IBM
AI Workflow: Machine Learning, Visual Recognition and NLP
IBM

AI Workflow: Machine Learning, Visual Recognition and NLP

This course is part of IBM AI Enterprise Workflow Specialization

Taught in English

Mark J Grover
Ray Lopez, Ph.D.

Instructors: Mark J Grover

4,832 already enrolled

Included with Coursera Plus

Course

Gain insight into a topic and learn the fundamentals

4.5

(70 reviews)

Advanced level
Designed for those already in the industry
13 hours (approximately)
Flexible schedule
Learn at your own pace

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

11 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.5

(70 reviews)

Advanced level
Designed for those already in the industry
13 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

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Build your subject-matter expertise

This course is part of the IBM AI Enterprise Workflow Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
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There are 2 modules in this course

This week covers model selection, evaluation and performance metrics. The focus is on evaluating models iteratively for improvements. You will survey the landscape of evaluation metrics and linear models in order to ensure you are comfortable using implementing baseline models. The materials build up to the case study where you will use natural language processing in a classification setting. When you are done iterating on your model you will connect its model performance to business metrics as an approach to better understand model utility.

What's included

6 videos19 readings6 quizzes1 ungraded lab

This week is primarily focused on building supervised learning models. We will survey available methods in two popular and effective areas of machine learning: Tree based algorithms and deep learning algorithms. We will cover the use of tree based methods like random forests and boosting along with other ensemble approaches. Many of these approaches serve as an important middle layer between interpretable linear models and difficult to interpret deep-learning models. For deep learning we will use a pre-built visual recognition model and use TensorFlow to demonstrate how to build, tune, and iterate on neural networks. We will also make sure that you understand popular neural network architectures. In the case study you will implement a convolutional neural network and ready it for deployment.

What's included

5 videos14 readings5 quizzes1 ungraded lab

Instructors

Instructor ratings
4.1 (12 ratings)
Mark J Grover
13 Courses83,880 learners

Offered by

IBM

Recommended if you're interested in Machine Learning

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Learner reviews

Showing 3 of 70

4.5

70 reviews

  • 5 stars

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    22.85%

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  • 1 star

    1.42%

BG
4

Reviewed on Sep 21, 2020

PL
5

Reviewed on May 2, 2020

NM
5

Reviewed on Jul 6, 2020

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