Course Description: This course introduces students to the field of Machine Learning, covering fundamental concepts, algorithms, and practical applications. Students will gain hands-on experience with popular Machine Learning libraries and frameworks.
Introduction to Machine Learning
- Overview of Machine Learning concepts and applications
- Differentiating between supervised, unsupervised, and reinforcement learning
- Setting up the Python environment for Machine Learning
Data Preprocessing and Exploration
- Data cleaning, transformation, and feature engineering
- Handling missing values and outliers
- Data visualization and exploratory data analysis (EDA)
Supervised Learning - Regression
- Linear regression and multiple linear regression
- Polynomial regression and model evaluation metrics
- Regularization techniques (Lasso, Ridge) and feature selection
Supervised Learning - Classification
- Logistic regression for binary classification
- K-Nearest Neighbors (KNN) and Support Vector Machines (SVM)
- Decision trees and ensemble methods (Random Forest, Gradient Boosting)
Unsupervised Learning - Clustering
- K-Means clustering and hierarchical clustering
- Density-based clustering (DBSCAN)
- Evaluating clustering performance
Dimensionality Reduction and Feature Extraction
- Principal Component Analysis (PCA)
- t-distributed Stochastic Neighbor Embedding (t-SNE)
- Applications of dimensionality reduction
Introduction to Deep Learning
- Basics of neural networks and activation functions
- Building and training a simple feedforward neural network
- Introduction to convolutional and recurrent neural networks
Final Projects and Capstone
- Applying Machine Learning concepts to real-world datasets
- Conducting a complete Machine Learning project
- Final project presentation and evaluation
Assessment:
- Class participation and engagement
- Homework assignments and coding exercises
- Mid-term and final projects
- Final project presentation and evaluation