Microcredential_AI101: Artificial Intelligence (Professional)

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About Course



This course introduces students to four critical components:

  • Fundamentals of AI: Gain a solid understanding of core AI concepts like machine learning, deep learning, natural language processing, and computer vision.
  • Problem-solving Techniques: Learn how to approach problems from an AI perspective and develop effective algorithms to solve them.
  • Programming Skills: Build practical skills in programming languages like Python that are essential for working with AI tools and libraries.
  • Real-World Applications: Explore how AI is applied in various industries like healthcare, finance, and robotics.

What Will You Learn?

  • Grasp core AI concepts: Students will gain a comprehensive understanding of fundamental AI principles like search algorithms, knowledge representation, reasoning, machine learning, and deep learning.
  • Identify AI applications: Students will be able to recognize situations where AI techniques can be effectively applied to solve problems.
  • Evaluate AI strengths and limitations: The course will equip students to assess the capabilities and limitations of different AI approaches.
  • Implement basic AI algorithms: Students will develop hands-on skills in implementing core AI algorithms using a chosen programming language (e.g., Python).
  • Utilize AI libraries and tools: The course will introduce students to popular AI libraries and tools, enabling them to work with real-world AI applications.
  • Design intelligent systems: Students will gain the ability to design and develop simple intelligent systems by applying learned AI techniques.
  • Analyze complex AI models: Students will be able to critically analyze and interpret the behavior of complex AI models like deep neural networks.
  • Adapt AI models for new tasks: The course will equip students to adapt existing AI models for solving new and unseen problems.
  • Explore ethical considerations: Students will gain a critical understanding of the ethical implications surrounding the development and deployment of AI systems

Course Content

Module 1

  • 02:50
  • 06:29
  • LO2: Analyse the fundamental concepts and terminologies of Artificial Intelligence
  • LO3: Knowledge Check
  • 02:46
  • 07:02
  • LO2: Discuss the applications, advantages, and disadvantages of Machine learning
  • LO3: Knowledge Check
  • 02:33
  • 05:09
  • LO2: Examine applications of Neural Networks along with its merits and limitations
  • LO3: Knowledge Check
  • Topic 4: Problem-Solving with AI
  • LO1: Identify real-world problems that can be solved using AI techniques
  • LO2: Formulate AI-driven solutions and critically evaluate the suitability of AI methods for specific applications
  • LO3: Knowledge Check
  • Topic 5: Implementation of AI search Algorithms in Python
  • LO1: Discuss Search Algorithms in AI and its types
  • LO2: Illustrate the basic programming skills in Python and relevant Libraries
  • LO3: Demonstrate the implementation of AI algorithms
  • LO4: Knowledge Check
  • Topic 6: AI Ethics
  • LO1: Recognise Ethical considerations in AI, including bias, fairness, transparency, and privacy
  • LO2: Design AI systems that adhere to responsible AI principles
  • LO3: Knowledge Check
  • Topic 7: AI Application in Image Recognition
  • LO1: Understand Image Recognition and its working
  • LO2: Analyse Applications of Image Recognition along with its merits and limitations
  • LO3: Knowledge Check
  • Topic 8: Natural Language Processing
  • LO1: Understand Natural Language Processing and its working
  • LO2: Analyse applications of Natural Language processing along with its merits and limitations
  • LO3: Knowledge Check
  • Topic 9: AI-based Recommendation Systems
  • LO1: Understand Recommendation systems and their working
  • LO2: Analyse applications of Recommendation systems along with their merits and limitations
  • LO3: Knowledge Check
  • Module Based Quiz

Final Exam

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