June 21,28 2024
2 Half Day (9:00 a.m to 12:00 p.m PST)
After this course, you will be able to:
- Describe the role of Machine Learning and where it fits into Information Technology strategies
- Explain the technical and business drivers that result from using Machine Learning
- 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
History and background of Machine Learning
Compare Traditional Programming Vs Machine Leaning
Supervised and Unsupervised Learning Overview
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
- Random Forest
- K-Means Clustering
Hands-on exercise 3: Perform Linear regression using Scikit-learn
Hands-on exercise 4: Perform Decision tree on Titanic Data set using Scikit-learn
References and Next steps
