What is Machine Learning?
Machine Learning is an emerging trend on Data Science Platforms. It helps computers to predict future events and outcomes, and helps applications to generate output according to their programming.
Advantages of Machine Learning
- Machine Learning helps devices learn with the help of pre-defined data sets and algorithims.
Implementation
Machine Learning has been implemented in all areas, from speaking about predicting a cricket score to other real-time applications such as Google Maps , which the time for reaching a destinationand routes the vehicle; or helping banks determine whether or not to provide a loan to a client.
Machine Learning with Microsoft Azure
Azure has started extending its support on Data Science Platforms with Machine Learning which helps us to build predictive analytics services.. Azure also supports us with pre-made algorithms which are already available with Microsoft Azure on Machine Learning Studio. Machine Learning Studio also helps us to drag and drop the components on the Machine Learning Studio Module. Once we build the model we can upload it to the Cortana Intelligence Gallery and share tit with other developers. The sample experiments that Microsoft Azure Machine Learning supports are of R and Python scripts.
Stick on towards the basic terms of Azure ML
| Key Terms | Explanation |
| Predictive Analytics | This predictive analytics is the first key-term which a developer who wants to work with Machine Learning should understand. Predictive Analytics uses mathematical algorithms to analyze the data sets that have been uploaded. Using predictive analytics you can deploy your web services, manage your web service endpoints, scale the web service and consume the web service. |
| Descriptive Analytics | Makes a log by analyzing the data set which will help us to understand what has happened. |
| Data Exploration | Wrapping up of all unstructured data to find the characteristics for focused analysis. |
| Data Mining | Examining your already available data sets to generate new data information. |
| Supervised Learning | Labels the trained data sets. |
| Unsupervised Learning | Unlabeled data sets |
| Training Data | Training a model from the data and making some modifications on the data set |
| Evaluation Data | The data which has been left out of the trained data will be used as Evaluation Data. |
| Algorithm | A set of rules to achieve our goal using mathematical formulas, data processing, data mining, automated reasoning, etc., |
| Anomaly Detection | Helps to figure out the anonymous values of the log which will help the model to discover problems. |
| Feature Engineering | Pools the data sets to enhance the outcome of the model. |
| Module | Functional model in Azure Machine Learning which enables entering and editing small data sets. |
| Model | An experiment grouped with training data |
| Prediction | Predicts the outcome of the Machine Learning model, also supports us with the Prediction score under Azure ML. |
| Regression | Predicts the outcome of a particular component with specific values such as Name, Year or any other components related to that particular data set. |