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