Machine Learning Is For Everyone

This introductory lecture helps in awareness about Machine Learning patterns and use cases in real world.
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.

Topic Outline:

• Course Introduction
• Machine Learning patterns

- Classification
- Clustering
- Regression

• Gartner Hype Cycle for Emerging Technologies
• Machine Learning offerings in Industry
• Exercise 1 - Install and Setup Anaconda.
• Python Libraries

- NumPy
- Pandas
- Scikit Learn

• Exercise 2: Data Analysis using Pandas
• Algorithms

- Linear Regression
- Decision Tree

• Exercise 3: Perform Linear regression using Scikit-learn
• References and Next steps