Course Syllabus
Welcome to CS207: Fundamentals of Machine Learning
Specific information about this course and its requirements can be found below. For more general information about taking Saylor Academy courses, including information about Community and Academic Codes of Conduct, please read the Student Handbook.
Course Description
Learn and master machine learning (ML) concepts, algorithms, and real-world applications while gaining hands-on experience building and evaluating ML models with Python.
Course Introduction
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.
This course includes the following units:
- 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
Course Learning Outcomes
Upon successful completion of this course, you will be able to:
- 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.
Throughout this course, you will also see learning outcomes in each unit. You can use those learning outcomes to help organize your studies and gauge your progress.
Course Materials
This course's primary learning materials are articles, lectures, and videos.
All course materials are free to access and can be found in each unit of the course. Pay close attention to the notes that accompany these course materials, as they will tell you what to focus on in each resource and will help you understand how the learning materials fit into the course as a whole. You can also see a list of all the learning materials in this course by clicking on Resources in the navigation bar.
Evaluation and Minimum Passing Score
Only the final examination is considered when awarding you a grade for this course. To pass this course, you will need to earn 70% or higher on the final exam.
Your score on the exam will be calculated as soon as you complete it. There is a 14-day waiting period between each attempt. You may only attempt the final exam a maximum of three times. Be sure to study in between each attempt! If you do not pass the exam after three attempts, you will not complete this course.
There is also a practice exam that you may take as many times as you want to help you prepare for the final exam. The course also contains end-of-unit assessments in this course. The end-of-unit assessments are designed to help you study and do not factor into your final course grade. You can take these as many times as you want to until you understand the concepts and material covered. You can see all of these assessments by clicking on Quizzes in the course's navigation bar.
Continuing Education Credits
The certificate earned by passing this self-paced course displays not only the program hours you completed, but also continuing education credits (CEUs) for documenting successful completion of courses that are designed to improve the knowledge and skills of working adults. Many industries value CEUs, and now your certificate reflects them clearly, and they may be used to support career advancement or to meet professional licensing standards. This course contains 1.9 CEUs.
Tips for Success
CS207: Fundamentals of Machine Learning is a self-paced course, meaning you can decide when to start and complete the course. We estimate the "average" student will take hours to complete. We recommend studying at a comfortable pace and scheduling your study time in advance.
Learning new material can be challenging, so here are a few study strategies to help you succeed:
- Take notes on terms, practices, and theories. This helps you understand each concept in context and provides a refresher for later study.
- Test yourself on what you remember and how well you understand the concepts. Reflecting on what you've learned improves long-term memory retention.
Technical Requirements
This course is delivered entirely online. You will need access to a computer or web-capable mobile device and consistent internet access to view or download resources and complete auto-graded assessments and the final exam.
To access the full course, including assessments and the final exam, log into your Saylor Academy account and enroll in the course. If you don’t have an account, you can create one for free here. Note that tracking progress and taking assessments require login.
For more details and guidance, please review our complete Technical Requirements and our student Help Center.
Optional Saylor Academy Mobile App
You can access all course features directly from your mobile browser, but if you have limited internet connectivity, the Saylor Academy mobile app provides an option to download course content for offline use. The app is available for iOS and Android devices.
Fees
This course is entirely free to enroll in and access. All course materials, including textbooks, videos, webpages, and activities, are available at no charge. This course also contains a free final exam and course completion certificate.