Artificial Intelligence (AI) Fundamentals

About Course

This course introduces students to the fundamental concepts and technologies of Artificial Intelligence (AI). Students will explore how AI algorithms work, their applications, and the impact of AI on modern industries.

Course Content

Module 1: Introduction to Artificial Intelligence

Lesson 1.1: What is AI?
Lesson 1.2: Key Components of AI
Lesson 1.3: The Ethics of AI
Quiz 1

Module 2: Machine Learning (ML) Basics

Module 3: Deep Learning and Neural Networks

Module 4: Natural Language Processing (NLP)

Module 5: Computer Vision

Module 6: Reinforcement Learning

Module 7: AI in Real-World Applications

Module 8: The Future of AI

Module 9: Conclusion and Final Project

About Course

Course Content

Module 1: Introduction to Artificial Intelligence

Lesson 1.1: What is AI?

Lesson 1.2: Key Components of AI

Lesson 1.3: The Ethics of AI

Quiz 1

Module 2: Machine Learning (ML) Basics

Lesson 2.1: Introduction to Machine Learning

Lesson 2.2: Data Preprocessing

Lesson 2.3: Supervised Learning Algorithms Linear Regression

Lesson 2.4: Unsupervised Learning Algorithms K-Means Clustering

Lesson 2.5: Evaluating Models

Module 3: Deep Learning and Neural Networks

Lesson 3.1: Introduction to Deep Learning

Lesson 3.2: Artificial Neural Networks (ANNs)

Lesson 3.3: Convolutional Neural Networks (CNNs)

Lesson 3.4: Recurrent Neural Networks (RNNs)

Lesson 3.5: Autoencoders and GANs

Module 4: Natural Language Processing (NLP)

Lesson 4.1: Introduction to NLP

Lesson 4.2: Text Preprocessing

Lesson 4.3: Language Models

Lesson 4.4: Sentiment Analysis and Text Classification

Lesson 4.5: Chatbots and Virtual Assistants

Module 5: Computer Vision

Lesson 5.1: Introduction to Computer Vision

Lesson 5.2: Image Classification and Object Detection

Lesson 5.3: Image Segmentation

Lesson 5.4: Face and Gesture Recognition Facial recognition systems

Module 6: Reinforcement Learning

Lesson 6.1: Introduction to Reinforcement Learning (RL)

Lesson 6.2: Markov Decision Processes (MDP) States, actions, rewards, and transitions

Lesson 6.3: Deep Q-Networks (DQN) Combining deep learning with RL

Lesson 6.4: Policy Gradient Methods

Module 7: AI in Real-World Applications

Lesson 7.1: AI in Healthcare

Lesson 7.2: AI in Finance

Lesson 7.3: AI in Autonomous Vehicles Self-driving car technologies

Lesson 7.4: AI in Manufacturing and Robotics Industrial robots and automation

Lesson 7.5: AI in Entertainment

Module 8: The Future of AI

Lesson 8.1: Trends in AI Research Advancements in explainable AI (XAI)

Lesson 8.2: Challenges in AI

Lesson 8.3: The Future Workforce and AI

Module 9: Conclusion and Final Project

Lesson 9.1: Recap and Review

Lesson 9.2: Final Project