Rutgers AI Bootcamp

The AI Boot Camp program gives learners the knowledge and skills to conduct robust analytics on a host of real-world problems. Earner demonstrated proficiency with multiple technologies, including Python, Pandas, unsupervised and supervised machine learning, neural networks, and natural language processing. Developed experience creating predictive models and evaluating performance while working in a group setting under tight deadlines. To graduate, participants complete and deploy a portfolio and demonstrate the agility to rapidly develop proficiency with a wide range of new technologies.

The course includes a total of 240 contact hours for a total of 24 continuing education units.

Earning Criteria

• Demonstrating Collaborative Data Preparation for AI:

  • For their first project, students are tasked to collaborate in small groups to source, prepare and analyze data for predictive, machine learning models to use for forecasting time series data. Once completed, students are expected to prepare a professional presentation where they demonstrate interview skills by speaking technically about different project features while also explaining the development process.

• Demonstrating Collaborative Machine Learning Fundamentals:

  • For their second project, students work in groups to train machine learning models, optimize the models, and evaluate model performance toward a specific application. Upon completion, students are asked to effectively demonstrate and explain through professional group presentations which model worked best for their application.

• Final Project on Natural Language Processing and AI Applications:

  • Learners work in groups to leverage deep learning and advanced AI language models to solve a problem in artificial intelligence related to natural language processing.

• In addition to the three projects, students must complete and turn in all but two homework assignments to stay eligible for the course certificate.

Learning Outcomes

At the conclusion of this course, learners will be able to:

• Create Python-based scripts to automate the cleanup, restructuring, and rendering of large, heterogeneous datasets.

• Interact with APIs using Python Requests and JSON parsing techniques.

• Create in-depth graphs, charts, and tables utilizing a wide-variety of data-driven programming languages and libraries.

• Apply machine learning techniques to gain knowledge and solve problems.

• Use unsupervised machine learning models to categorize unlabeled data.

• Use supervised machine learning models trained on labeled data to make predictions about data.

• Evaluate and improve the performance of machine learning models by using test data, metrics, and optimization techniques.

• Use neural networks and deep learning models to make predictions about data.

• Determine the sentiment of vector-encoded text using NLP and transformers.

• Apply the fundamentals of NLP and transformer models to describe how Generative AI creates content.

• Describe recent innovations in AI and their impact on the field of AI.