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Mark J Grover and Ray Lopez, Ph.D.

This six course specialization is designed to prepare you to take the certification examination for IBM AI Enterprise Workflow V1 Data Science Specialist. IBM AI Enterprise Workflow is a comprehensive, end-to-end process that enables data scientists to build AI solutions, starting with business priorities and working through to taking AI into production. The learning aims to elevate the skills of practicing data scientists by explicitly connecting business priorities to technical implementations, connecting machine learning to specialized AI use cases such as visual recognition and NLP, and connecting Python to IBM Cloud technologies. The videos, readings, and case studies in these courses are designed to guide you through your work as a data scientist at a hypothetical streaming media company.

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This six course specialization is designed to prepare you to take the certification examination for IBM AI Enterprise Workflow V1 Data Science Specialist. IBM AI Enterprise Workflow is a comprehensive, end-to-end process that enables data scientists to build AI solutions, starting with business priorities and working through to taking AI into production. The learning aims to elevate the skills of practicing data scientists by explicitly connecting business priorities to technical implementations, connecting machine learning to specialized AI use cases such as visual recognition and NLP, and connecting Python to IBM Cloud technologies. The videos, readings, and case studies in these courses are designed to guide you through your work as a data scientist at a hypothetical streaming media company.

Throughout this specialization, the focus will be on the practice of data science in large, modern enterprises. You will be guided through the use of enterprise-class tools on the IBM Cloud, tools that you will use to create, deploy and test machine learning models. Your favorite open source tools, such a Jupyter notebooks and Python libraries will be used extensively for data preparation and building models. Models will be deployed on the IBM Cloud using IBM Watson tooling that works seamlessly with open source tools. After successfully completing this specialization, you will be ready to take the official IBM certification examination for the IBM AI Enterprise Workflow.

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What's inside

Six courses

AI Workflow: Business Priorities and Data Ingestion

(0 hours)
This first course of a six part specialization introduces you to the scope of the specialization and prerequisites. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones.

AI Workflow: Data Analysis and Hypothesis Testing

(1 hours)
This course, the second in the IBM AI Enterprise Workflow Certification specialization, focuses on exploratory data analysis (EDA). You will learn best practices for data visualization, handling missing data, and hypothesis testing. You will also learn techniques of estimation with probability distributions and extending these estimates to apply null hypothesis significance tests. By the end of this course, you should be able to:

AI Workflow: Feature Engineering and Bias Detection

(1 hours)
This third course in the IBM AI Enterprise Workflow Certification specialization introduces best practices for feature engineering, handling class imbalances, and detecting bias in data. By the end of this course, you will be able to employ the tools that help address class and class imbalance issues, explain the ethical considerations regarding bias in data, employ AI Fairness 360 open-source libraries to detect bias in models, and more.

AI Workflow: Machine Learning, Visual Recognition and NLP

(1 hours)
This is the fourth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones.

AI Workflow: Enterprise Model Deployment

(1 hours)
This course introduces you to deploying models for use in large enterprises. You will learn about Apache Spark, a commonly used framework for running machine learning models, and best practices for using it. You will also learn about data manipulation, model training, and model tuning. The course wraps up with an introduction to model deployment technologies.

AI Workflow: AI in Production

(1 hours)
This course focuses on models in production at a hypothetical streaming media company. It introduces IBM Watson Machine Learning and Docker for API deployment. Kubernetes is covered for container management. Other IBM tools for production model deployment and maintenance are also introduced. Feedback loops in AI workflow are discussed to promote efficient iteration. By the end of this course, you will be able to:

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