Course curriculum

  • 1

    Certified AI Developer

    • Introduction to the course
  • 2

    Introduction to AI

    • What is Artificial Intelligence?

    • Intelligent Agents

    • Advantages and Disadvantages of AI

    • Challenges of AI
  • 3

    Problem Solving

    • What is Searching?

    • Uninformed Search Algorithm

    • Informed Search Algorithm

    • Adversarial Search

    • Constraint satisfaction problems
  • 4

    Knowledge Representation And Planning

    • Knowledge Representation

    • Knowledge Representation Techniques

    • Propositional Logic

    • First Order Logic

    • Rule-Based System
  • 5

    Probabilistic Reasoning

    • Basic Probability Concepts

    • Markov and Hidden Markov Model

    • Association rules

    • Dimensionality reduction

    • Feature selection and Feature Extraction
  • 6

    Machine Learning

    • What is Machine Learning?

    • Types of Learning

    • Clustering

    • Classification

    • Decision Tree

    • Regression

    • Support Vector Machine

    • What is Reinforcement learning?
  • 7

    Communication and Perceiving

    • Natural Language Processing

    • Perception
  • 8

    Neural Network

    • What is a Neural Network?

    • Types of Neural Network

    • Neural Network Components
  • 9

    Data Mining Tools

    • RapidMiner

    • Working with RapidMiner

    • Weka

    • Working with Weka

    • Orange

    • R/RStudio

    • KNIME
  • 10

    Projects

    • Installing Prerequisites

    • Clustering using K-means in Python
  • 11

    Study Material

    • Study Material