All Classes and Courses

An Overview of Machine Learning in Wolfram Language

Video Lesson | FREE

This video gives an overview of the highly automated machine learning framework in Wolfram Language, which allows you to do so much with just a few lines of code. You will learn about high-level functions that are task oriented and can be applied to a variety of input such as text, images and numeric data. Examples include building a simple image search and classification system, topic classification of text and prediction of sale prices of homes.

Building Blocks for Deep Learning

Video Course | FREE

This video course explores how to construct neural networks in the Wolfram Language. The Wolfram Language neural network framework provides symbolic building blocks to build, train and tune a network, as well as automatically process input and output using encoders and decoders. You'll learn how to build feed-forward networks and about recurrent neural nets and why they are interesting.

Introduction to Image Processing

Interactive Course | FREE

Requirements: This course requires basic working knowledge of the Wolfram Language.

Certification Levels: CompletionLevel 1

Make cutting-edge image processing simple with the Wolfram Language. Learn the fundamentals of digital image processing, including image representation and classical operations on images. This course emphasizes practical applications and understandable explanations of how image operations work. Numerous examples are included to illustrate standard applications.

Introduction to Machine Learning in Wolfram Language

Instructor Led | SEE COURSE SCHEDULE

Requirements: This course requires basic working knowledge of Wolfram Language.

Certification Levels: Completion

This course introduces the easy-to-use machine learning superfunctions available in Wolfram Language. You will learn how to perform supervised and unsupervised learning tasks with just a few lines of code. We will start with regression, classification, clustering and anomaly detection, and from there, we'll move on to the state-of-the-art Neural Net Framework. Examples using the Wolfram Neural Net Repository are shown with instructions for building your own neural networks from scratch. Basic familiarity with Wolfram Language or introductory-level skill in any programming language is recommended.

Learning to Tackle Real-World Computer Vision Applications

Video Lesson | FREE

How can you apply the deep learning framework integrated in the Wolfram Language for solving real-world image processing applications? This class explores some of the depth of the Wolfram Language's neural net framework capabilities and shows how the trained networks can be tweaked to suit a wide range of complex image analysis tasks. With the help of examples, you will gain practical insights into effectively leveraging neural nets for your own applications.

Multiparadigm Data Science

Interactive Course | FREE

Requirements: This course requires basic working knowledge of the Wolfram Language

Certification Levels: CompletionLevel 1Level 2

Multiparadigm Data Science is a rapidly advancing new approach of using modern analytical techniques, automated machine learning and human-data interfaces to arrive at better answers. This course introduces the basic concepts of the multiparadigm approach, demonstrating both the flexible, integrated project workflow and the broad computational toolkit that supports it from start to finish. Discover best practices, exploration techniques and ways to leverage the high-level Wolfram Language to get real, quantifiable answers to the full range of data science problems.

Video Creation, Editing and Analysis Using Wolfram Language

Video Course | FREE

Learn how to create, edit, process and analyze videos with Wolfram Language. You can capture videos using webcams, or create them using files, images, existing video clips or built-in functions that create individual frames. Then learn how to cut, alter and compose video footage with dedicated functions for video editing and processing. You can also learn how to enhance video quality, perform color correction and combine videos and images using overlays and grids. This course includes examples of applying machine learning and neural networks to process videos and recognize objects, faces, speech or actions in the videos.

Wolfram Technology in Action: Data Science Webinar Series

Archived Special Event | FREE

This three-part webinar series showcases a range of data science applications in the Wolfram Language, featuring talks from the 2019 Wolfram Technology Conference. Presentations highlight built-in Wolfram Language functionality for data analysis, modeling, visualization, automated reporting and machine learning. Topic areas include Twitter analytics, tidal flooding, computational taxonomy, video game AI planning and more.

Zero to AI in 60 Minutes

Video Course | FREE

Follow this video series to get started with machine learning in the Wolfram Language. Automated machine learning capabilities in the Wolfram Language and machine learning concepts are demonstrated with the use of examples. Start with the concept of supervised learning and three key techniques: classification, prediction and sequence prediction. Learn how to work with various types of data, select from predefined methods and interpret results. Common issues related to datasets and ways to address them are discussed. Move on to the concept of unsupervised machine learning tasks such as feature extraction, encoding and dimension reduction. Text and image classification examples are used to demonstrate these methods. Next, get an overview of the neural network framework integrated in the Wolfram Language. Finally, learn how to deploy developed models through APIs and web forms for use by external programs and individuals.

An Overview of Deep Neural Networks Applications

Video Lesson | FREE

A high-level overview of deep neural networks applications. This class shows many examples of problems that can be solved with deep neural nets, including image classification, sequence prediction, speech recognition and question answering. The class concludes with a case study of how the ImageIdentify function was built on the Wolfram neural network framework. This class assumes some familiarity with the Wolfram Language and neural network concepts.

Analyzing Text to Answer Fact-Based Questions

Video Lesson | FREE

This video class introduces FindTextualAnswer, the built-in Wolfram Language function that combines well-established techniques for information retrieval with state-of-the-art deep learning techniques to find answers in text. FindTextualAnswer analyzes text and can yield several possible answers, the probabilities of those answers being correct and other properties that can help you understand the context of each answer in response to your specific, fact-based questions. In this class, you will learn the scope of this function and some practical applications, as well as gain insights into how it is implemented and the deep learning approach available in the Wolfram Language.

Applying Neural Networks Webinar Series

Archived Special Event | FREE

This webinar series takes you on a deep dive into the latest workflows for building, training and evaluating neural networks. See how neural net models are used to solve complex processing tasks involving image, audio and natural language data. Sessions include hands-on demonstrations, showing real-world applications using the latest built-in Wolfram Language functionality and neural net models. Pretrained models from the Neural Net Repository and customized models are shown.

Automated Data Science

Video Course | FREE

Learn about machine learning functions that have been tuned to automate the data science process. This video course shows examples of using computation with data that go beyond traditional statistical methods and highlight the role of automated modeling in the modern data science process. Automated classification and regression functionalities are demonstrated using the built-in Wolfram Language symbols Classify and Predict.

Automated Structure Discovery: Unsupervised Learning

Video Lesson | FREE

The Wolfram Language has functions that work directly on many types of data and automatically extract some sort of structure from it. FindClusters, ClusteringTree and ClusteringComponents are examples of functions that perform the unsupervised learning task of clustering. ClusterClassify classifies new samples based on information gathered from unlabeled input data via clustering. Other functions like FeatureExtract, FeatureNearest, FeatureSpacePlot and DimensionReduce provide tools for automatic exploration of the data in the feature space. This video introduces these functions to get you started on unsupervised machine learning tasks. It is suitable for beginners without previous knowledge of machine learning.

Biodiversity Explorations with Machine Learning
(Study Group Sessions)

Archived Special Event | FREE

Learn to apply machine learning techniques and Wolfram Language functions to biodiversity data. These Study Group sessions begin by introducing you to biodiversity data access functions available with the entity framework built into the Wolfram Language and in the Wolfram Function Repository. Later sessions cover examples of classification, social media text analysis, audio processing of bird sounds and deploying a trained neural network image classifier to your mobile phone. Each session includes lessons, polls to review key concepts and practice problems.

Building and Training Basic Neural Networks

Video Lesson | FREE

The Wolfram Language neural network framework provides symbolic building blocks to build, train and tune a network as well as automatically process input and output using encoders and decoders. Learn how to do this in steps, along with examples of logistic regression and basic image recognition.

Building Applications with the Wolfram Neural Net Repository

Video Lesson | FREE

Learn to build applications using the neural network models available in the Wolfram Neural Net Repository. This class showcases existing models in the repository and the different tasks for which they are intended, such as classification, feature extraction, image processing, regression, language modeling and more, with new models and new application areas being added all the time. Use cases showing applications built by students from Wolfram summer programs are shared. Learn to leverage available models to create your own applications.

Built-in Machine Learning in Wolfram Language

Video Lesson | FREE

You can apply machine learning to diverse subject areas without expert-level knowledge with the help of Wolfram Language. Even though you can build complicated models from scratch, you can also use any of the existing, pre-trained models on different inputs like text, numbers and images. This video introduces the many machine learning functions available for such wide-ranging tasks as image identification, text recognition, sentiment classification and others.

Computational Audio

Video Course | FREE

Investigate and analyze audio data and learn to easily add fun effects. These videos introduce the new integrated support for audio processing in the Wolfram Language and cover audio effects, audio synthesis and audio analysis. The processing and analysis techniques you learn here can be combined with comprehensive support for signal processing, statistical analysis and machine learning to enable easy prototyping of applications in various fields, including music, speech, communication and broadcasting.

Deep Neural Networks for Computer Vision

Video Course | FREE

Get started with deep learning for your computer vision and image processing applications! This video course introduces the deep learning framework integrated in Wolfram Language and shows how to use it in practice. You'll receive a quick introduction to the components of a neural network and their potential use in computer vision tasks. You will then learn to build, modify, train and deploy neural networks. Finally, with the help of real-world examples for processing and analyzing images, you will gain practical insights into effectively leveraging neural nets for your own applications.

Deploying and Running a Custom Neural Net on Your Phone

Video Lesson | FREE

Learn about creating and training a neural net image classifier application for the iPhone to identify different types of mushrooms. Follow along step by step with this tutorial using built-in Wolfram Language functions as well as external code and applications to create a fully functional app that can be immediately deployed to a phone and used right away.

Exploring the Neural Network Framework from Building to Training

Video Lesson | FREE

This video showcases the easy-to-use framework available in the Wolfram Language to build, modify, train and deploy neural networks. Learn how the Wolfram Language simplifies the process of encoding input and decoding output for neural nets. Also introduced are layers (almost 30 different types)—the basic building blocks for constructing neural networks—and the process of connecting them in chains and graphs to build complicated networks according to your requirements. This class is intended for users who have a basic familiarity with neural nets and who would like to explore the Wolfram Language features that make the process of building and training networks more intuitive.

Introduction to Neural Networks

Video Lesson | FREE

Learn about the properties of neural networks, their component layers, how to combine operations in a chain or graph container and how to train a network using the built-in functions of the Wolfram Language. See the use of encoders and decoders for automatically processing input and output to a network. Follow along step by step as we build a digit classifier from scratch, train a neural network and evaluate its performance.

Learning from Input and Output: Supervised Learning

Video Lesson | FREE

Predict and Classify are highly automated functions in the Wolfram Language for supervised learning tasks. This video shows how these functions can be used to quickly create a simple interpretative model from the input data or customized for a specific task by setting options like Method and PerformanceGoal. It discusses how trained classifier models can be evaluated using metrics like Accuracy and ConfusionMatrix. This class is suitable for beginners. No previous knowledge of machine learning is required.