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  • AI
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AI Fundamentals

Course Description

AI Fundamentals is a beginner-friendly course crafted to introduce you to the fascinating world of Artificial Intelligence. Whether you're a curious learner, a professional from a non-technical background, or someone looking to pivot into tech, this course will provide you with a clear understanding of how AI works, where it’s used, and why it matters.

Through real-world examples, practical exercises, and exposure to trending tools and concepts—such as generative AI and large language models—you’ll gain both theoretical knowledge and applied skills. The course demystifies key AI technologies like machine learning, neural networks, and natural language processing, while also exploring the ethical and societal impacts of AI systems.

No coding background? No problem. You’ll be guided step-by-step using accessible platforms and no-code tools to build a foundation you can build on—whether you aim to enter the field professionally or simply stay ahead of the curve in a tech-driven world.

What You'll Learn:
  • Grasp foundational AI concepts – Understand what AI is, its evolution, and its major subfields like machine learning, natural language processing, and robotics.
  • Differentiate AI types – Learn the differences between Narrow AI, General AI, and Super AI, and their respective use cases.
  • Work with machine learning basics – Get introduced to key algorithms and concepts such as supervised and unsupervised learning.
  • Explore deep learning – Discover how neural networks function, including CNNs and Transformers, and their applications in vision, speech, and text.
  • Understand language models – Learn how AI processes human language using NLP and how LLMs like GPT are transforming industries.
  • Use popular AI tools and platforms – Practice using tools like Google Colab, Teachable Machine, and PyTorch, even without advanced coding knowledge.
  • Address ethical concerns – Identify and discuss AI ethics, including bias, data privacy, and global governance standards.
  • Apply AI to real-world problems – Analyze case studies from sectors like healthcare, finance, and retail, and complete a hands-on capstone project.

 

 

Course Curriculum

  • Introduction to Artificial Intelligence

    • Definition and scope of AI

    • History and milestones in AI development

    • Key terminologies and misconceptions

    • Branches of AI (ML, NLP, Robotics, CV, Expert Systems)

    • Current trends in AI (Generative AI, LLMs like GPT, etc.)

     

  • Types and Approaches of AI

    • Reactive Machines vs. Limited Memory vs. Theory of Mind AI

    • Symbolic AI vs. Connectionist AI

    • Narrow AI vs. General AI vs. Superintelligence

    • Rule-based systems and heuristics

     

  • Machine Learning Essentials

    • What is Machine Learning?

    • Supervised, Unsupervised, and Reinforcement Learning

    • Key algorithms (Linear Regression, Decision Trees, Clustering)

    • AI vs. ML vs. Deep Learning

     

  • Deep Learning and Neural Networks

    • Introduction to Neural Networks

    • CNNs, RNNs, and Transformers

    • Overview of training processes

    • Use cases in image and speech recognition

     

  • Natural Language Processing (NLP)

    • NLP fundamentals and applications

    • Text processing and sentiment analysis

    • Chatbots and conversational AI

    • Introduction to LLMs (Large Language Models) like ChatGPT

     

  • AI Tools, Platforms, and Frameworks

    • Introduction to TensorFlow, PyTorch, Scikit-learn

    • Google Colab and Jupyter Notebooks for experimentation

    • No-code AI tools (Teachable Machine, Microsoft Lobe)

    • AI as a Service (AIaaS) platforms

     

  • Ethics, Bias, and Responsible AI

    • Understanding AI bias and fairness

    • Data privacy and security concerns

    • Ethical AI development and usage

    • Global regulatory frameworks and governance (EU AI Act, etc.)

     

  • AI in Practice – Case Studies

    • AI in healthcare, finance, manufacturing, and retail

    • Autonomous vehicles and smart assistants

    • Edge AI and IoT integration

    • Emerging trends: AI in climate tech and education

     

  • Capstone Project

    • Design a basic AI solution to solve a real-world problem

    • Project planning, model selection, and evaluation

    • Presentation and peer feedback

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Chronolearn

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Title From Date To Date Cost
Upcoming Classes 2025-05-24 2025-05-25 1249
Upcoming Classes 2025-05-31 2025-06-01 1249
Upcoming Classes 2025-06-14 2025-06-15 1249
This Course Fee:

₹2,399.00

₹3,500.00
Course includes:
  • img Level
      Beginner
  • img Duration 8h
  • img Passing Marks 75
  • img Exam Duration 1h 30m
  • img Certifications Yes
  • img Language
      English German Arabic French Spanish
  • img Access 90 Days
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