Learn and master machine learning (ML) concepts, algorithms, and real-world applications while gaining hands-on experience building and evaluating ML models with Python.
This comprehensive course is designed to equip you with a strong foundation in machine learning (ML) through a systematic, step-by-step approach. This course covers the essential principles of supervised and unsupervised learning algorithms, providing a deep understanding of how machine learning models work and how they can be applied in real-world scenarios. You will explore the entire ML workflow, from data collection and preprocessing to model building and evaluation, ensuring you gain practical, hands-on experience at each stage.
Throughout the course, you will master key concepts in data preprocessing, feature engineering, and model evaluation techniques. We will cover a range of core algorithms, including regression, classification, and clustering, as well as evaluation metrics to help you assess model performance and make data-driven decisions. Practical exercises and Python-based implementations will reinforce your understanding and allow you to build predictive models. By the end of the course, you will be equipped to handle complete machine learning projects, from data preparation to evaluation, while ensuring your models are both effective and ethical.
In addition to the technical skills, this course emphasizes the importance of ethical decision-making in AI development. You will explore critical issues like bias, fairness, and accountability in machine learning, learning how to build models that are not only accurate but also responsible and equitable. Whether you want to enhance your career, pursue further studies, or contribute to the growing field of AI, CS207 provides you with the knowledge and skills necessary to create impactful and ethical machine learning systems.
- Unit 1: Introduction to Machine Learning
- Unit 2: Machine Learning Workflow
- Unit 3: Data Preprocessing
- Unit 4: Data Visualization
- Unit 5: Supervised Learning – Regression
- Unit 6: Supervised Learning – Logistic Regression
- Unit 7: Unsupervised Learning – Clustering
- Unit 8: Model Evaluation and Validation
- Unit 9: Practical Implementation of ML Models
- Unit 10: Ethical and Responsible AI
- Explain machine learning concepts, including supervised and unsupervised learning;
- Explain the ML workflow, including data collection, preprocessing, modeling, and evaluation;
- Apply data processing and visualization techniques to prepare data sets, interpret data, and perform feature extraction;
- Implement machine learning models, including regression, classification, and clustering;
- Identify overfitting, underfitting, and other challenges in machine learning models;
- Build end-to-end machine learning projects that include documented workflows and are reproducible;
- Explain the performance of machine learning models using basic metrics; Analyze ethical considerations in machine learning.
- Blank paper for notes or scratch work
- Water or a non-alcoholic beverage