chemometric Course

Welcome to the chemometric Course repository! This course aims to provide a comprehensive introduction to machine learning using Python, Jupyter Notebooks, and popular libraries such as scikit-learn . Whether you’re a beginner or an experienced data scientist, this course will equip you with the necessary tools and knowledge to tackle real-world ML problems.

Table of Contents About Course Content Prerequisites Getting Started Contributing

About In this course, we cover fundamental ML concepts, algorithms, and practical implementations. You’ll find Jupyter notebooks for various topics, including supervised learning, unsupervised learning and more.

Course Content The course includes the following materials:

Lecture Notebooks: Detailed notebooks covering different ML topics. Lab Exercises: Hands-on labs to reinforce your understanding. Slides: HTML and PDF versions of presentation slides. Study Materials: Additional resources for deeper learning.

Prerequisites Before diving into the course, make sure you have: Basic knowledge of Python. Familiarity with data manipulation and visualization. A desire to explore the exciting field of machine learning!

Getting Started Clone this repository to your local machine. Install the required dependencies. Explore the notebooks in the notebooks directory. Experiment with the code, modify examples, and run your own experiments.

Contributing Contributions are welcome! If you find any issues or want to enhance the course content, feel free to submit pull requests.

Remember, this course is about exploration and learning. Enjoy your ML journey!