High-Level Machine Learning Version 12 pushes further in the direction of having fully automated machine learning capabilities for every task and data type. Most existing functions have been improved, notably by the introduction of a new automation procedure. New functions are also introduced, mostly in the unsupervised learning domain in order to learn without labeled data, but also in the active learning domain in order to learn from a "teacher". Overall, this updated high-level framework should give the possibility for novices to easily implement machine learning solutions, and for experts to reduce their development time. Learn to generate artificial data. » Fill in missing values automatically. » Identify anomalous examples. » Learn data representations. » Visualize data in feature space. » Create semantic search engines. » Learn to predict the future of sequential data. » Train a classifier automatically. » Guide the automation of high-level functions. » Analyze classifier performance in detail. » Train a classifier from a teacher. » Related Examples Train a Classifier Automatically » Analyze the Performance of an Image Classifier » Train Gradient-Boosted Trees to Predict Wind Speed » Optimize Code with Active Classification » Visualize Paintings in Feature Space » Learn a Nonlinear Manifold on Numeric Data » Recover Head Poses by Reducing the Dimension » Create a Fashion Image Search Engine » Fill in Missing Values in an Astronomy Dataset » Synthesize Missing Values in Numeric Data » Create a Custom Image Inpainter » Generate Art with Learn Distribution »
