AI Workflow: Machine Learning, Visual Recognition and NLP

IBM via Coursera

Coursera based on 86 ratings

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Overview

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...

Syllabus

Model Evaluation and Performance Metrics

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.

Building Machine Learning and Deep Learning Models

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.