Machine Learning Fundamentals
Understand how machine learning works — algorithms, data pipelines, model evaluation, and real-world applications. 8 lessons for beginners, no coding required.
What You'll Learn
- Explain the three types of machine learning — supervised, unsupervised, and reinforcement learning — and when each applies
- Compare common ML algorithms (regression, decision trees, random forests, neural networks) and identify which fits each problem type
- Design a data pipeline with proper feature engineering, train-test splitting, and cross-validation
- Evaluate model performance using accuracy, precision, recall, F1 score, and the bias-variance tradeoff
- Identify the right ML framework for each task — scikit-learn for traditional ML, PyTorch for research, TensorFlow for production
- Assess ethical risks in ML systems including algorithmic bias, fairness, and accountability
Course Syllabus
Frequently Asked Questions
Do I need to know how to code?
No. This course explains machine learning concepts, algorithms, and workflows without requiring you to write code. You'll understand what ML engineers do and how ML systems work — which is valuable whether or not you plan to code. If you want to code ML after this course, Lesson 8 maps your next steps.
Is this the same as a deep learning course?
No. Deep learning is a subset of machine learning that uses neural networks with many layers. This course covers the full ML landscape — including deep learning as one topic alongside traditional algorithms like decision trees, random forests, and regression. Most ML problems are solved with traditional algorithms, not deep learning.
What math background do I need?
Basic algebra is enough. We explain concepts intuitively with real-world examples. If you can understand 'prediction error = actual value minus predicted value,' you have the math you need.
Will this help me get a job in ML?
This course builds foundational understanding. ML engineer roles typically require coding skills and project experience on top of these concepts. Lesson 8 maps the complete learning path from this course to job readiness, including which coding skills and projects to build next.