Learn about Machine Learning patterns and use cases in the real world, while getting a review of statistics and data analytics to be more data-minded, helping to understand 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 set up Anaconda.
- Perform hands-on activity using Jupyter Notebooks.
Topic Outline:
- Course Introduction
- Machine Learning patterns
- Classification
- Clustering
- Regression - Gartner Hype Cycle for Emerging Technologies
- Machine Learning offerings in Industry
- Hands-on exercise 1: Install and Setup Anaconda.
- Python Libraries
- NumPy
- Pandas
- Scikit Learn - Hands-on exercise 2: Data Analysis using Pandas
- Algorithms
- Linear Regression
- Decision Tree - Hands-on 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.
