What you'll learn
- ✓ - Understand the fundamentals and key concepts of Machine Learning.
- ✓ - Discover the different types of Machine Learning: supervised, unsupervised, and reinforcement learning.
- ✓ - Understand and apply fundamental Machine Learning algorithms.
- ✓ - Prepare, clean, and preprocess datasets.
- ✓ - Train and test Machine Learning models.
- ✓ - Evaluate model performance using appropriate metrics.
- ✓ - Understand how to apply Machine Learning to real-world problems.
About this course
Discover the fundamentals of Machine Learning and learn how computers can identify patterns, make predictions, and solve real-world problems using data.
This course introduces the core concepts of Machine Learning, including supervised and unsupervised learning, data preprocessing, model training, and evaluation. Through practical examples, you will develop a solid understanding of machine learning workflows and explore how different algorithms can be applied to real-world challenges.
By the end of this course, you will have the foundational knowledge needed to start building your own Machine Learning projects.
Course plan
4 chapters · 5 lessons
Prerequisites
- Basic knowledge of Python programming. - Basic understanding of mathematics, particularly algebra and statistics. - No previous experience in Machine Learning is required. - Curiosity and motivation to explore Artificial Intelligence and data-driven solutions.
About the teacher
Hassen Amri
Generative Ai Instructor At Horizon School | Senior Generative AI at WatsNext | Linkedin: https://www.linkedin.com/in/hassenamri005
Student reviews
No reviews yet.