is the Future

Part 1: Natural Language Processing

Natural Language Processing

  • Natural Language Processing in Python - Step 1
  • Natural Language Processing in Python - Step 2
  • Natural Language Processing in Python - Step 3
  • Natural Language Processing in Python - Step 4
  • Natural Language Processing in Python - Step 5
  • Natural Language Processing in Python - Step 6
  • Natural Language Processing in Python - Step 7
  • Natural Language Processing in Python - Step 8
  • Natural Language Processing in Python - Step 9
  • Natural Language Processing in Python - Step 10
  • Natural Language Processing in R - Step 1
  • Natural Language Processing in R - Step 2
  • Natural Language Processing in R - Step 3
  • Natural Language Processing in R - Step 4
  • Natural Language Processing in R - Step 5
  • Natural Language Processing in R - Step 6
  • Natural Language Processing in R - Step 7
  • Natural Language Processing in R - Step 8
  • Natural Language Processing in R - Step 9
  • Natural Language Processing in R - Step 10

Part 2: Deep Learning
Artificial Neural Networks

ANN Intuition

  • Plan of Attack
  • The Neuron
  • The Activation Function
  • How do Neural Networks work?
  • How do Neural Networks learn?
  • Gradient Descent
  • Stochastic Gradient Descent
  • Backpropagation

Building an ANN

  • Business Problem Description
  • Building an ANN - Step 1
  • Building an ANN - Step 2
  • Building an ANN - Step 3
  • Building an ANN - Step 4
  • Building an ANN - Step 5
  • [Exercise] Should we say goodbye to that customer?

Evaluating, Improving and Tuning the ANN

  • Evaluating the ANN
  • Improving the ANN
  • Tuning the ANN
  • [Exercise] - Put me one step down on the podium

Convolutional Neural Networks

CNN Intuition

  • Plan of attack
  • What are convolutional neural networks?
  • Step 1 - Convolution Operation
  • Step 1(b) - ReLU Layer
  • Step 2 - Pooling
  • Step 3 - Flattening
  • Step 4 - Full Connection
  • Softmax & Cross-Entropy

Building a CNN

  • Introduction to CNNs
  • Building a CNN - Step 1
  • Building a CNN - Step 2
  • Building a CNN - Step 3
  • Building a CNN - Step 4
  • Building a CNN - Step 5
  • [Exercise] - What's that pet?

Evaluating, Improving and Tuning the CNN

  • [Exercise] - Get the gold medal

Recurrent Neural Networks

RNN Intuition

  • Plan of attack
  • The idea behind Recurrent Neural Networks
  • The Vanishing Gradient Problem
  • LSTMs
  • Practical intuition
  • EXTRA: LSTM Variations

Building a RNN

  • Building a RNN - Step 1
  • Building a RNN - Step 2
  • Building a RNN - Step 3
  • Building a RNN - Step 4
  • Building a RNN - Step 5
  • Building a RNN - Step 6
  • [Exercise] - Google Stock Price Prediction

Evaluating, Improving and Tuning the RNN

  • Evaluating the RNN
  • [Exercise] - Improving and Tuning the RNN

Self-Organizing Maps

SOMs Intuition

  • Plan of attack
  • How do Self-Organizing Maps Work?
  • Why revisit K-Means?
  • K-Means Clustering (Refresher)
  • How do Self-Organizing Maps Learn? (Part 1)
  • How do Self-Organizing Maps Learn? (Part 2)
  • Live SOM example
  • Reading an Advanced SOM
  • EXTRA: K-means Clustering (part 2)
  • EXTRA: K-means Clustering (part 3)

Building a SOM

  • Building a SOM - Step 1
  • Building a SOM - Step 2
  • Building a SOM - Step 3
  • Building a SOM - Step 4

Mega Case Study

  • Mega Case Study - Step 1
  • Mega Case Study - Step 2
  • Mega Case Study - Step 3
  • Mega Case Study - Step 4

Boltzmann Machines

Boltzmann Machine Intuition

  • Plan of attack
  • Boltzmann Machine
  • Energy-Based Models (EBM)
  • Editing Wikipedia - Our Contribution to the World
  • Restricted Boltzmann Machine
  • Contrastive Divergence
  • Deep Belief Networks
  • Deep Boltzmann Machines

Building a Boltzmann Machine

  • Installing Ubuntu on Windows
  • Installing PyTorch
  • Building a Boltzmann Machine - Introduction
  • Building a Boltzmann Machine - Step 1
  • Building a Boltzmann Machine - Step 2
  • Building a Boltzmann Machine - Step 3
  • Building a Boltzmann Machine - Step 4
  • Building a Boltzmann Machine - Step 5
  • Building a Boltzmann Machine - Step 6
  • Building a Boltzmann Machine - Step 7

 AutoEncoders

AutoEncoders Intuition

  • Plan of attack
  • Auto Encoders
  • A Note on Biases
  • Training an Auto Encoder
  • Overcomplete hidden layers
  • Sparse Autoencoders
  • Denoising Autoencoders
  • Contractive Autoencoders
  • Stacked Autoencoders
  • Deep Autoencoders

Building an AutoEncoder

  • Building an AutoEncoder - Step 1
  • Building an AutoEncoder - Step 2
  • Building an AutoEncoder - Step 3
  • Building an AutoEncoder - Step 4
  • Building an AutoEncoder - Step 5
  • Building an AutoEncoder - Step 6
  • Building an AutoEncoder - Step 7

Part 9: Dimensionality Reduction
Principal Component Analysis (PCA)

  • PCA in Python - Step 1
  • PCA in Python - Step 2
  • PCA in Python - Step 3
  • PCA in R - Step 1
  • PCA in R - Step 2
  • PCA in R - Step 3

Linear Discriminant Analysis (LDA)

  • LDA in Python
  • LDA in R

Kernel PCA

  • Kernel PCA in Python
  • Kernel PCA in R

Part 10: Model Selection & Boosting
Model Selection

  • k-Fold Cross Validation in Python
  • Grid Search in Python - Step 1
  • Grid Search in Python - Step 2
  • k-Fold Cross Validation in R
  • Grid Search in R

XGBoost

  • XGBoost in Python - Step 1
  • XGBoost in Python - Step 2
  • XGBoost in R

Projects

  • Instacart Market Basket Analysis
  • All State Insurance Claims Severity Prediction
  • Forecast inventory demand based on historical sales data
  • Video and image recognition with the famous MNIST data
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