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.
IBM AI Enterprise Workflow 專項課程
此课程是 100% 在线学习吗？是否需要现场参加课程？
The entire specialization will require 35-40 hours of study. Each of the 6 courses requires 4 to 9 hours of study each.
What background knowledge is necessary?
It is assumed you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understanding of sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process. If you are unsure, Course 1 includes a Readiness Exam you can take to see if you are prepared.
Do I need to take the courses in a specific order?
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.
Sorry, you will not.
What will I be able to do upon completing the Specialization?
By the end of this specialization you will be able to:
1. Build an end to end AI solution.
2. Leverage Design Thinking as a framework to work through the translation of business goals into AI technical implementations.
3. Bring together different capabilities such as Machine Learning, and specialized AI use cases.
4. Leverage Python as the tool of choice for building AI models, while integrating IBM technologies to facilitate enterprise tasks such as cross-collaboration for the creation of machine learning models, employing out-of-the-box trained models for natural language processing and visual recognition, and deploying models to production.
Who should take this specialization?
This specialization targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this specialization is NOT for you as you need real world expertise to benefit from the content of these courses.
Can I take the certification exam here on Coursera?
How much does the certification exam cost?
I don't know anything about design thinking or Watson Studio. Can I still take this course?
Do I have to use the IBM Cloud tools for this course?
No. Most of the exercises may be completed with open source tools running on your personal computer. However, the exercises are designed with an enterprise focus and are intended to be run in an enterprise environment that allows for easier sharing and collaboration. Some of the exercises in this specialization are heavily focused on deployment and testing of machine learning models and use the IBM Watson tooling found on the IBM Cloud.
Can I use my favorite open source tools for this course?
Yes. All IBM Cloud Data and AI services are based upon open source technologies.
What is the cost for using the IBM Cloud tools for this course?
The exercises in the course may be completed by anyone using the IBM Cloud "Lite" plan, which is free for use.
What tools are required to complete the courses in this specialization?