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Overview
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.
Key facts
- Throughout this IBM AI Enterprise Workflow Specialization offered by Coursera in partnership with IBM, 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.
Programme Structure
Courses include:
- AI Workflow: Business Priorities and Data Ingestion
- AI Workflow: Data Analysis and Hypothesis Testing
- AI Workflow: Feature Engineering and Bias Detection
- AI Workflow: Machine Learning, Visual Recognition and NLP
- AI Workflow: Enterprise Model Deployment
- AI Workflow: AI in Production
Key information
Duration
- Part-time
- 2 months
- 10 hrs/week
Start dates & application deadlines
You can apply for and start this programme anytime.
Language
English
Delivered
Online
- Self-paced
Campus Location
- Mountain View, United States
Disciplines
Data Science & Big Data Artificial Intelligence Machine Learning View 202 other Short Courses in Machine Learning in United StatesWhat students do after studying
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Academic requirements
We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.
English requirements
We are not aware of any English requirements for this programme.
Other requirements
General requirements
Advanced Level
- Designed for those already in the industry.
- To obtain additional information about the programme, we kindly suggest that you visit the programme website, where you can find further details and relevant resources.
Tuition Fees
Tuition fees are shown in and the most likely applicable fee is shown based on your nationality.
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International Applies to you
Applies to youNon-residentsFree - Out-of-StateFree
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Domestic
Applies to youIn-StateFree
Additional Details
Course is free for the first 7 days. After 7 days, the course can be accessed with the Coursera Plus Subscription
Funding
Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project.
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