Agile Methodology: Principles, Uses and Framework
December 19, 2024
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This course is part of IBM AI Enterprise Workflow Specialization
Instructors: Mark J Grover
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This is the third 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.
Course 3 introduces you to the next stage of the workflow for our hypothetical media company. In this stage of work you will learn best practices for feature engineering, handling class imbalances and detecting bias in the data. Class imbalances can seriously affect the validity of your machine learning models, and the mitigation of bias in data is essential to reducing the risk associated with biased models. These topics will be followed by sections on best practices for dimension reduction, outlier detection, and unsupervised learning techniques for finding patterns in your data. The case studies will focus on topic modeling and data visualization. By the end of this course you will be able to: 1. Employ the tools that help address class and class imbalance issues 2. Explain the ethical considerations regarding bias in data 3. Employ ai Fairness 360 open source libraries to detect bias in models 4. Employ dimension reduction techniques for both EDA and transformations stages 5. Describe topic modeling techniques in natural language processing 6. Use topic modeling and visualization to explore text data 7. Employ outlier handling best practices in high dimension data 8. Employ outlier detection algorithms as a quality assurance tool and a modeling tool 9. Employ unsupervised learning techniques using pipelines as part of the AI workflow 10. Employ basic clustering algorithms Who should take this course? This course 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 course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed that you have completed Courses 1 and 2 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand 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.
This module will introduce you to skills required for effective feature engineering in today's business enterprises. The skills are presented as a series of best practices representing years of practical experience.
6 videos14 readings5 assignments1 ungraded lab
This module will continue the discussion of skill related to feature engineering for practicing data scientists, with a focus on outliers and the use of unsupervised learning techniques for finding patterns.
5 videos11 readings5 assignments1 ungraded lab
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
Instructor ratings
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
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Reviewed on Jul 5, 2020
Dear Team,Namaste !! Well...All Instructer Very Help Full ...Quick Reply for any Queries ...Concept Clearance.Thanks & RegardsNeela Mistry
Reviewed on May 3, 2020
It's quite good but the content could be more in-depth as an 'advance' course.
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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. The certification exam is administered by Pearson VUE and must be taken at one of their testing facilities. You may visit their site at https://home.pearsonvue.com/ for more information.
Please visit the Pearson VUE web site at https://home.pearsonvue.com/ for the latest information on taking the AI Enterprise Workflow certification test.
It is highly recommended that you have at least a basic working knowledge of design thinking and Watson Studio prior to taking this course. Please visit the IBM Skills Gateway at http://ibm.com/training/badges and "Find a Badge" related to "design thinking" or "Watson Studio". From there you will be directed to courses covering these topics.
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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