Artificial Intelligence & Machine Learning Training

About Course

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.

Fee: INR 15000/-

Duration: 45 hours

Mode of Training: Online Live Class

Upcoming Batch:

  • Online Batch: 27th October  -8:00 PM – 10:00 PM, IST, ( Monday to Friday)
  • Online Batch: 06th November  -7:00 AM – 9:00 AM, IST, ( Monday to Friday)

Course Content

  • 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
    • Functions & Subroutines
    • Object-oriented Programming
    • Commonly used predefined function in Python
    • Using numpy for Mathematical Computation in Python
    • Miscellaneous Functions & their applications
    • Linear Algebra required for Artificial Intelligence
    • Getting started with pandas, quandl and theano

  • 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
    • Washing Machine Problem
    • Tipping Problem Analysis
    • Fuzzy Clustering
    • Fuzzy C 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


  • Machine Learning

    • Artificial Intelligence & Machine Learning
    • Applications of Machine Learning
    • Getting Started with Machine Learning
    • Supervised Learning Introduction & Examples
    • Unsupervised Learning Introduction & Examples
    • Regression & Classification Problem Analysis
    • Linear Regression Method
    • Working with Linear Regression Problems in Python
    • Gradient Descent Algorithm for Linear Regression
    • Multivariate Linear RegressionHousing Prizes Prediction
    • Linear Regression Hands on Sessions
    • Logistic Regression Introduction and usecases


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

    • Backward Propagation Training
    • Delta method and Gradient Descent
    • Working with Projects
    • Getting data from Scikit learn
    • working with scikitlearn
    • ANN using scikitlearn
    • data analysis tools from scikit learn

    Best Mean Fitting

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

  • Support Vector Machine

    • Introduction to SVM
    • Concept of Support Vector Machine
    • Kernel Method and Nonlinear Decision Boundaries
    • Working with scikit learn library
    • SVM Parameters
    • Using Support Vector for Classification
    • Using Support Vector for Regression
    • Character recognition using SVM

    Natural Language Processing

    • Working with Sklearn, nltk python library for NLP
    • Natural Language Understanding & Generation
    • Accessing External Data, Working with words and sentences
    • Using NLTK for extracting data
    • Tokenization, Stemming, Lemmatization and POS Tagging
    • Naive Baiyes Method
    • Sentiment Analysis Example

     

  • Genetic Algorithm

    • Working with Genetic Algorithm
    • Getting started with Genetic Algorithm
    • Reproduction, Crossover & Mutation
    • Roulette Wheel method of selection
    • Fitness Function
    • Defining Fitness Function
    • Permutation & Combinations
    • Working with GA Examples
    • Optimization using GA
    • Clustering
    • K Means Clustering
    • Hands on Session with K Means
    • Principal Component Analysis
    • PCA Applications and usecases
    • PCA Implementation
    • Hands On Sessions

 

 

 

 

 

 

 

 

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