Learn about the implementation and practical aspects of Artificial Intelligence and how to write a plan for applying AI in your own organization in a step-by-step manner.

In today's data driven world, a lot of the data isn't text anymore. This course will teach you how to recognize and classify images using neural networks.

Managing the machine learning process using recommended practices is a must to enable collaboration, tracing, and real-time monitoring. This course will teach you what are the main concerns and issues you need to consider while developing a machine learning model and after deploying it.

The AWS Machine Learning Specialty Exam covers four distinct domains. This course will review the exam, the concepts from these four domains, and techniques for how you can best prepare for it.

Learn how to deploy neural network regularizers like dropout and L2 regularization to prevent overfitting in your models. Understand how regularization techniques improve model generalization, ensuring better performance on unseen data.

Technology provides a competitive edge to organizations which makes the need to understand machine learning even more important. This course will help you better understand how to deploy trained machine learning models to a production environment.

In this course, you'll learn about how data science practitioners can utilize tools for managing the models they create. You'll also see those tools showcased in Microsoft Azure.

In this presentation, you will look at the top challenges you face deploying machine learning models to production and how to tackle those challenges using MLOps.

In this session, we will talk about the data science project cycle which holds five main stages - defining your project objectives, collecting and cleaning your data, training and testing a predictive model, deploying it and monitoring.

This course covers the important differences between various canonical problems in machine learning, as well as the considerations in choosing the right solution techniques, based on the specifics of the problem you are trying to solve and the data that you have available.

Google Cloud AI offers a wide range of machine learning services. AutoML features cutting-edge technology which uses your training data to find the best model for your use case. In this course, you'll learn to build a custom machine learning model.

This course will cover how to leverage Azure Machine Learning for a successful data science initiative across the key components of workflow, data pipeline, and infrastructure.