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Course Details

If you are a computer geek, if you love coding and wish to explore dimensionless world of programming, TechTrunk brings you the core Artificial Intelligence Training which will take you through core development and programming experience and will make you expert in writing algorithms for AI applications, you will learn Machine Learning, Fuzzy Logic, NLP, SVM and much more.

Content

Day 1Session 1

  • Introduction to Artificial Intelligence
  • Applications, Industries and growth
  • Techniques used for AI
  • AI for everything
  • Getting started with Artificial Intelligence
  • Python Basics & Hands On
  • Getting started with Python
  • Working with Software Environment
  • Variables, Lists, Vectors, Matrices & Arrays
  • Control Structures – If else, for and while loop

Session 2

  • Functions & Subroutines
  • Object oriented Programming
  • Commonly used predefined function in Python
  • Using numpy for mathematical Computation in Python
  • Miscellaneous Functions & their applications

Day 2Session 1

  • Fuzzy Logic
  • Getting started with Fuzzy Logic
  • Applications of Fuzzy Logic
  • Working with Fuzzy logic
  • Problem Formulation, Defuzzification & Rulebase
  • Membership Functions
  • Defuzzification Methods
  • Mamdani & Sugeno Methods

Session 2

  • Washing Machine Problem
  • Tipping Problem Analysis
  • Fuzzy Clustering
  • Fuzzy C Means Clustering
  • Fuzzy K Means Clustering
  • Fuzzy Logic in Various Branches
  • Fuzzy Logic packages in Python
  • Using Pyfuzzy with python
  • Programming Fuzzy Logic Applications
  • Practical Examples, Case Studies & Hands on session on Fuzzy Logic

Day 3Session 1

  • Artificial Intelligence & Machine Learning
  • Applications of Machine Learning
  • Getting Started with Machine Learning
  • Supervised Learning Introduction & Examples
  • Unsupervised Learning Introduction & Examples

Session 2

  • Regression & Classification Problem Analysis
  • Linear Algebra in Python
  • Linear Regression Method
  • Working with Linear Regression Problems in Python

Day 4Session 1

  • Artificial Neural Network (ANN)
  • Introduction to Neuron
  • Introduction to Network Architecture
  • Designing Neural Network Model
  • Model Representation Methods
  • Single Layer Neural Network
  • Weights & Activation Functions
  • Multilayer Neural Network Architecture
  • Introduction to Gradient Descent Algorithm

Session 2

  • Working with theano & Python
  • Training Straight line hypothesis
  • Training the Network
  • Backward Propagation Training
  • Using the Network
  • Importing & Exporting Network
  • Importing & Exporting Training Data
  • Dynamic Neural Network
  • Practical Examples, Case Studies & Hands on sessions

Day 5Session 1

  • Working with Projects
  • Getting data from Scikit learn
  • working with scikitlearn
  • data analysis tools from scikit learn

Session 2

  • Best Mean Fitting
  • Working with Best Mean Fitting
  • Single Line as Hypothesis Training
  • Using theano for best mean fitting
  • Practical Example

Day 6Session 1

  • Support Vector Machine
  • Introduction to SVM
  • Concept of Support Vector Machine
  • Working with scikit learn library
  • SVM Parameters
  • Using Support Vector for Classification
  • Using Support Vector for Regression

Character recognition using SVM

 

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