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…

SageMaker unsupervised algorithms

SageMaker unsupervised algorithms

There are five SageMaker unsupervised algorithms that process tabular data. Unsupervised Learning algorithms process data that has not been labeled. IP Insights is an anomaly detection algorithm to detect problems and threats in an IR network. K-Means is a clustering algorithm. Object2Vec translates input data to vectors. Principal Component Analysis (PCA) algorithm is used in…

SageMaker supervised algorithms

SageMaker supervised algorithms

There are five SageMaker supervised algorithms for tabular data. DeepAR Forecasting uses Deep Learning for financial forecasting. Linear Learner is good for regression problems. Factorization Machines can be used for the same purpose, but can handle data with gaps and holes better. K-Nearest Neighbor is good at categorising data. XGBoost can predict if an item…

SageMaker text processing algorithms

SageMaker text processing algorithms

There are four SageMaker text processing algorithms: BlazingText, LDA, NTM and Sequence-to-sequence. BlazingText converts text to numeric vectors. LDA and NTM identify topics in text documents and Sequence-to-sequence provides machine translation of languages. Each algorithm has it’s own section and embedded video. These revision notes are part of subdomain 3.2 Select the appropriate model(s) for…

SageMaker image processing algorithms

SageMaker image processing algorithms

There are three built-in SageMaker image processing algorithms. They are all Supervised Learning algorithms and so have to be trained using labelled data. Each one analyzes images in a different way and returns different inference data for downstream processing. SageMaker’s three built-in image processing algorithms each have their own way of visualizing real word objects….

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…

Training Machine Learning models

Training Machine Learning models

Before a Machine Learning Model can be deployed to the production environment it has to be trained. Training Machine Learning Models allows the algorithm to learn from the training data how to make a generalized prediction. This is an iterative process where the training data is processed multiple times as the algorithm learns from previous…