Course description
This course provides a detailed introduction to Machine Learning (ML), covering fundamental concepts, key algorithms, and real-world applications. Participants will learn about supervised and unsupervised learning, model evaluation, and optimization techniques. The course also covers popular ML libraries, data preprocessing, and feature engineering. By the end, learners will have the skills needed to build and deploy ML models.
What will i learn?
- Understand key Machine Learning concepts and their applications
- Learn data preprocessing and feature engineering techniques
- Explore and implement supervised and unsupervised ML algorithms
- Build, evaluate, and optimize ML models
- Work on real-world ML projects
Requirements
- Basic understanding of mathematics and statistics
- Interest in AI and Machine Learning
- Familiarity with Python is recommended but not mandatory
Frequently asked question
Beginners and professionals looking to learn ML fundamentals.
Basic programming knowledge is helpful but not required.
ML concepts, algorithms, data handling, and model evaluation.
