Core Ai

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  • Batch Name

    Core AI
  • Course Start

    1-February-2025
  • Course duration

    90 Hours
  • Eligibility

    8th

Core AI Course 

Do Artificial Intelligence Course with CITC – An ISO 9001:2015 certified organisation, associated with NIELIT, MSME & Google. 

About Course

Artificial Intelligence courses are designed to provide knowledge in various  topics in AI, including machine learning, deep learning, natural language processing, computer vision, robotics, and data analytics.
Artificial Intelligence is a unique career path that can lead you to success. AI itself will need development, maintenance, and sales. Artificial Intelligence courses are therefore going to be a lifesaver in the future.

What Will You Learn?

You will learn the following skills:

  • Introduction to AI
  • Python introduction for AI
  • Machine Learning Basics
  • Data Processing and Feature Engineering
  • Supervised Learning Algorithms
  • Unsupervised Learning Algorithms
  • Neural Networks Basics
  • AI Tools and Frameworks

Scope

Following are the few fields students can look at after pursuing this program but work experience and job profile are a must:

  • Product Manager 
  • AI Engineer
  • Big Data Engineer
  • Business Intelligence Developer
  • Data Scientist
  • Machine Learning Engineer
  • Research Scientist
  • AI Data Analyst

Module 1

Course Syllabus

  • What is Artificial Intelligence?
  • Types of Artificial Intelligence
  • Applications of AI
  • Challenges in AI Development
  • The Future of Artificial Intelligence
  • Early Foundations of AI
  • The Birth of Artificial Intelligence
  • The Era of Optimism (1950s–1970s)
  • The AI Winters
  • The Resurgence of AI (1990s–2000s)
  • Modern AI Era (2010s–Present)
  • Key Factors Driving AI Evolution
  • Challenges During AI Evolution
  • Lessons Learned from AI’s History
  • The Road Ahead for AI
  • What is Machine Learning?
  • Types of Machine Learning
  • Key Concepts in Machine Learning
  • Steps in the Machine Learning Workflow
  • Applications of Machine Learning
  • Challenges in Machine Learning
  • Future Trends in Machine Learning
  • Introduction to Supervised Learning
  • Introduction to Unsupervised Learning
  • Key Differences Between Supervised and Unsupervised Learning
  • Real-World Applications
  • Challenges in Both Approaches
  • Combining Supervised and Unsupervised Learning
  • Understanding Deep Learning
  • Neural Network Structure
  • Training Neural Networks
  • Types of Neural Networks
  • Applications of Deep Learning
  • Challenges in Deep Learning
  • Tools and Frameworks for Deep Learning
  • What Are Neural Networks?
  • Mathematics of Neural Networks
  • Types of Neural Networks
  • Applications of Neural Networks
  • Challenges in Neural Networks
  • Future Directions
  • Introduction to AI Frameworks
  • TensorFlow
  • PyTorch
  • Comparison: TensorFlow vs. PyTorch
  • Applications of TensorFlow and PyTorch
  • Challenges in Using AI Frameworks
  • Future Directions
  • What is Natural Language Processing (NLP)?
  • Components of NLP
  • Techniques in NLP
  • Tools and Libraries in NLP
  • Applications of NLP
  • Challenges in NLP
  • Future Directions
  • What is Computer Vision?
  • Key Concepts in Computer Vision
  • Techniques in Computer Vision
  • Tools and Libraries for Computer Vision
  • Applications of Computer Vision
  • Challenges in Computer Vision
  • Future Directions
  • What is Reinforcement Learning?
  • Mathematical Framework of Reinforcement Learning
  • Key Algorithms in Reinforcement Learning
  • Applications of Reinforcement Learning
  • Challenges in Reinforcement Learning
  • Future Directions

Module 2

Course Syllabus

  • What is AI in Robotics?
  • Components of AI in Robotics
  • AI Algorithms in Robotics
  • Applications of AI in Robotics
  • Challenges in AI Robotics
  • Future Directions
  • Understanding Ethics in AI
  • Understanding Bias in AI
  • Strategies to Mitigate Bias in AI
  • Ethical Concerns in AI Applications
  • Societal Impact of AI Ethics
  • Future Directions in Ethical AI
  • What Are Chatbots and Virtual Assistants?
  • Key Components of Chatbots and Virtual Assistants
  • Building Chatbots: Step-by-Step Guide
  • Advanced Features of Virtual Assistants
  • Applications of Chatbots and Virtual Assistants
  • Challenges in Building Chatbots and Virtual Assistants
  • Future Directions in Chatbots and Virtual Assistants
  • Role of AI in Business
  • Applications of AI in Business
  • Implementation Strategies for AI in Business
  • Challenges of AI Adoption in Business
  • Future Directions for AI in Business
  • What is AI Model Deployment?
  • Key Concepts in AI Model Deployment
  • Steps in AI Model Deployment
  • Tools and Frameworks for Model Deployment
  • Challenges in AI Model Deployment
  • Best Practices for AI Model Deployment
  • Future Directions in AI Model Deployment
  • What is AI for Data Analytics?
  • Core Components of AI-Driven Data Analytics
  • Techniques in AI-Driven Data Analytics
  • Tools and Platforms for AI-Driven Data Analytics
  • Applications of AI in Data Analytics
  • Challenges in AI for Data Analytics
  • Future Trends in AI-Driven Data Analytics
  • Generative Adversarial Networks (GANs)
  • Recurrent Neural Networks (RNNs)
  • GANs vs. RNNs
  • Future Trends in GANs and RNNs
  • AI in Healthcare
  • AI in Autonomous Vehicles
  • Importance of AI Tools and Platforms
  • Categories of AI Tools and Platforms
  • Key Challenges in Using AI Tools and Platforms
  • Future Trends in AI Tools and Platforms

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