Machine Learning

Machine Learning 

What is  Machine Learning?

"Machine learning is the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence."

In traditional programming, we feed a set of instructions for a defined set of scenarios to the computer. Then the computer will utilize its computing capabilities to help human process the data efficiently. In machine learning, a large amount of data is input into the computer. Then computer processes all the data and comes up with a trained model(solution). After this model is used to solve the unseen problems of the real world.

Applications of  Machine Learning

Machine Learning has started growing fast because nowadays we have less expensive computational power and lots of data which we can use the train our models. Therefore Machine learning is applied in a lot of areas. Such as,
• Search Engines(to give appropriate results)
• Mail services ( Spam filters)
• Handwritten character recognition
• Recommendations
• Word suggestions in Keyboards
• Robots / Autonomous Vehicles
• Natural Language Processing


Basic Types of Machine Learning

There are three types of Machine Learning. They are supervised machine learning, unsupervised machine learning, and reinforcement learning.

Supervised Learning

In supervised Learning, the algorithm learns to map a given input to a desired output .so it can map an unforeseen input to an output. Examples of supervised Learning are Spam filters, handwritten character recognition.

Unsupervised Learning

In unsupervised learning, the algorithm learns without having a desired output for a given input. It just attempts to find out the structure within the data set (identify similarities and divisions among data). Clustering is an example of unsupervised learning.

Reinforcement Learning

reinforcement learning concerned about how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward.  an agent takes actions in an environment, which is interpreted into a reward and a representation of the state, which are fed back into the agent.

Benefits of Machine learning

1.Simplifies Product Marketing and Assists in Accurate Sales Forecasts
2.Massive Data Consumption from Unlimited Sources.
3.Rapid Analysis Prediction and Processing of data.
4. Interpret Past Customer Behaviors of businesses.
5. Facilitates Accurate Medical Predictions and Diagnoses in the health sector.
6.Simplifies Time-Intensive Documentation in Data Entry
7. Improves Precision of Financial Rules and Models.

Machine Learning has huge effects on the economy and our day to day life. By using machine learning knowledge we can automate entire work tasks and industries and the job market will be changed rapidly. Therefore a lot of engineers and researchers are paid attention to Machine Learning field.