Sebastian Thrun’s Introduction To Machine Learning
Freerobust introduction to the subject and also the foundation for a Data Analyst “nanodegree” certification sponsored by Facebook and MongoDB.
FreeFree tier
About Sebastian Thrun’s Introduction To Machine Learning
This class will teach you the end-to-end process of investigating data through a machine learning lens, and you'll apply what you've learned to a real-world data set. The course covers 19 lessons including Naive Bayes, Support Vector Machines, Decision Trees, Regressions, Clustering, Feature Selection, PCA, Validation, Evaluation Metrics, and more. Taught by Sebastian Thrun (Founder of Udacity) and Katie Malone. No prior technical experience is required; only fluency in written and spoken English. The course is free and rated 4.7 out of 5.
Key Features
19 lessons covering key ML algorithms including Naive Bayes, SVM, Decision Trees, Regressions, Clustering, PCA, and more
Taught by Sebastian Thrun and Katie Malone
Free course with no technical prerequisites
Hands-on projects using the Enron dataset
Covers classification, regression, clustering, feature selection, validation, and evaluation metrics
Pros & Cons
Pros
- Free access to high-quality content
- Taught by industry experts including Sebastian Thrun
- Covers a broad range of ML topics in depth
- Includes hands-on mini-projects with a real dataset
- No prior technical experience required
- Self-paced learning with clear course outline
Cons
- Intermediate level may require some basic mathematical understanding
- Only one skill (linear regression) listed explicitly despite covering many algorithms
- Limited interactivity compared to paid Udacity nanodegree programs
Best For
Learning machine learning from scratchPreparing for a career in data science or analyticsUnderstanding and applying ML algorithms to real-world dataBuilding a foundation for advanced ML and deep learning courses
FAQ
Is this course free?
Yes, the course is free.
Do I need any prior experience?
No prior technical experience is required, but you need to be able to communicate fluently in written and spoken English.
What topics are covered?
Topics include Naive Bayes, Support Vector Machines, Decision Trees, Regressions, Clustering, Feature Scaling, Feature Selection, PCA, Validation, Evaluation Metrics, and more.