Machine Learning in simple words: Azure Machine Learning part I

Nowadays Machine learning is a very hot topic, everyone is talking about Machine learning and discussing how it can be useful in their business or in his or her career.

Machine Learning in simple words

Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that machines should be able to learn and adapt through experience.

Types of Machine Learning

Supervised Learning

• When there is pre-defined dataset to train your program
• Based on  its training data the program can make accurate decisions when given new data
• So it is like learning with the teacher
• It is like Classification and regression
• For example you receive bunch of flowers with labels and your program can indention the flowers on basis of the labeling

Unsupervised Learning

• When there is no teacher to train
• When your program is smart enough to automatically find patterns and relationships in the database which is without labeling.
• In this learning, you didn’t use any past/prior knowledge about people and classified them “on-the-go”
• It is like clustering and association
• For example, you receive flowers without labeling so the program needs the algorithm to identify the flowers

Reinforcement learning

• It is just like hit and trial kind of learning
• The program learns from their own experience.
• A software program that performs a defined task optimally and learns by trial and error through the experience.

Where it is being used

Many big industries have already started implementing Machine learning for their business.

For example, I recently participated in a well-known bank hackathon where the themes of the hackathon were mainly on Machine learning and AI.

One of the examples is, Mobile Check Deposits –  Take a picture of your filled cheque and upload it to your account. No need to physically visit the bank and wait for the cheque to be deposited in your account. It saves time and easier to use. Also can be used for fraud detection.

This is just one example, but there are many other examples:

• Self-driving cars
• Fraud preventions techniques
• Air traffic controls
• Uber uses Machine learning to make Uber more powerful
• Social networks like Facebook uses machine learning, for example when you upload an image it automatically suggests whom you should tag in the picture
• Pinterest can recommend similar pins from the image you uploaded
• Snapchat introduced facial filters, called Lenses. These filters track facial movements, allowing users to add animated effects or digital masks that adjust when their faces moved
• Online shopping, the suggestion comes from the user’s previous interest
• Smart personal assistance like Alexa, Cortana, Siri and lot more

At this moment, there are many sensors and other things which are collecting the data which they will use for their Machine Learning projects.

What’s required to create good machine learning systems?

• Data preparation capabilities
• Algorithms – basic and advanced
• Automation and iterative processes
• Scalability
• Ensemble modeling
• Easy and frequent deployments

Machine learning project Lifecycle

It basically contains 3 teams working together:

First Data scientist acquires and transforms the data building a deep understanding which allows them to build a model:

Once the model is chosen, Operational Engineer deploys it and setups monitoring and management in the production environment:

And programmatic access to this deployed model are embedded in code by the Developers converting them into the API which can be accessed from outer world:

These APIs can be accessed from the outer world.

For example, Microsoft Cognitive services have an open Vision API. Have a look hereif you require more information on this.

In my next post, I will explain some frequent issues during the Machine Learning development and how you can overcome using Azure Machine Learning. (* Update – The post is here)

Hope it helps.