1. Introduction to Machine Learning:
What is machine learning? The role of machine learning in data-driven decision-making. Real-world applications and examples of machine learning. 2. Scikit-learn (sklearn):
Introduction to scikit-learn as a popular Python library for machine learning. Installing scikit-learn. Overview of scikit-learn's main components. 3. Supervised Learning:
Understanding supervised learning. The concept of labeled data and target variables. Examples of supervised learning tasks (classification and regression). 4. Unsupervised Learning:
Understanding unsupervised learning. The absence of target labels. Examples of unsupervised learning tasks (clustering and dimensionality reduction). 5. Data Preparation:
Data cleaning and preprocessing. Data splitting into training and testing sets. Scaling and normalization of data. 6. Supervised Learning Algorithms:
6.1. Classification:
Introduction to classification algorithms (e.g., Logistic Regression, Decision Trees, Random Forest, Support Vector Machines, k-Nearest Neighbors). Model training, evaluation, and hyperparameter tuning. Confusion matrices, precision-recall, and ROC curves. 6.2. Regression:
Introduction to regression algorithms (e.g., Linear Regression, Ridge Regression, Lasso Regression, Support Vector Regression). Model training, evaluation, and hyperparameter tuning. Evaluation metrics like Mean Absolute Error (MAE) and Mean Squared Error (MSE). 7. Unsupervised Learning Algorithms:
7.1. Clustering:
Introduction to clustering algorithms (e.g., K-Means, Hierarchical Clustering, DBSCAN). Model training and evaluation (e.g., silhouette score). Applications of clustering. 7.2. Dimensionality Reduction:
Introduction to dimensionality reduction techniques (e.g., Principal Component Analysis, t-Distributed Stochastic Neighbor Embedding). Model training and visualization of reduced-dimensional data. Applications of dimensionality reduction. 8. Model Selection and Evaluation:
Techniques for model selection and cross-validation. Overfitting and underfitting. Bias-variance trade-off. 9. Scikit-learn Workflow:
A step-by-step guide to using scikit-learn for supervised and unsupervised learning tasks. Model training, prediction, and evaluation. 10. Model Deployment and Integration (Brief Overview): - An introduction to deploying machine learning models in real-world applications.
11. Ethical Considerations in Machine Learning: - Addressing ethical concerns related to data bias, fairness, and responsible AI.