Ready to build machine learning models with confidence? This course focuses on equipping you with the essential Python skills to effectively apply machine learning. You’ll dive into key ML concepts and gain hands-on experience building models using scikit-learn and Jupyter Notebooks.
The program covers a wide range of techniques:
- Regression: Start with fundamental regression methods like linear, multiple linear, polynomial, and logistic regression.
- Supervised Learning: Progress to popular supervised models, including decision trees, K-Nearest Neighbors, and support vector machines.
- Unsupervised Learning: Explore unsupervised techniques such as various clustering methods and dimensionality reduction using powerful algorithms like PCA, t-SNE, and UMAP.
You’ll reinforce your learning through real-world labs where you’ll practice crucial skills like model evaluation, cross-validation, regularization, and pipeline optimization. Your understanding will be solidified with a final project on rainfall prediction and a comprehensive course-wide exam.
Enroll now and start your journey to confidently building machine learning models with Python!
| Module 1 | Introduction to Machine Learning |
| Module 2 | Linear and Logistic Regression |
| Module 3 | Building Supervised Learning Models |
| Module 4 | Building Unsupervised Learning Models |
| Module 5 | Evaluating and Validating Machine Learning Models |
| Module 6 | Final Project and Exam |