Machine learning is a subfield of Artificial Intelligence that focuses on enabling machines to learn from their environment autonomously, without external assistance or intervention. The Machine Learning course offers a comprehensive introduction to the principles and techniques of machine learning, enabling students to build predictive models and extract insights from data. The course covers a wide range of topics, including supervised and unsupervised learning, model evaluation, feature selection, and model optimization. Students will learn how to apply machine learning algorithms to real-world datasets and gain hands-on experience in developing machine learning solutions. Through this machine learning course, learners will gain the skills and knowledge necessary to navigate this fascinating field. They will explore the principles, techniques, and tools used in machine learning and discover how to effectively apply them.
- Basic programming skills (preferably in Python)
- Familiarity with data structures and algorithms
- Understanding of probability and statistics
- Knowledge of linear algebra and calculus
- Some exposure to computer science concepts and algorithms
The course adopts a blended learning approach, combining theoretical lectures, practical coding exercises, and hands-on labs. The lectures provide a solid understanding of machine learning principles, algorithms, and techniques. The practical coding exercises help students implement and apply machine learning algorithms to various datasets. The labs offer opportunities to work on larger projects and gain practical experience in building end-to-end machine learning solutions.
Introduction to Machine Learning
Overview of machine learning concepts and applications
Types of machine learning algorithms
The machine learning workflow
Decision trees and ensemble methods (e.g., random forests, gradient boosting)
Model Evaluation and Selection
Training set, validation set, and test set
Evaluation metrics (e.g., accuracy, precision, recall, F1-score)
Cross-validation and hyperparameter tuning
Clustering algorithms (e.g., k-means, hierarchical clustering)
Dimensionality reduction techniques (e.g., principal component analysis, t-SNE)
Feature Engineering and Selection
Handling missing data
Feature scaling and normalization
Feature selection techniques (e.g., forward selection, backward elimination)
Neural Networks and Deep Learning
Introduction to artificial neural networks
Feedforward neural networks and backpropagation
Convolutional neural networks and recurrent neural networks
Model Deployment and Interpretation
Model deployment options (e.g., web services, APIs)
Model interpretation and explainability techniques
Monitoring and updating deployed models
Advanced Topics in Machine Learning
Support Vector Machines (SVM)
Generative Adversarial Networks (GANs)
Upon completing the course, students will:
- Understand the fundamental concepts, algorithms, and techniques of machine learning.
- Be able to apply supervised and unsupervised learning algorithms to solve real-world problems.
- Gain proficiency in popular machine learning libraries and frameworks (e.g., scikit-learn, TensorFlow).
- Evaluate and select appropriate models based on evaluation metrics and validation techniques.
- Perform feature engineering and selection to improve model performance.
- Have hands-on experience in developing and deploying machine learning solutions.
The course includes hands-on labs to reinforce the theoretical concepts and provide practical experience. The labs may include:
- Implementing linear regression for predicting housing prices based on a dataset.
- Building a decision tree classifier for a classification task (e.g., spam detection) using scikit-learn.
- Applying clustering algorithms (e.g., k-means) to group similar data points in a dataset.
- Developing a convolutional neural network (CNN) for image classification on a dataset (e.g., MNIST).
- Performing feature selection and engineering techniques on a dataset to improve model performance.
- Deploying a trained machine learning model as a web service using Flask or other frameworks.