Predictive Artificial Intelligence

Apply predictive analytics, machine learning, and feature engineering to design data-driven solutions for business problems.

Course Overview

Master the practical use of predictive AI to solve real business problems. This hands-on course guides you through the full analytics workflow—from data collection and preparation to building, training, and deploying predictive analytical models. Explore key techniques like feature engineering, regression, and classification through real-world cases such as credit card fraud detection. Learn to communicate results clearly, build stakeholder trust, and lead impactful, data-driven initiatives in your organization.

Who is this virtual course for?

Mid-Level to Senior Professionals in roles like data science, analytics, business intelligence, product innovation, and technology leadership/consulting across industries such as finance, healthcare, retail, tech and manufacturing.
Tech leaders, Data Engineers, and Analysts seeking to upskill or pivot into AI-driven roles.
Data Scientists and Data Engineers looking to integrate predictive analytics and AI into their workflow.
Business Intelligence Analysts wanting to move from traditional reporting to more advanced predictive modeling.
Tech and Product Managers who oversee data-driven products and want to understand predictive capabilities.
Founders or Chief Technology Officers (CTOs) or Chief Data Officers (CDOs) seeking to lead AI transformation within their organizations.

Throughout this course, you will:

Define predictive analytics and explain its benefits for business development and administration.
Identify the fundamental data types for predictive analytics and how to collect, prepare, and analyze data.
Build and evaluate predictive analytics models using popular tools, techniques, and forecasting methods.
Apply predictive analytics to solve real-world business problems.

Course Outline

1Introduction to Predictive AI

2Mapping a Business Outcome to Predictive AI

3Data to AI: Prediction Engineering

4Data to AI: Features Engineering

5Data to AI: Modeling

6Anomaly Detection

7Deploying Predictive AI

8How to Develop a Predictive AI Project Roadmap

Meet your instructor

Kalyan Veeramachaneni

Principal Research Scientist, MIT Schwarzman College of Computing

The MIT xPRO learning experience

LEARN BY DOING

Practice processes and methods through simulations, evaluations, case studies, and tools.

 LEARN FROM OTHERS

Connect with an international community of professionals while working on projects based on real-world examples.

LEARN ON DEMAND

Access all content online and watch videos on the go.

REFLECT AND APPLY

Apply your newly acquired skills in your organization, using examples from technical orking environments and informed, practical advice.

DEMONSTRATE YOUR SUCCESS

Earn a Professional Certificate and 4.8 Continuing Education Units (CEUs) from MIT.

LEARN FROM THE BEST

Gain insights from MIT faculty and industry experts.

By the end of this course, you will be able to:

By the end of the Predictive AI Course, participants will gain the skills to leverage machine learning for strategic decision-making and achieving business goals. They will be able to:

Define and apply predictive AI using various data types. Assemble skilled teams and develop models based on key steps to solve real-world challenges.
Establish KPIs to guide predictive AI initiatives and align business goals with AI solutions. Create a comprehensive predictive AI requirements document.
Apply prediction engineering techniques to build models and extract data for practical applications. Use tools like FeatureTools and Pandas.
Convert raw data into features to boost model accuracy with automated and human-driven engineering, and master Featuretools to streamline this process.
Use supervised learning methods to build predictive models using hyperparameters. Evaluate models and interpret results with scikit-learn, XGBoost, and Pyreal.
Master data anomaly detection and apply it across sectors using unsupervised learning methods and tools like Orion to boost model accuracy and efficiency.
Analyze and evaluate AI models in terms of time, resources, and cost. Track deployment progress with tools like SHAP and Pyreal.
Assemble data experts, stakeholders, and bridge roles for collaboration in predictive AI projects with data assessments, success metrics, and scaling tools.