Before you start
What you will learn
- Supervised vs Unsupervised Machine Learning
- How Statistical Modeling relates to Machine Learning, and how to do a comparison of each.
- Different ways machine learning affects society
Applications of Machine Learning
Supervised vs Unsupervised Learning
Python libraries suitable for Machine Learning
Module 2 - Regression
Model evaluation methods
Module 3 - Classification
Support Vector Machines
Module 4 - Unsupervised Learning
Module 5 - Recommender Systems
Content-based recommender systems
You'll look at real-life examples of Machine Learning and how it affects society in ways you may not have guessed!
We'll explore many popular algorithms including Classification, Regression, Clustering, and Dimensional Reduction and popular models such as Train/Test Split, Root Mean Squared Error and Random Forests.
Most importantly, you will transform your theoretical knowledge into practical skill using hands-on labs. Get ready to do more learning than your machine!
Who can take this course?
Unfortunately, learners from one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. EdX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.
IBM's Python Data Science Professional Certificate
Earn a Professional Certificate in 2-4 months if courses are taken one at a time.View the program
- 10–20 hours of effort
In this course, you will learn how to analyze data in Python using multi-dimensional arrays in numpy, manipulate DataFrames in pandas, use SciPy library of mathematical routines, and perform machine learning using scikit-learn!
- 18–24 hours of effort
Create a project that you can use to showcase your Data Science skills to prospective employers. Apply various data science and machine learning techniques to analyze and visualize a data set involving a real life business scenario and build a predictive model.
- 10–20 hours of effort
Data visualization is the graphical representation of data in order to interactively and efficiently convey insights to clients, customers, and stakeholders in general.
- Machine Learning with Python: A Practical Introduction
- 2–5 hours of effort
This Python course provides a beginner-friendly introduction to Python for Data Science. Practice through lab exercises, and you'll be ready to create your first Python scripts on your own!
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