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Advanced Certification in Artificial intelligence


 Advanced

INR 10000/-  INR 1999/-

What You'll Learn

Understand the theory behind Artificial Intelligence
Start building AI with no previous coding experience using Python
Optimize your AI to reach its maximum potential in the real world
Solve Real World Problems with AI

About

Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and reacts like humans. Some of the activities computers with artificial intelligence are designed for include: speech recognition, learning, planning, problem solving….Artificial intelligence must have access to objects, categories, properties and relations between all of them to implement knowledge engineering. Start building AI with no previous coding experience using Python, merge AI with OpenAI Gym to learn as effectively as possible. Optimize your AI to reach its maximum potential in the real world.

This course will be useful to solve complex universe problems, can be used for gaming purpose, provide virtual assistant to automotive industry user for better performance, can create intelligent robots which can perform tasks with their own experiences without pre-programmed and many more.

Requirements

  • No prior experience required.
  • Only Zeal to learn required

Course Outline

  • Introduction to AI
  • History of AI
  • Application of AI
  • Course logistics
  • Goals of Artificial Intelligence
  • Artificial Intelligence Techniques
  • Applications of Artificial Intelligence
  • What contributes to Artificial Intelligence
  • Real life Use cases in various industries
  • Creating Environment and Installation of AI packages
  • Basics of Python used in AI
  • What is NumPy?
  • Introduction to Pandas
  • Machine Learning basics
  • Deep Learning basics
  • Data Pre-processing
  • Neural Networks
  • Back Propagation in Neural Networks
  • Gradient Descent in AI
  • Cost Function in AI
  • Intelligent Agents
  • Environments
  • Agents Terminology
  • Properties of Environment
  • Search Terminologies
  • Single Agent Pathfinding problems
  • Graph Based Search
  • Brute-Force Search (Uninformed Search)
  • Heuristic Search (Informed Search)
  • Local Search Algorithms
  • Regression
  • Simple Linear Regression
  • Multiple Linear Regression
  • Decision Tree Regression
  • Random Forest Regression
  • Classification
  • Support Vector Classification (SVM)
  • K – Nearest Neighbour Algorithm (KNN)
  • Naïve Bayes Classification
  • Clustering
  • K-means clustering
  • Hierarchical clustering
  • Neural Networks
  • Feed Forward Network
  • Input layer, hidden layer, output layer
  • Loss Function
  • Optimizers
  • adam
  • rmsprop
  • Activation Functions
  • sigmoid
  • relu
  • Artificial Neural Network
  • Convolutional Neural Network
  • Conv2D & Conv3D layer
  • Pooling Layer
  • Flattening Layer
  • Recurrent Neural Network
  • Time Series Analysis
  • LSTM – Long Short-Term Memory
  • Sequential Model
  • About Fuzzy Logic
  • Its System Architecture
  • Application of Fuzzy Logic Systems and relevant examples
  • Advantages and Disadvantages
  • Content Based Filtering
  • Collaborative Filtering
  • Association Rule Learning
  • Markov Decision Process
  • Bellman Equation
  • Q-Learning Algorithm
  • Deep Q-Learning Algorithm

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Student feedback

★★★★★
5.0 average based on 2 reviews
Harsh Chaudhary
★★★★★ 4 months ago

It was very informative and structured very well. However to get the most of it, specially the practical part, its good to have a good base of coding. I didnt and struggle to get the most of the practical part. I do recommend the course.

Pramod thakur
★★★★★ 4 months ago

This course was really useful. It gave me a complete picture about AI theoretically as well as practically. Explanation of the code was really clear and enjoyed each and every details of it. Thanks a ton for giving us, an awesome course on AI. Keep up the good work!

Course Features

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