Machine Learning

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.

Content:

•  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