AI Workflow: Machine Learning, Visual Recognition and NLP

Go to Course: https://www.coursera.org/learn/ibm-ai-workflow-machine-learning-vr-nlp

Introduction

### Course Review: AI Workflow: Machine Learning, Visual Recognition, and NLP #### Overview The course "AI Workflow: Machine Learning, Visual Recognition and NLP" is a well-structured part of the IBM AI Enterprise Workflow Certification specialization offered on Coursera. As the fourth installment in the series, it is designed to guide learners through the intricacies of machine learning models and their evaluation metrics, particularly within the context of a hypothetical streaming media company. Given its continuity with previous courses, this course is not only informative but also essential if you aim to master the complete workflow in AI enterprise applications. #### Syllabus Breakdown The course is divided into two primary segments, each focusing on critical aspects of AI workflows: 1. **Model Evaluation and Performance Metrics**: - This week delves into the complex world of model selection and evaluation metrics. A solid understanding of these concepts is crucial, as they form the backbone of effective machine learning. You will learn about various evaluation methods that help you iteratively improve your models. The emphasis on natural language processing within a classification context is a standout feature, connecting theory with practical applications. The integration of model performance with business metrics offers a unique perspective, allowing scholars to appreciate the practical utility of the models they create. 2. **Building Machine Learning and Deep Learning Models**: - The second week focuses on building supervised learning models, making it highly relevant for practitioners in the field. The course provides an excellent overview of tree-based algorithms (like random forests and boosting) and deep learning methodologies. The hands-on approach of using TensorFlow to build, tune, and iterate on neural networks simplifies complex concepts, making them accessible even to those newer to machine learning. The inclusion of a case study where you implement a convolutional neural network reinforces the practical aspect of the course, ensuring that you can apply your learning to real-world scenarios. #### Recommendations - **Prerequisite Knowledge**: It's essential that participants have a foundational understanding of machine learning concepts and algorithms, as the course builds significantly on previous content. If you haven't already completed the earlier courses in the specialization, I strongly recommend doing so to ensure you fully grasp the material presented here. - **Hands-On Practice**: The course encourages active participation and engagement, so be sure to invest time in hands-on coding and model building. Additionally, leveraging online resources related to TensorFlow and NLP can further enhance your learning experience. - **Networking Opportunities**: Consider joining the discussion forums and connecting with peers and instructors. These interactions can amplify your understanding and provide you with various insights and different perspectives. - **Real-World Application**: After finishing this course, think about how you can apply the skills learned to your professional context. Whether it's improving existing models at work or embarking on your own projects, applying knowledge in practical scenarios is where true learning happens. ### Conclusion "AI Workflow: Machine Learning, Visual Recognition, and NLP" is an exceptional course that provides robust theoretical foundations and practical applications in the field of AI. It stands out for its focus on linking model performance with business implications, thereby serving as a bridge between technical proficiency and real-world applicability. I highly recommend this course to individuals looking to deepen their understanding of AI workflows and elevate their skills to meet the demands of modern enterprises.

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.

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.