Enroll in this training and receive a one-month complimentary e-learning subscription with access to 40+ courses.
This course is provided by Big Data Trunk for Stanford Technology Training Program but a limited few seats available to the public.
Students of this class may have opportunity to be considered for Internship with Big Data Trunk.
This class helps increase awareness about Machine Learning patterns and use cases in the real world, and will help you understand the different ML techniques. Learn about popular ML offerings, and utilize Jupyter Notebooks to perform hands-on labs.
Prerequisite: Basic Python Programming training, or equivalent experience
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
