Machine Learning with Azure - Part 2

As in Part 1, we came to know about basics of Machine Learning – what, why and ML algorithms? Now, we will study about Machine Learning in Microsoft Azure.

As we know machine learning is effectively used for real-time examples whether it is in weather predictions or Robotics or speech recognition and many more. Machine learning is a data science technique that allows computers to use existing data to forecast future behaviors, outcomes, and trends.

What is Azure?

Azure is a complete cloud platform that can host our existing applications, streamline the development of new applications, and even enhance on-premises applications. Azure integrates the cloud services that we need to develop, test, deploy, and manage our applications—while taking advantage of the efficiencies of cloud computing.

By hosting our applications in Azure, we can start small and easily scale our application as our customer demand grows. Azure also offers the reliability that’s needed for high-availability applications, even including failover between different regions. We can also manage our services programmatically by using service-specific APIs and templates.

Azure Machine Learning: a cloud-based predictive analytics service

Azure Machine Learning is a cloud predictive analytics service that makes it possible to quickly create and deploy predictive models as analytics solutions.

We can work from a ready-to-use library of algorithms, use them to create models on an internet-connected PC, and deploy our predictive solution quickly.

Here, in our mind, it comes Why Azure ML? So, the answer is that we can understand by the below diagram which states Azure ML is fully managed, Integrated, uses various algorithms in different languages like .net, Python, C++ .. etc and deploy in minutes.

Now, we are using a terminology Predictive analytics maximum time. Now, let’s understand what is it means?

Predictive analytics uses math formulas called algorithms that analyze historical or current data to identify patterns or trends in order to forecast future events. It is the process of building models from historical or current data in order to forecast future outcomes.

Similarly, like predictive analytics, there is some more terminology that we should know if we are working on Machine Learning in Azure:

Data exploration: It is the process of gathering information about a large and often unstructured data set in order to find characteristics for focused analysis.

Data mining: It refers to automated data exploration.

Descriptive analytics: It is the process of analyzing a data set in order to summarize what happened. The vast majority of business analytics - such as sales reports, web metrics, and social networks analysis - are descriptive.

Training data: When we train a model from data, we use a known data set and make adjustments to the model based on the data characteristics to get the most accurate answer. In Azure Machine Learning, a model is built from an algorithm module that processes training data and functional modules, such as a scoring module.

Evaluation data: Once we have a trained model, evaluate the model using the remaining test data. We use data we already know the outcomes for, so that we can tell whether our model predicts accurately.

Algorithm: A self-contained set of rules used to solve problems through data processing, math, or automated reasoning.

Anomaly Detection: A model that flags unusual events or values and helps us discover problems. For example, credit card fraud detection looks for unusual purchases.

Categorical Data: Data that is organized by categories and that can be divided into groups. For example, a categorical data set for autos could specify year, make, model, and price.

Classification: A model for organizing data points into categories based on a data set for which category groupings are already known.

Feature Engineering: The process of extracting or selecting features related to a data set in order to enhance the data set and improve outcomes. For instance, airfare data could be enhanced by days of the week and holidays.

Module: A functional part of a Machine Learning Studio model, such as the Enter Data module that enables entering and editing small data sets. An algorithm is also a type of module in Machine Learning Studio.

Model: A supervised learning model is the product of a machine learning experiment comprised of training data, an algorithm module, and functional modules, such as a Score Model module.

Numerical Data: Data that has to mean as measurements (continuous data) or counts (discrete data). Also referred to as quantitative data.

Partition: The method by which we divide data into samples.

Prediction: A prediction is a forecast of a value or values from a machine learning model. We might also see the term "predicted score." However, predicted scores are not the final output of a model. An evaluation of the model follows the score.

Regression: A model for predicting a value based on independent variables, such as predicting the price of a car based on its year and make.

Score: A predicted value generated from a trained classification or regression model, using the Score Model module in Machine Learning Studio. Classification models also return a score for the probability of the predicted value. Once we have generated scores from a model, we can evaluate the model's accuracy using the Evaluate Model module.

Sample: A part of a data set intended to be representative of the whole. Samples can be selected randomly or based on specific features of the data set.

Part 3 will soon arrive...