Statistics.com courses below can be audited free for 30 days or you can choose to receive a verified certificate for a small fee. Verified students will have access to all course materials including graded assignments and labs (if applicable). You will have unlimited access to this course material until the course ends and receive a certificate (upon successful completion) to add to your LinkedIn profile or resume,
Select a course to learn more.
Deploying AI & ML Models in Production using Amazon Web Services
Deploying AI & ML Models in Production using Microsoft Azure Machine Learning
Deploying AI & ML Models in Production using Google Cloud Platform
Data Pipeline Automation & Optimization using Amazon Web Services
Data Pipeline Automation & Optimization using Microsoft Azure Machine Learning
Data Pipeline Automation & Optimization using Google Cloud Platform
Basic Modeling Techniques
Concern about the harmful effects of machine learning algorithms and AI models (bias and more) has resulted in greater attention to the fundamentals of data ethics.
AI’s popularity has resulted in numerous well-publicized cases of bias, injustice, and discrimination.
Machine Learning Operations (MLOps) lies at the core of the AI Engineering function. In Statistics.com’s MLOps with AWS program you will learn to combine data engineering and data science skills to deploy machine learning models.
Machine Learning Operations (MLOps) lies at the core of the AI Engineering function. In Statistics.com’s MLOps with GCP program you will learn to combine data engineering and data science skills to deploy machine learning models.
Machine Learning Operations (MLOps) lies at the core of the AI Engineering function. In Statistics.com’s MLOps with Azure program you will learn to combine data engineering and data science skills to deploy machine learning models.
Sometimes data science projects go awry, when the predictions made by statistical and AI / machine learning algorithms turn out to be not just wrong, but biased and unfair in ways that cause harm. This program provides responsible guidance and practical tools to build better models and avoid these problems.
