Machine Learning
Showing 49–60 of 233 results
Deep Reinforcement Learning in Python
Learn and use powerful Deep Reinforcement Learning algorithms, including refinement and optimization techniques.
DelftX: AI in Practice: Applying AI
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.
Demystifying Image Recognition: Dive into Deep Learning
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.
Demystifying Machine Learning Operations (MLOps)
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.
Demystifying the AWS Certified Machine Learning Specialty Exam
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.
Deploy Trained Models
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.
Deploying and Managing Models in Microsoft Azure
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.
Deploying Machine Learning Models to Production: Challenges & Solutions
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.
Deployment Isn’t the Final Step: Monitoring Machine Learning Models in Production Environments
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.
Designing a Machine Learning Model
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.
Designing and Implementing Solutions Using Google Cloud AutoML
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.
Designing Machine Learning Solutions on Microsoft Azure
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.