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Artificial Intelligence Fundamentals

Discover the basics of Artificial Intelligence (AI) and its revolutionary effects on various sectors

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Certificate :

After Completion

Start Date :

10-Jan-2025

Duration :

30 Days

Course fee :

$150

COURSE DESCRIPTION:

  1. Discover the basics of Artificial Intelligence (AI) and its revolutionary effects on various sectors.

  2. This introductory course offers a thorough overview of essential AI concepts, methods, and uses. 

  3. Learn about machine learning and natural language processing through practical applications. 

  4. Engage with AI tools and frameworks for a hands-on learning experience.

  5. Equip yourself for further exploration in the realm of intelligent systems.

CERTIFICATION:

  1. Earn a Certified AI Fundamentals Specialist credential, showcasing your foundational knowledge of AI concepts and techniques.

LEARNING OUTCOMES:

By the conclusion of the course, participants will possess the skills to:

  1. Grasp the historical context, breadth, and ethical considerations surrounding AI. 

  2. Distinguish between major AI fields, such as machine learning, deep learning, and reinforcement learning. 

  3. Implement supervised, unsupervised, and reinforcement learning methods to address practical challenges. 

  4. Investigate natural language processing (NLP) applications, including text analysis and translation. 

  5. Utilize AI frameworks and libraries like TensorFlow, PyTorch, and Scikit-learn for model development.

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Course Curriculum

Introduction to Artificial Intelligence
  1. Definition and overview of AI.
  2. Types of AI: Narrow AI, General AI, and Super AI.
  3. AI vs. Machine Learning vs. Deep Learning.
  4. History and evolution of Artificial Intelligence.
  5. Key applications of AI in industries like healthcare, finance, transportation, and more.
Core Concepts of AI
  1. Agents and Environments: Understanding intelligent agents and their interaction with environments.
  2. Problem-solving: AI approaches to search and optimization problems.
  3. Knowledge Representation: Storing information in a form understandable by machines.
  4. Logical reasoning: Inference techniques and decision-making.
  5. AI planning: Creating systems that can predict and act on future states.
AI Techniques
  1. Search Algorithms: Breadth-first search, depth-first search, A* algorithm, and heuristic search.
  2. Knowledge Representation: Propositional logic, predicate logic, semantic networks, and frames.
  3. Machine Learning Basics: Introduction to supervised, unsupervised, and reinforcement learning.
  4. Natural Language Processing: Fundamentals of language understanding, tokenization, and text processing.
  5. Computer Vision: Basics of image processing and object recognition.
AI Tools and Programming
  1. Overview of Python for AI development.
  2. Introduction to key libraries: NumPy, pandas, scikit-learn, TensorFlow, and PyTorch.
  3. Working with AI platforms and tools: Google AI, IBM Watson, and Microsoft Azure AI.
  4. Developing basic AI projects like chatbot development, game-playing AI, and simple prediction models.
Ethics and Challenges in AI
  1. Understanding bias in AI algorithms.
  2. Data privacy concerns and challenges.
  3. Ethical dilemmas in AI decision-making.
  4. Explainable AI (XAI): Making AI systems transparent and interpretable.
  5. Impacts of AI on jobs and society: Automation, upskilling, and re-skilling.
  1. AI and emerging technologies: Quantum AI, AI in IoT, and edge AI.
  2. Role of AI in autonomous systems: Self-driving cars, drones, and robotics.
  3. AI for social good: Applications in environmental protection, disaster management, and healthcare.
  4. Discussing singularity: Myths and facts about AI surpassing human intelligence.
Capstone Project
  1. Design and implement a foundational AI system.
    • Example projects:
      • Building a simple chatbot with Natural Language Processing.
      • Developing a game-playing AI (e.g., Tic-Tac-Toe or Snake game).
      • Creating a basic recommendation system.
      • Developing a facial recognition system using image processing techniques.
    .

Training Features

Comprehensive Foundation in AI

Learn the basic principles and techniques of Artificial Intelligence.

Hands-On Learning

Work on practical exercises, mini-projects, and a final capstone project to reinforce learning.

Introduction to AI Tools

Gain exposure to essential tools and libraries used in AI development, such as Python, TensorFlow, and scikit-learn.

Ethical AI Practices

Understand the importance of ethical considerations in AI development and deployment.

Career Preparation

Develop foundational skills to pursue advanced AI studies or entry-level AI-related roles.

Certification

Receive a certificate of completion to showcase your AI skills and knowledge.

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