Machine Learning in Practice

This course is an advanced data analysis and prediction techniques and tools with applications.

List of Topics

• Fundamental concepts of machine learning and its applications.

• Overview of Supervised and Unsupervised Learning

• Data representation and features engineering

• Model Evaluation and Improvement

• Algorithm Chains and Pipelines

• Working with Text data

• Working with Image data

• Introduction to Deep learning models

• Machine learning applications in health sciences

Required book

• Introduction to Machine Learning with Python: A Guide for Data Scientists, By Sarah Guido, Andreas Müller

References

• Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning

• Real-World Machine Learning, by Henrik Brink, Joseph W. Richards, and Mark Fetherolf

• Deep Learning: A Practitioner’s Approach, by Josh Patterson and Adam Gibson. 2017. 

Prerequisite

PH1976-Fundamentals of Data Analytics and Predictions and good practical Python programming skills