Machine Learning (ML) is a field of computer science with the
help of which computer systems can provide sense to data in
much the same way as human beings do. Its key focus is to
allow computer systems to learn from experience without any
human intervention.
Machine learning is not a new technology. Most of the learning
algorithms, such as neural networks, are based on decades-old
research. Its growth is tied to developments in three important
areas:
1.Availability of data: Billions of people are online with
billions of connected devices or sensors. It generates a
large amount of data which when combined with
decreasing costs of data storage, is easily available for use.
Machine learning uses this as training data for learning
algorithms, developing new rules to perform increasingly
complex tasks.
2.Computation power: Extremely powerful computers and
the ability to connect remote processing power through the
Internet makes it possible for machine-learning techniques
to process enormous amounts of data.
3. Innovations of the algorithm: The techniques of
machine learning, specifically in layered neural networks –
also known as “deep learning” – have given a boost to new
services.
Machine learning technology is highly in demand. Both start-ups and large companies want to hire professionals in machine learning. If you are good at designing and implementing machinelearning algorithms, you can be a machine-learning engineer. You can be a data scientist, analyse, and interpret large chunks of data. If you have a strong background in statistics, economics, and calculus, you can also be a data analyst. You can also be a data architect and take care company’s big data ecosystem.
Although machine learning evokes thoughts of science fiction,
it already has many uses today, for example:
1. Spam email filtering: Email services use machine
learning to filter incoming emails. Users can train their
spam filters by marking emails as “spam”.
2. Personalized content: Online services use machine
learning to personalize the user’s experience. Services, like
Flipkart or Netflix, “learn” from your previous purchases
and the purchases of other users in order to recommend
relevant content for you.
3. Fraud detection: Banks use machine learning to check
for strange activity on your account. Any unusual activity,
such as foreign transactions, could be flagged by the
algorithm.
4. Speech recognition: Applications use natural language
processing to optimize speech recognition functions.
Microsoft’s “Cortana”, Amazon’s “Alexa” or Apple’s “Siri”
are examples of some intelligent personal assistants.