Note: This is a lecture. A laptop is recommended, but not required.
This introductory lecture helps in awareness about Machine Learning patterns and use cases in real world. We'll review statistics and data analytics to drive all participants to be more data-minded, providing an understanding of the Data Science process.
After this course, you will be able to:
- Describe Supervised and Unsupervised learning techniques and usages
- Understand techniques like Classification, Clustering and Regression
- Discuss how to identify which kinds of technique to be applied for specific use case
- Understand the popular Machine offerings like Amazon Machine Learning, TensorFlow, Azure Machine Learning, Spark mlib, Python and R etc.
- Install and Setup Anaconda.
- Perform hands-on activity using Jupyter Notebooks.
- Course Introduction
- Machine Learning patterns
- Gartner Hype Cycle for Emerging Technologies
- Machine Learning offerings in Industry
- Exercise 1 - Install and Setup Anaconda.
- Python Libraries
- Scikit Learn
- Exercise 2: Data Analysis using Pandas
- Linear Regression
- Decision Tree
- Exercise 3: Perform Linear regression using Scikit-learn
- References and Next steps
University IT Technology Training classes are only available to Stanford University staff, faculty, or students. A valid SUNet ID is needed in order to enroll in a class.