About this Course
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Flexible deadlines

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Advanced Level

Approx. 7 hours to complete

Suggested: This course requires 7.5 to 9 hours of study....

English

Subtitles: English

Skills you will gain

Data ScienceInformation EngineeringArtificial Intelligence (AI)Machine LearningPython Programming

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Advanced Level

Approx. 7 hours to complete

Suggested: This course requires 7.5 to 9 hours of study....

English

Subtitles: English

Syllabus - What you will learn from this course

Week
1
4 hours to complete

Data transforms and feature engineering

6 videos (Total 31 min), 14 readings, 5 quizzes
6 videos
Introduction to Class Imbalance1m
Class Imbalance Deep Dive9m
Introduction to Dimensionality Reduction2m
Dimension Reduction13m
Case study intro / Feature Engineering1m
14 readings
Data Transformation: Through the eyes of our Working Example3m
Transforms / Scikit-learn3m
Pipelines3m
Class imbalance: Through the eyes of our Working Example3m
Class Imbalance5m
Sampling techniques2m
Models that naturally handle imbalance2m
Data bias2m
Dimensionality Reduction: Through the eyes of our Working Example3m
Why is dimensionality reduction important?3m
Dimensionality reduction and Topic models5m
Topic modeling: Through the eyes of our Working Example3m
Getting Started with the topic modeling case study (hands-on)2h
Data transforms and feature engineering: Summary/Review5m
5 practice exercises
Getting Started: Check for Understanding2m
Class imbalance, data bias: Check for Understanding2m
Dimensionality Reduction: Check for Understanding3m
CASE STUDY - Topic modeling: Check for Understanding2m
Data transforms and feature engineering:End of Module Quiz10m
Week
2
3 hours to complete

Pattern recognition and data mining best practices

4 videos (Total 10 min), 11 readings, 5 quizzes
4 videos
Introduction to Outliers2m
Outlier Detection3m
Introduction to Unsupervised learning2m
11 readings
ai360: Through the eyes of our Working Example3m
Introduction to ai360 (hands-on)15m
Outlier detection: Through the eyes of our Working Example3m
Outliers3m
Unsupervised learning: Through the eyes of our Working Example3m
An overview of unsupervised learning2m
Clustering3m
Clustering evaluation3m
Clustering: Through the eyes of our Working Example3m
Getting Started with the clustering case study (hands-on)2h 10m
Pattern recognition and data mining best practices: Summary/Review4m
5 practice exercises
ai360 Tutorial: Check for Understanding2m
Outlier detection: Check for Understanding2m
Unsupervised learning: Check for Understanding2m
CASE STUDY - Clustering: Check for Understanding2m
Pattern recognition and data mining best practices: End of Module Quiz12m

Instructors

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Mark J Grover

Digital Content Delivery Lead
IBM Data & AI Learning
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Ray Lopez, Ph.D.

Data Science Curriculum Leader
IBM Data & Artificial Intelligence

About IBM

IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and the world's most advanced servers and supercomputers. Utilizing its business consulting, technology and R&D expertise, IBM helps clients become "smarter" as the planet becomes more digitally interconnected. IBM invests more than $6 billion a year in R&D, just completing its 21st year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 5 Nobel Laureates, 9 US National Medals of Technology, 5 US National Medals of Science, 6 Turing Awards, and 10 Inductees in US Inventors Hall of Fame....

About the IBM AI Enterprise Workflow Specialization

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....
IBM AI Enterprise Workflow

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

  • This course assumes that you are already familiar with basic data science concepts including probability and statistics, linear algebra, machine learning, and the use of Python and Jupyter. It is assumed you have completed the first two courses of the specialization: AI Workflow: Business Priorities and Data Ingestion, AI Workflow: Data Analysis and Hypothesis Testing.

  • 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. The exercises in the last two modules of the course are heavily focused on deployment and testing of machine learning models and use the IBM Watson tooling found on the IBM Cloud.

  • Yes. All IBM Cloud Data and AI services are based upon open source technologies.

  • The exercises in the course may be completed by anyone using the IBM Cloud "Lite" plan, which is free for use.

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