This repository contains the notebooks and the final project of the IBM Machine Learning with Python Course. The purpose of this course is to introduce the fundamentals of machine learning and its applications using the Python programming language.

The course covers the following topics:

• Introduction to Machine Learning
• Regression
• Classification
• Clustering
• Model Evaluation and Selection
• Decision Trees
• SVM (Support Vector Machines)
• Naive Bayes
• Unsupervised Learning All the notebooks are organized by week and contain detailed explanations, code examples and exercises that will help you understand the concepts presented during the course.

In order to use these notebooks, you should have the following software installed:

• NumPy
• Pandas
• Matplotlib
• Scikit-learn
• Seaborn

Clone the repository to your local machine. Install the required packages as mentioned above. Navigate to the directory where the repository was cloned and start Jupyter Notebook. Open the notebooks and follow the instructions provided. Final Project The final project of the course is also included in this repository. It involves building, evaluating and comparing several Machine Learning models using different algorithms. The aim of the project is to demonstrate your skills and knowledge acquired throughout the course.

Upon completion of the course, you will receive a certificate of completion from IBM.

Course Certificate

Please note that the notebooks in this repository are for educational purposes only and should not be used for commercial purposes.