Lilly Tech Systems
IBM AI Engineering Professional
Prepare for the IBM AI Engineering Professional Certificate. Master machine learning with scikit-learn, deep learning with Keras and TensorFlow, IBM Watsonx.ai, and model deployment — with 20+ practice questions and detailed explanations. Earn your IBM digital badge through Coursera.
Your Study Path
Follow these lessons in order for complete preparation, or jump to any topic you need to review.
1. Certification Overview
IBM badges, Coursera pathway, course structure, what to expect, and preparation strategy.
2. ML with Python
Scikit-learn, regression, classification, clustering, and evaluation metrics. Practice questions included.
3. Deep Learning
Keras, TensorFlow, CNNs, RNNs, transfer learning, and model optimization. Practice questions included.
4. IBM Watsonx
Watsonx.ai, foundation models, Prompt Lab, tuning studio, and IBM AI governance. Practice questions included.
5. Model Deployment
Watson Machine Learning, model deployment, REST APIs, batch scoring, and monitoring. Practice questions included.
6. Practice Assessment
20 questions covering all topics with detailed explanations for every answer.
7. Tips & Resources
Cheat sheet, study tips, FAQ accordion, and additional resources.
What You'll Learn
By the end of this course, you will be ready to:
Earn Your IBM Badge
Complete the IBM AI Engineering Professional Certificate on Coursera and earn your IBM digital badge.
Build ML Models
Use scikit-learn for regression, classification, and clustering. Evaluate models with industry-standard metrics.
Master Deep Learning
Build CNNs and RNNs with Keras and TensorFlow. Apply transfer learning for computer vision and NLP tasks.
Deploy with IBM
Use Watsonx.ai for foundation models and Watson ML for deploying models as production APIs.