Every field of computing is being impacted by Machine Learning: software engineering, data analysis, and artificial intelligence.
In this online, self-paced course discover the machine learning models that interpret large amounts of data. Learn to utilize Python’s libraries to solve predictive problems (supervised learning) and data clustering problems (unsupervised learning). Study machine learning techniques such as multiple linear regressions (Ridge and Lasso), generalized linear models and classification, clustering and dimensionality reduction methods. Gain hands-on experience solving complex and simple real-world problems across a broad array of industries. This course concludes with a capstone project in which you complete a small freelance coding assignment.
For the duration of the course you will have 24/7 access to course materials allowing you to save your progress and resume where you left off at any time.
Course Learning Outcomes
By completion of this course, successful students will be able to:
- Describe how machine learning can be used to create models that interpret large amounts of data
- Apply machine learning methods and algorithms in the context of real-world problems
- Explore Machine Learning oriented practical applications of scientific libraries such SciKit-Learn and TensorFlow
- Identify how to formulate learning tasks as computational problems and the methods that are designed to solve these problems
- Design and implement methods for problems in pattern recognition, system identification or predictive analysis
- Complete a capstone project to demonstrate proficiency using Python programming language for Machine Learning
- Introduction to Machine Learning
- Playing with built-in datasets
- Linear regression
- Polynomial regression
- Logistic regression
- Support vector machines
- Decision trees and random forests
- K-nearest neighbors
- Naive Bayes
- Clustering models
- Artificial neural networks and deep learning