Quick Note on Pricing:
We believe education should be accessible, so if you'd like to get this course without any cost, please return during each of the following days and use the provided coupon format:
During the first 3 days of the month (1-3), enter "SMLCCOURSE" followed by the month (eg. "01" for January and "11" for November) followed by "1"—here is an example for a coupon during the first three days of December: "SMLCCOURSE121"
For days 10-13 of the month, enter "SMLCCOURSE" followed by the month (eg. "01" for January and "11" for November) followed by "2"—here is an example for December: "SMLCCOURSE122"
For days 20-23 of the month, enter "SMLCCOURSE" followed by the month (eg. "01" for January and "11" for November) followed by "1"—here is an example for December: "SMLCCOURSE123"
However, if you'd like to support us, you can always pay for the course. All proceeds will go towards making AI education more accessible.
Interested in machine learning but confused by the jargon? If so, we made this course for you.
Machine learning is the fastest-growing field with constant groundbreaking research. If you're interested in any of the following, you'll be interested in ML:
And so much more!
No past knowledge is required: we'll start with the basics of Python and end with gradient-boosted decision trees and neural networks. The course will walk you through the fundamentals of machine learning, explaining mathematical foundations as well as practical implementations. By the end of our course, you'll have worked with five public data sets and have implemented all essential supervised learning models. After the course's completion, you'll be equipped to apply your skills to Kaggle data science competitions, business intelligence applications, and research projects.
We made the course quick, simple, and thorough. We know you're busy, so our curriculum cuts to the chase with every lecture. If you're interested in the field, this is a great course to start with.
Here are some of the Python libraries you'll be using:
Numpy (linear algebra)
Pandas (data manipulation)
Seaborn (data visualization)
Scikit-learn (optimized machine learning models)
Keras (neural networks)
XGBoost (gradient-boosted decision trees)
Here are the most important ML models you'll use:
Not convinced yet? By taking our course, you'll also have access to sample code for all major supervised machine learning models. Use them how you please!
Start your data science journey today with The Complete Intro to Machine Learning with Python.