Master Scikit-Learn, the most powerful Python library for Machine Learning! This course covers everything from data preprocessing, regression, classification, clustering, dimensionality reduction, hyperparameter tuning, and time series forecasting. Learn through real-world projects, including house price prediction, spam detection, customer segmentation, and time series forecasting.
By the end, you'll be able to build, optimize, and deploy ML models with confidence. Perfect for beginners to advanced learners looking to apply Machine Learning in real-world scenarios.
What you'll learn
- Introduction to Scikit-Learn – Install, set up, and understand its core functionality.
- Data Preprocessing – Handle missing values, encode categorical data, and scale numerical features.
- Regression Models – Build Linear, Polynomial, Ridge, and Lasso regression models.
- Classification Models – Train Logistic Regression, Decision Trees, SVMs, and Naïve Bayes classifiers.
- Model Evaluation & Performance Metrics – Use MAE, RMSE, R² Score, Confusion Matrix, Precision, Recall, and F1-score.
- Clustering & Unsupervised Learning – Implement K-Means, Hierarchical Clustering, and DBSCAN.
- Dimensionality Reduction – Apply PCA and t-SNE for feature selection and visualization.
- Hyperparameter Tuning – Optimize models using GridSearchCV and RandomizedSearchCV.
- Time Series Forecasting – Use Scikit-Learn for predicting future trends.
- Real-World Projects – Work on multiple projects like house price prediction, spam detection, customer segmentation, and time series forecasting.
Requirements
- Basic knowledge of Python (variables, loops, functions).
- Familiarity with NumPy & Pandas for data handling.
- Basic understanding of Machine Learning concepts (helpful but not mandatory).
- A system with Python installed (Anaconda or standalone).
- Jupyter Notebook or any Python IDE (VS Code, PyCharm, etc.).
- Willingness to learn and apply ML concepts through real-world projects.
