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Artificial Intelligence

Artificial Intelligence 1

About the Course

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

  • Duration: 50 hours class room program, 8 Weekends
  • Prerequisites: Good understanding of Python Programming and package like Numpy, Sklearn, Pandas and Matplotlib (Self Paced Modules and classes are available on request)
  • Lab: Execises on Multiple Algorithims in ML, DL and NN using Python and Tensor Flow

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

Welcome to the course!

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

Data Preprocessing

  • Python and Data preprocessing (Crash Course - Self paced)

    1. Python Fundamentals
    2. Numpy
    3. Pandas
    4. Data Visualization
    5. Scikit Learn
    6. Data Preprocessing

Using Git and GitHub

  • Setting up Your GitHub Account
  • Configuring Your First Git Repository
  • Making Your First Git Commit
  • Pushing Your First Commit to GitHub
  • Git and GitHub Workflow Step-by-Step

Machine Learning

Regression

  1. Simple Linear Regression
  2. Multiple Linear Regression
  3. Bias-Variance trade-off

Classification

  1. Logistic Regression
  2. K-Nearest Neighbors (K-NN)
  3. SVM
  4. Decision Trees
  5. Random Forest

Clustering

  1. K-means
  2. Hierarchical
  3. DBSCAN

Dimensionality Reduction

  1. Linear discriminant analysis
  2. Principal component analysis

Natural Language Processing

Natural Language Processing

NLTK

  1. NLP with NLTK
  2. NLTK extensions and exploration

Sentiment Analyzer

  1. Description of Sentiment Analyzer
  2. Preprocessing: Tokenization
  3. Preprocessing: Tokens to Vectors
  4. Sentiment Analysis using Logistic Regression
  5. Sentiment Lexicons
  6. Regular Expressions
  7. Twitter Sentiment Analysis
  8. Twitter Sentiment Analysis - Regular Expressions
  9. Twitter Sentiment Analysis - KNN, Decision trees, Random forests and Sentiwordnet

Latent Semantic Analysis

  1. Intro to Latent Semantic Analysis
  2. PCA and SVD - The underlying math behind LSA
  3. Latent Semantic Analysis in Python
  4. Advanced LSA

Article spinner

  1. Article Spinning Introduction and Markov Models
  2. Trigram Model
  3. Article spinner in Python

Tensorflow and Neural Networks

Tensorflow

  1. Introducing TF
  2. Computation Graph
  3. Tensors
  4. Placeholders and Variables
  5. Neural Networks
  6. Perceptron
  7. Activation Functions
  8. Cost Functions
  9. Gradient Descent Backpropagation

Artificial Neural Networks

  1. Regression in TF
  2. Regression in TF and NN using Estimator
  3. Regression in TF and NN using Keras
  4. Classification in TF
  5. Classification in TF and NN using Estimator
  6. Classification in TF and NN using Keras

Computer Vision

Convolutional Neural Networks

  1. Intro to CNN
  2. Convolution Operation
  3. Activation Layer
  4. Pooling
  5. Flattening
  6. Full Connection
  7. Softmax, Argmax & Cross-Entropy

Basics of Computer Vision and OpenCV

  1. Image Formation
  2. Getting Started with OpenCV
  3. Understanding Color Spaces
  4. Histogram representation of Images
  5. Image Manipulations
  6. Live Sketch App
  7. Identifying Shapes
  8. Counting Circles and Ellipses

Object Detection

  1. Object Detection Overview
  2. How SSD is different
  3. The Multi-Box Concept
  4. Predicting Object Positions
  5. The Scale Problem
  6. Feature Description Theory
  7. Finding Corners
  8. SIFT, SURF, FAST, BRIEF and ORB
  9. Histogram of Oriented Gradients
  10. Hands-on Object Detection

Face Detection

  1. Face and Eye Detection
  2. Viola-Jones Algorithm
  3. Haar-like Features
  4. Integral Image
  5. Training Classifiers
  6. Adaptive Boosting (Adaboost)
  7. Cascading
  8. Merging Faces (Face Swaps)
  9. Yawn Detector and Counter
  10. Facial Recognition

Motion Analysis and Object Tracking (Ball Tracking)

  1. Filtering by Color
  2. Background Subtraction and Foreground Subtraction
  3. Using Meanshift for Object Tracking
  4. Using CAMshift for Object Tracking
  5. Optical Flow