IBM AI Enterprise Workflow

This first course introduces learners to the scope of the specialization, prerequisites, and the concept of design thinking. It also covers the basics of scientific thinking and guides learners through building a data ingestion pipeline using Python and Jupyter notebooks.

This course focuses on exploratory data analysis, best practices for data visualization, handling missing data, and hypothesis testing. Learners will apply estimation techniques, probability distributions, and null hypothesis significance tests through case studies.

Learners will delve into feature engineering, class imbalances, bias detection in data, dimension reduction, outlier detection, and unsupervised learning techniques. Case studies focus on topic modeling and data visualization.

This course covers evaluation metrics, model selection, linear models, tree-based models, neural networks, and out-of-the-box Watson models for natural language understanding and visual recognition. Case studies focus on natural language processing and image analysis.

Learners will explore Apache Spark for running machine learning models, best practices for data manipulation, model training, and tuning. The course culminates with creating and deploying a recommender system and an introduction to model deployment technologies.

This final course focuses on deploying models in a hypothetical streaming media company, using IBM Watson Machine Learning, Docker containers, Kubernetes, and other tools in the IBM ecosystem. It also covers feedback loops and unit testing in model production.