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AI Workflow: Machine Learning, Visual Recognition and NLP

Approx. 14 hours to complete

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Course Summary

Learn how to create an end-to-end AI workflow using machine learning, VR, and NLP technologies with this comprehensive course from IBM.

Key Learning Points

Understand the AI workflow and its applications in various industries
Learn how to use machine learning algorithms for data analysis and decision making
Explore VR and NLP technologies and their potential in creating immersive experiences and enhancing communication

Learning Outcomes

Create an end-to-end AI workflow using machine learning, VR, and NLP technologies
Understand the applications of AI in various industries
Develop skills in data analysis and decision making using machine learning algorithms

Prerequisites or good to have knowledge before taking this course

Basic knowledge of programming
Familiarity with statistics and linear algebra

Course Difficulty Level

Intermediate

Course Format

Online self-paced course
Video lectures
Assignments and quizzes

Similar Courses

Applied Data Science with Python Specialization
Deep Learning Specialization

Related Education Paths

Notable People in This Field

Andrew Ng
Fei-Fei Li

Related Books

Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition

Description

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.

Outline

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

Summary of User Reviews

This course on AI workflow, machine learning, VR, and NLP by IBM has received positive reviews from users. Many users have praised the course for its comprehensive coverage on AI workflow and machine learning.

Key Aspect Users Liked About This Course

Comprehensive coverage on AI workflow and machine learning

Pros from User Reviews

Excellent course material and videos
Great instructors with industry experience
Real-world examples and hands-on projects
Good introduction to natural language processing
Useful resources and references provided

Cons from User Reviews

Some sections may be too technical for beginners
Not enough focus on virtual reality (VR)
Lack of interaction and engagement with instructors
Some exercises and quizzes are confusing
Course may be too long for some learners

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