“In the world of intelligent applications, data will be the king!”. Despite of way they making the revenue, data has become the main asset of each company. Sales and distribution data, customer data repos, employee records, all sort of structured and unstructured data have become the life blood of the company’s business process because it is vital to get the accurate and relevant data to get the correct business decisions and do relevant business related predations.

Digital data and cloud storage follow Moore’s law: the world’s data doubles every two years, while the cost of storing that data declines at roughly the same rate.

This abundance of large amounts of data enables more features and tasks, and better machine learning models and methodologies should to be created for predictive analytics.

When the data is widely available in the cloud, and when it needs large computation power and infrastructure to process and analyze data repositories, the best move is the cloud!

Machine learning (ML) is starting to move to the cloud, where a scalable web service is an API call away. Data scientists will no longer need to manage infrastructure or implement custom code. The systems will scale for them, generating new models on the fly, and delivering faster, more accurate results.

What is Machine Learning?

Simply, machine learning is teaching the silicon chips to think! 😀 If we use the general definition: “Machine learning is the systematic study of algorithms and systems that improve their knowledge or performance with experience”

When you going through the theories behind machine learning you may find it is closely related to computational statistics, where you use computers in prediction making.  Machine learning comes out with range of computing tasks to solve problems where designing and programming explicit algorithms is unfeasible.

All of these things mean it’s possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale. The result? High-value predictions that can guide better decisions and smart actions in real time without human intervention.

Where the hell ML is used?

Did you notice that eBay is pushing you to buy a protective glass after you buying a fancy phone case for your iPhone? Netflix is suggesting movies for you? Siri or Cortana speech recognition? All these tiny miracles have been possible with the power of machine learning. Spam filtering you emails, speech recognition, recommender systems in electronic commerce are some famous applications of machine learning.

So… How we going to do?

If you google or do a Bing search on machine learning, you’ll find out hundreds of ways of applying machine learning techniques in practical applications and tools that we can use to create machine learning models.

With my post series, mainly am going to take you a journey with Azure Machine Learning Studio, which comes under the Cortana Intelligence Suite.

Why AzureML?

With advanced capabilities, free access, strong support for R, cloud hosting benefits, drag-and-drop development and many more features, Azure ML is ready to take the consumerization of ML to the next level.

It’s easy as ABC and powerful enough to handle petabytes of data with the power of Azure.

Theories??

Basics on computing and statistics will be useful to go forward. It’s fantastic if you have a rough idea about the machine learning algorithms, data pre preparation methods kind of stuff. Don’t worry. Here’s a book to read!  🙂

So will take the first step to Azure ML in the coming post.

Part 02