Although the term “machine learning” is existing from 1959, when Arthur Samuel an American pioneer in field of computing games and Artificial Intelligence. There has been a renewed in interest this f field why so generation of loads of data (sensor data+ social media+ online purchase data) coupled with cheap storage cost and computational cost (cloud computing ).

Why do we want machines to learn?

Anil wants to buy a car. He is trying to calculate how much money needs to be saved . He searched on the internet and learned that new cars are around Rs40,0000, used year-old ones are Rs 200000 2-year old are Rs 1,00000 and so on. Anil is sharp in Analytics and figured out a pattern that the price of car drops by 100000 per year with age of car but will not drop beyond Rs 30000.

In machine learning terminology, Anil used regression he predicted a value (price) based on known historical data. People do it all the time, when trying to estimate a reasonable cost for a used mobile. Let’s get back to example of cars. They have different manufacturing dates, dozens of options, technical condition, seasonal demand spikes, etc and many more hidden factors. It is not easy for average human being like Anil to keep all factor in mind and so we use a machine to find all hidden pattern related price . The most exciting thing is that the machine copes with this task much better than a human being does when carefully analyzing all the dependencies in their mind.

That was the birth of machine learning.

Components of machine learning

The only goal of machine learning is to predict results based on incoming data ,the greater variety in the samples data we have, the easier it is to find relevant patterns and predict the result. Therefore, we need three components to teach the machine:

(a) Data : Technically data is facts and statistics collected together for reference or analysis.

 

Objective Sample data
Want to detect spam spam messages
Want to forecast stocks price history
Want to find out user preferences activities on Face book

The more diverse the data, the better the result. There are two main ways to get the data

(1) Manual collection – time consuming method but less error prone
(2) Automatic – cheaper method to collect data but may lack diversity in data

Google use their own customers to label data for them for free. Remember ReCaptcha which forces you to “Select all street signs”? It is method of collecting data automatically
a good collection of data is called a dataset which is used for testing various algorithm
Features: A feature is a measurable property of the object we are trying to analyze Features are the basic building blocks of datasets. The quality of the features in your dataset has a major impact on the quality of insights you will be able to get when you use that dataset for machine learning. Features also known as parameters or variables. Those could be car mileage, user’s gender, stock price, word frequency in the text. These are the factors for a machine to look Technically we can say features are the factor used by machine to find pattern .

Algorithms: Technically defined is a process or set of rules to be followed in calculations or other problem-solving operations, especially by a computer. Any problem can be solved differently that is there may exist many algorithm to solve the problem . The choice of method will have affect on precision , performance , size of final model. There is one important factor though if the data set is not good , even the best algorithm won’t help. it’s also referred as “garbage in – garbage out”. .

Classification of Machine Learning

There are mainly two types of machine learning

(a) Supervised Learning: The machine has a “supervisor” or a “teacher” who gives the machine all the answers, like whether it’s a cat in the picture or a dog. The teacher has already divided (labelled) the data into cats and dogs, and the machine is using these examples to learn. One by one. Dog by cat. The machine will learn faster with a teacher, so it’s more commonly used in real-life tasks.

(b) Unsupervised learning: Unsupervised learning means the machine is left on its own with a collection of dataset and a task to find out who’s who. Data is not labelled, there’s no teacher, the machine is trying to find any patterns on its own . Some recommendation systems that you find on the web in the form of marketing automation are based on this type of learning.

Applications of machine learning

One of the popular applications of AI is Machine Learning (ML), in which computers, software, and devices perform via cognition (very similar to human brain)

1. Virtual Personal Assistants

Siri, Alexa, Google Now are some of the popular examples of virtual personal assistants. As the name suggests, they assist in finding information, when asked over voice. All you need to do is activate them and ask “What is my schedule for today? For answering, your personal assistant looks out for the information, recalls your related queries, or send a command to other resources (like phone apps) to collect info. You can even instruct assistants for certain tasks like “Set an alarm for 6 AM next morning

Virtual Assistants are integrated to a variety of platforms. For example:

  • Smart Speakers: Amazon Echo and Google Home
  • Smartphones: Samsung Bixby on Samsung S8
  • Mobile Apps: Google Allo

2. Predictions while travelling

Traffic Predictions: We all have been using GPS navigation services. we do that, our current locations and velocities are being saved at a central server for managing traffic. This data is then used to build a map of current traffic. While this helps in preventing the traffic and does congestion analysis, the underlying problem is that there are less number of cars that are equipped with GPS. Machine learning in such scenarios helps to estimate the regions where congestion can be found on the basis of daily experiences.

3. Social Media Services

Social media platforms are utilizing machine learning for their own and user benefits. Here are a few examples that you must be noticing, using, and loving in your social media accounts, without realizing that these wonderful features are pure applications of ML.

People You May Know: Machine learning works on a simple concept: understanding with experiences. Facebook continuously notices the friends that you connect with, the profiles that you visit very often, your interests, workplace, or a group that you share with someone etc. On the basis of continuous learning, a list of Facebook users are suggested that you can become friends with.

Face Recognition: You upload a picture of you with a friend and Facebook instantly recognizes that friend. Facebook checks the poses and projections in the picture, notice the unique features, and then match them with the people in your friend list. The entire process at the backend is complicated and takes care of the precision factor but seems to be a simple application of ML at the front end.

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