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

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AI Workflow: Machine Learning, Visual Recognition and NLP
 at 
Coursera 
Overview

Duration

14 hours

Start from

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Total fee

Free

Mode of learning

Online

Difficulty level

Advanced

Official Website

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Credential

Certificate

AI Workflow: Machine Learning, Visual Recognition and NLP
 at 
Coursera 
Highlights

  • Shareable Certificate Earn a Certificate upon completion
  • 100% online Start instantly and learn at your own schedule.
  • Course 4 of 6 in the IBM AI Enterprise Workflow Specialization
  • Flexible deadlines Reset deadlines in accordance to your schedule.
  • Advanced Level
  • Approx. 14 hours to complete
  • English Subtitles: English
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Details Icon

AI Workflow: Machine Learning, Visual Recognition and NLP
 at 
Coursera 
Course details

More about this course
  • This is the fourth 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 4 covers the next stage of the workflow, setting up models and their associated data pipelines for a hypothetical streaming media company. The first topic covers the complex topic of evaluation metrics, where you will learn best practices for a number of different metrics including regression metrics, classification metrics, and multi-class metrics, which you will use to select the best model for your business challenge. The next topics cover best practices for different types of models including linear models, tree-based models, and neural networks. Out-of-the-box Watson models for natural language understanding and visual recognition will be used. There will be case studies focusing on natural language processing and on image analysis to provide realistic context for the model pipelines.
  • By the end of this course you will be able to:
  • Discuss common regression, classification, and multilabel classification metrics
  • Explain the use of linear and logistic regression in supervised learning applications
  • Describe common strategies for grid searching and cross-validation
  • Employ evaluation metrics to select models for production use
  • Explain the use of tree-based algorithms in supervised learning applications
  • Explain the use of Neural Networks in supervised learning applications
  • Discuss the major variants of neural networks and recent advances
  • Create a neural net model in Tensorflow
  • Create and test an instance of Watson Visual Recognition
  • Create and test an instance of Watson NLU
  • 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 through 3 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.
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AI Workflow: Machine Learning, Visual Recognition and NLP
 at 
Coursera 
Curriculum

Model Evaluation and Performance Metrics

Course Objectives

Evaluation Metrics

Introduction to Predictive Linear and Logistic Regression

Linear Models

Watson Natural Language Understanding Service Overview

Case Study Introduction

Evaluation Metrics: Through the Eyes of our Working Example

Evaluation Metrics

Regression Metrics

Classification Metrics

Multi-class and Multi-label Metrics

Model Performance: Through the Eyes of our Working Example

Generalizing Well to Unseen Data

Model Plots, Bias, Variance

Relating the Evaluation Metric to a Business Metric

Linear Models: Through the Eyes of our Working Example

Generalized Linear Models

Linear and Logistic Regression

Regularized Regression

Stochastic Gradient Descent Classifier

Watson Natural Language Understanding: Through the eyes of our Working Example

Watson Developer Cloud Python SDK

Performance and Business Metrics: Through the Eyes of our Working Example

Getting Started with Performance and Business Metrics Case Study (Hands-on)

Summary/Review

Check for Understanding

Check for Understanding

Check for Understanding

Check for Understanding

Check for Understanding

End of Module Quiz

Building Machine Learning and Deep Learning Models

Tree Based Methods

Introduction to Tree Based Methods

Neural Networks

Introduction to neural networks

IBM Watson Visual Recognition Overview

Tree-based Methods: Through the Eyes of our Working Example

Decision Trees

Bagging and Random Forests

Boosting

Ensemble Learning

Neural networks: Through the eyes of our Working Example

Multilayer perceptron (MLP)

Neural network architectures

On interpretability

Watson Visual Recognition: Through the Eyes of our Working Example

Watson Developer Cloud Python SDK

TensorFlow: Through the Eyes of our Working Example

Getting Started with Convolutional Neural Networks and TensorFlow (Hands-on)

Summary/Review

Check for Understanding

Check for Understanding

Check for Understanding

Check for Understanding

End of Module Quiz

AI Workflow: Machine Learning, Visual Recognition and NLP
 at 
Coursera 
Admission Process

    Important Dates

    May 25, 2024
    Course Commencement Date

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