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Predictive Artificial Intelligence

Explore machine-learning applications, data collection, predictive modeling, and feature engineering to solve business problems.

  • START DATE February 25, 2025
  • TIME COMMITMENT 6-8 hours/week
  • DURATION 8 Weeks
  • FORMAT Online
  • PRICE $1,900
Predictive AI is reshaping businesses through historical data and advanced algorithms to forecast trends, optimize operations, and mitigate risks while maintaining a competitive edge. This course equips participants with machine learning skills, covering data collection, preparation, analysis, and model building. It also explores prediction, feature engineering, and modeling techniques through practical use cases, emphasizing result interpretation for non-technical audiences, model trust, and cost-benefit analyses. Participants learn to launch predictive analytics projects, gain stakeholder support, and drive data-driven decision-making.

WHAT YOU WILL LEARN

MIT xPRO is collaborating with online education provider Global Alumni to deliver this online course. By clicking LEARN MORE, you will be taken to a page where you can download the brochure and apply to the program via Global Alumni.

  • Define and deploy predictive AI with diverse data types, while assembling skilled teams to develop models that address real-world challenges.
  • Set KPIs to drive predictive AI initiatives and align them with business objectives. Additionally, develop a detailed predictive AI requirements document.
  • Utilize prediction engineering techniques to build models and extract data for practical use, while working with tools like ComposeML and Pandas.
  • Transform raw data into features to enhance model accuracy using both automated and manual engineering methods, and master Featuretools to simplify the process.
  • Use supervised learning methods and hyperparameters to build predictive models. Assess model accuracy and interpret results with scikit-learn, XGBoost, and PyTorch.
  • Master data anomaly detection and apply it across sectors with unsupervised learning methods. Leverage tools like Orion to enhance model accuracy and efficiency.
  • Evaluate and analyze AI models considering time, personnel and costs. Additionally, track deployment progress using tools such as SHAP and Pyreal.
  • Assemble teams of data experts, stakeholders, and other roles to collaborate on predictive AI projects. Use data assessments, success metrics, and scaling tools.

Who Should Enroll

  • Leaders and managers with business development and administrative experience seeking to learn new tools to make more informed business decisions.

  • Business and data analysts looking to learn more about data science and its practical application.

  • Consultants interested in acquiring technical knowledge around predictive analytics and its practical applications.

  • AI-dedicated and other professionals across industries eager to gain a deeper understanding of the technicalities of predictive artificial intelligence.