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

Offered byCoursera

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

Upskilling is a better roadmap to success. Enroll in this course to learn critical principles of Machine Learning through real-life case studies & examples

Duration	14 months
Start from	Start Now
Total fee	Free
Mode of learning	Online
Difficulty level	Intermediate
Official Website	Explore Free Course
Credential	Certificate

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

Enroll for free
Pay only for getting a verified certificate
Earn a certificate of learning on course completion
This course is offered by IBM
SKILLS YOU WILL GAIN - Data Science,Information Engineering, Artificial Intelligence (AI), Machine Learning, Python Programming

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

Skills you will learn

Regression

Who should do 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 are the course deliverables?

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

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.

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

Model Evaluation and Performance Metrics

Building Machine Learning and Deep Learning Models

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

Eligibility criteria Up Arrow Icon

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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