Learn how to quickly and easily manage your photo collection with Luminar 3. See how to leverage the app’s new organizational features and use adjustments to enhance your images.

Learn how to create beautiful renders, videos, and panoramas with Lumion, the powerful architectural visualization tool.

Now any architect can create beautiful renders quickly. With Lumion, you work in real time. Production-quality images can be saved to disk in seconds. Lumion can also produce videos and animated sequences from the same project file. In this course, instructor Brian Myers covers the essentials of this powerful visualization tool. Learn how to import CAD models, add cameras, and build out a beautiful environment, with different types of virtual weather and terrain. Brian shows how to add water features and foliage, as well as assign and adjust materials to help your renders look more realistic. Plus, learn how to work with the Build Mode context menu to replace, move, duplicate, and randomize positioning of items—without affecting any of the other objects or settings in your render. Brian closes the course by showing how to render video and still images with the same steps you can use in your own Lumion projects.

Get an IT administrator's guide to installing, configuring, backing up, and troubleshooting Mac OS X El Capitan.

Learn how to use cluster analysis, association rules, and anomaly detection algorithms for unsupervised learning.

Establish a strong foundation in ML by exploring the IBM SPSS Modeler and learning about CHAID and C&RT. This course is designed to help expand your data science skills.

Expand your data science skills by learning how to leverage the concepts of linear regression to solve real-world problems.

This project-based course shows programmers of all skill levels how to use machine learning to build programs that can make recommendations—like recommending new products.

Discover how to solve value estimation problems with machine learning. Learn how to build a value estimation system that can estimate the value of a home.

Work toward a mastery of machine learning by exploring advanced decision tree algorithm concepts. Learn about the QUEST and C5.0 algorithms and a few advanced topics.

Classification methods are among the most important in modern data science. Learn classification strategies and algorithms for machining learning and AI.

Scalability is one of the biggest challenges in data science. Learn how to evaluate data, choose the right algorithms, and perform predictive modeling at scale.

Get started with machine learning and the Core ML framework, and learn how to build your own custom machine learning model and integrate it into an iOS app.

Want to incorporate machine learning into your iOS app? With the Core ML SDK and the Create ML app from Apple, you can quickly start integrating machine learning features into your projects. In this course, Emmanuel Henri covers the basics of machine learning, as well as how to work with Core ML and Create ML to add machine learning models to your apps. Emmanuel covers the different parts of the Core ML SDK, how to set up a new project using Create ML, and how to get input values into and test a model. He also steps through how to integrate your machine learning model into an iOS application and convert models from non-iOS sources.

Learn how to use machine learning to automate and improve your marketing campaigns, including customer engagement and account-based marketing.

Learn how to apply the power of machine learning to mobile app development, using platforms such as IBM Watson, Microsoft Azure Cognitive Services, and Apple Core ML.

Excel, R, and Power BI are applications universally used in data science and across businesses and organizations around the world. If you’ve spent any time trying to figure out how to better model your data to get useful insights from it that you can act upon, you’ve most likely encountered these applications. In this course, Helen Wall shows how to use Excel, R, and Power BI for logistic regression in order to model data to predict the classification labels like detecting fraud or medical trial successes. Helen walks through several examples of logistic regression. She shows how to use Excel to tangibly calculate the regression model, then use R for more intensive calculations and visualizations. She then illustrates how to use Power BI to integrate the capabilities of Excel calculations and R in a scalable, sharable model.

Welcome to Machine Learning with ML.NET. In this course, instructor Pranav Rastogi guides you through the concepts of machine learning, what you can build with these concepts, and how to get started. First, Pranav explains what ML.NET is and what you can do with the framework. He covers how to build a ML model for sentiment analysis of customer reviews and explains how to classify incoming GitHub issues into one of the many tags (labels) using a multiclass classification algorithm. Pranav shows you how to recommend movies for users using collaborative filtering-based recommendation approach. He concludes by discussing how deep learning enables many more scenarios, using sound, images, text and other data types.

This course was created by Microsoft.NET. We are pleased to host this training in our library.

You’ve probably heard about machine learning before, but have you ever wondered what that term really means? How does a machine learn? Have you thought about building a machine learning model, but didn’t know where to start? In this course, Frederick Nwanganga introduces machine learning in an approachable way and provides step-by-step guidance on how to get started with machine learning via the most in-demand language in use today, Python. Frederick starts with exactly what it means for machines to learn and the different ways they learn, then gets into how to collect, understand, and prepare data for machine learning. He also provides guided examples of how to accomplish each step using Python. Finally, he brings it all together to build, evaluate, and interpret the results of a machine learning model in Python.

Learn to use scikit-learn, the popular open-source Python library, to build efficient machine learning models.