Start when you are ready. Once you start with the first course, you will be assigned a new course every week.
Complete the Machine Learning Path over 8 weeks or set your own pace.
Take an assessment at the end of each course. After successfully passing the exam, you will earn a badge.
Complete all 8 badges to earn a certification.
You will have access to a monthly mentoring webinar to help you complete the Certification Path.
This machine learning primer follows two main threads: foundational concepts and practical examples. We will identify modeling opportunities and categorize them. This course will help you design your own analytics solutions and solve a broad range of problems. It will teach you how to generate value from the data assets and communicate to your stakeholders the importance and meaning of models in data-intensive environments.
With the Complete Python Fundamentals course, you will work on your ability to leverage this versatile language with this fundamentals course designed to take you from zero experience to a powerful, practical use of this language. Python is the most popular programming language in the world of data science and you will learn how to write functions and manipulate data.
SQL it’s the most commonly used tool among data scientists today and is an invaluable tool in your journey in Machine Learning Certification. By completing this workshop, you will develop a working knowledge of how to explore a relational database, how to use SQL to retrieve data from that database, and how to transform it to answer your data science questions.
This session offers a comprehensive introduction to the powerful Pandas library for data analysis built on top of the Python programming language. By completing this workshop, you'll have a strong foundation for using Pandas in your day-to-day data analysis needs.
This course will provide you with a comprehensive overview of Linear Algebra and Calculus. Dr. Krohn will cover Intro to Linear Algebra, Matrix Operations, Limits & Derivatives and Partial Derivatives & Integrals.
The statistics for Machine Learning course will cover all the main aspects of Probability and Statistics including topics in Frequentist Statistics, Regression, and Bayesian Statistics.
Supervised Machine Learning walks you through all steps of the classical supervised machine learning pipeline. This course covers topics like cross-validation and splitting strategies, evaluation metrics, supervised machine learning algorithms (like linear and logistic regression, support vector machines, and tree-based methods like the random forest, gradient boosting, and XGBoost), and interpretability.
Most of the world’s data is unlabeled, and applying machine learning to this unlabeled data to solve real world problems is one of the great challenges of artificial intelligence. This course shows why unsupervised learning is so critical to working with data, especially if the data is not only unlabeled but is very large scale and high volume. This course compares unsupervised learning with supervised learning and later combines the two approaches to develop semi-supervised learning solutions.
Certification courses are available to everyone. However, successful course completion badges and an assessed certificate exam award is only available to premium annual subscribers.