If you are a tech geek then you might be listening a lot about Artificial Intelligence and Machine Learning.
So, let’s discuss a little about Artificial Intelligence.

Artificial Intelligence

It is a broad area of computer science that make the machine seem like they have human intelligence. The goal of AI is to mimic the human brain and to create systems that can function like humans.
Following are the few examples of AI:
– Smart Assistants (SIRI, ALEXA)
– Drone Robots
– Conversational Bots

Applications of AI:

So here are some of the applications of AI

  • Astronomy
  • Healthcare
  • Robotics
  • Gaming

To know more about Artificial Intelligence click the link below
https://www.investopedia.com/terms/a/artificial-intelligence-ai.asp

So , lets discuss about Machine Learning in detail.

Machine Learning

It is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.

It focuses on the development of computer programs that can access data and use it learn for themselves.

Machine Learning . 1

The process begins with observations or data.
For example direct experience, instruction, in order to look for patterns in data and make better decisions.

So, the primary aim is to allow the computers to learn automatically.
And without human intervention and adjust accordingly.

In order to learn Artificial Intelligence and Machine learning one needs to have a basic knowledge of the programming languages.
http://acquirehowto.com/new-programming-languages-growing-rapidly-in-modern-industries/

Difference between Machine Learning and Artificial Intelligence

ARTIFICIAL INTELLIGENCE
  • The aim is to increase chance of success and not accuracy .
  • It work as a computer program that does smart work.
  • AI is decision making.
MACHINE LEARNING
  • The aim is to increase accuracy .
  • It is a simple concept machine takes data and learn from data.
  • It allows system to learn new things from data.

Machine Learning Methods

These algorithms are of two types:

  • Supervised
  • Unsupervised

So, above listed types are discussed below.

Supervised algorithms

It can apply what has been learned in the past to new data using labeled examples to predict future events. Starting from the analysis of a known training dataset, the learning algorithm produces an inferred function.
It makes predictions about the output values. The system is able to provide targets for any new input after sufficient training.
The learning algorithm can also compare its output with the correct, intended output.
And it find errors in order to modify the model accordingly.

Unsupervised algorithms

They are used when the information is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure. The system doesn’t figure out the right output.
But it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data.

Machine learning . 2

Semi-supervised algorithms

This falls somewhere in between supervised and unsupervised learning. Since they use both labeled and unlabeled data for training – typically a small and a large amount of data.
The systems that use this method are able to improve learning accuracy. Usually, this method is chosen when the acquired labeled data requires skilled and relevant resources. Otherwise, acquiring unlabeled data generally does not require additional resources.

Reinforcement algorithms

This is a method that interacts with its environment by producing actions and discovers errors. Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning.
This method allows machines and software agents to automatically determine the ideal behavior.
Simple reward feedback is required for the agent to learn which action is best. This is known as the reinforcement signal.

Conclusion

So, Machine learning enables the analysis of massive quantities of data. Also it delivers more accurate results.
It also identifies profitable opportunities or dangerous risks. It may also requires additional time and resources to train it properly. Combining machine learning with AI and cognitive technologies can make it even more effective.