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AI Workflow: Machine Learning, Visual Recognition and NLP
  • Level Expert
  • Duration 14 hours
  • Course by IBM
  • Offered by

About

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.

Modules

Evaluation metrics

    • 2 Videos
    • 5 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Check for Understanding

2 Videos

  • Course Objectives
  • Evaluation Metrics

5 Readings

  • Evaluation Metrics: Through the Eyes of our Working Example
  • Evaluation Metrics
  • Regression Metrics
  • Classification Metrics
  • Multi-class and Multi-label Metrics

Model performance

    • 4 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Check for Understanding

4 Readings

  • 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

    • 2 Videos
    • 5 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Check for Understanding

2 Videos

  • Introduction to Predictive Linear and Logistic Regression
  • Linear Models

5 Readings

  • Linear Models: Through the Eyes of our Working Example
  • Generalized Linear Models
  • Linear and Logistic Regression
  • Regularized Regression
  • Stochastic Gradient Descent Classifier

TUTORIAL: Watson Natural Language Understanding

    • 1 Videos
    • 2 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Check for Understanding

1 Videos

  • Watson Natural Language Understanding Service Overview

2 Readings

  • Watson Natural Language Understanding: Through the eyes of our Working Example
  • Watson Developer Cloud Python SDK

CASE STUDY: Performance and business metrics

    • 1 Videos
    • 2 Readings
    • 1 Lab
    • 1 Assignment
Show info about module content

1 Assignment

  • Check for Understanding

1 Labs

  • Case Study Answer Key Notebook

1 Videos

  • Case Study Introduction

2 Readings

  • Performance and Business Metrics: Through the Eyes of our Working Example
  • Getting Started with Performance and Business Metrics Case Study (Hands-on)

End of module review & evaluation

    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • End of Module Quiz

1 Readings

  • Summary/Review

Tree-based methods

    • 2 Videos
    • 5 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Check for Understanding

2 Videos

  • Tree Based Methods
  • Introduction to Tree Based Methods

5 Readings

  • Tree-based Methods: Through the Eyes of our Working Example
  • Decision Trees
  • Bagging and Random Forests
  • Boosting
  • Ensemble Learning

Neural networks

    • 2 Videos
    • 4 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Check for Understanding

2 Videos

  • Neural Networks
  • Introduction to neural networks

4 Readings

  • Neural networks: Through the eyes of our Working Example
  • Multilayer perceptron (MLP)
  • Neural network architectures
  • On interpretability

TUTORIAL: Watson Visual Recognition

    • 1 Videos
    • 2 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Check for Understanding

1 Videos

  • IBM Watson Visual Recognition Overview

2 Readings

  • Watson Visual Recognition: Through the Eyes of our Working Example
  • Watson Developer Cloud Python SDK

CASE STUDY: TensorFlow

    • 2 Readings
    • 1 Lab
    • 1 Assignment
Show info about module content

1 Assignment

  • Check for Understanding

1 Labs

  • Case Study Answer Key Notebook

2 Readings

  • TensorFlow: Through the Eyes of our Working Example
  • Getting Started with Convolutional Neural Networks and TensorFlow (Hands-on)

End of module review & evaluation

    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • End of Module Quiz

1 Readings

  • Summary/Review

Auto Summary

Dive into IBM's AI Workflow course, designed for seasoned data science professionals. This advanced program, led by IBM on Coursera, delves into machine learning, visual recognition, and NLP. Over 840 minutes, you’ll master regression, classification, model evaluation, neural networks, and deploy Watson models. Ideal for experts with prior course completion and robust data science foundation, subscriptions are available in Starter and Professional tiers. Perfect for enhancing enterprise AI deployment skills.

Mark J Grover
Instructors

Mark J Grover

Ray Lopez, Ph.D.
Instructors

Ray Lopez, Ph.D.