About This Course

• Understand the fundamentals of Artificial Intelligence and Machine Learning.
• Learn the differences between AI, Machine Learning, and Deep Learning.
• Explore supervised and unsupervised learning techniques.
• Work with real-world datasets for data analysis.
• Build and evaluate basic machine learning models.
• Learn data preprocessing and feature engineering.
• Understand model training, testing, and evaluation.
• Get introduced to neural networks and deep learning concepts.
• Apply AI and ML techniques through hands-on projects.
• Build a strong foundation for advanced AI and data science learning.

Requirements

• Basic computer skills.
• A computer with internet access.
• No prior AI or Machine Learning experience required.
• Basic understanding of mathematics is helpful but not mandatory.
• Willingness to learn and practice.

Learning Outcomes

After completing this course, learners will be able to:

• Explain fundamental AI and Machine Learning concepts.
• Prepare datasets for machine learning tasks.
• Train and evaluate basic machine learning models.
• Solve simple real-world prediction problems.
• Continue learning advanced AI and deep learning topics.

Course Staff

Dr. Michael Johnson

AI & Machine Learning Instructor

An experienced AI researcher and educator with expertise in machine learning, data science, and intelligent systems.

Sophia Lee

Teaching Assistant

A Machine Learning Engineer passionate about helping beginners understand AI through practical examples and projects.

FAQ

Do I need programming experience?
Basic programming knowledge is helpful but not required.

Will there be hands-on projects?
Yes. The course includes practical exercises and beginner-friendly AI projects.

Who is this course for?
Students, software developers, data enthusiasts, and anyone interested in learning AI and Machine Learning from the ground up.