Unsupervised Learning for Machine Learning

Unsupervised Learning for Machine Learning

What is Unsupervised Learning? Unsupervised learning is the machine learning task of inferring a function to describe hidden structure from unlabeled data. Unsupervised Learning is used to infer patterns in unlabeled datasets. The algorithms can detect hidden patterns and data groupings in data without help from humans through labeling. Unsupervised learning is ideal for exploring…

MLS-C01 AWS Machine Learning practice exam

MLS-C01 AWS Machine Learning practice exam

This page has an AWS Machine Learning practice exam. The MLS-C01 exam contains 65 multiple choice questions. The exam must be completed in 180 minutes. The questions are multiple choice so you should pick the best answer. A few questions allow more than one answer to be chosen. The answers to the questions are displayed…

35 Q & A for SageMaker built-in algorithms

35 Q & A for SageMaker built-in algorithms

The AWS Machine Learning – Speciality certification exam (MLS-C01) tests your abilities to select the correct answer to real life scenarios. 36% of the questions in the MLS-C01 exam will be from Domain 3. These SageMaker built-in algorithms are part of Sub-domain 3.2, Select the appropriate models for a given Machine Learning problem. Sub-domain 3.2…

Supervised Learning for Machine Learning

Supervised Learning for Machine Learning

What is Supervised Learning? For Supervised Learning you need labeled training data. In Supervised Learning we provide data that has already been identified and therefore labeled, as being what we are looking for. Once the Machine Learning model has been trained it can then be presented with real unknown data to which the Machine Learning…

How to evaluate Machine Learning models

How to evaluate Machine Learning models

Evaluating Machine Learning models is the last stage before deploying a model to production. We evaluate Machine Learning models to confirm that they are performing as expected and that they are good enough for the task they were created for. The evaluation stage is performed after model training is finished. Different techniques are used depending…

Streaming data for Machine Learning

Streaming data for Machine Learning

Streaming data processing is used when data is continuously being generated and needs to be processed as it arrives. The AWS service for data streaming processing is Kinesis. Kineses comprises of four services each with different capabilities and some that can be used together. As well as Kinesis there is another AWS service that can…