AI in Machine Learning

 What is Machine Learning ?

Machine learning is part of Computer engineering course. Machine learning is way to create new thing as robot , Ai machine , bots etc.

There are a variety of different machine learning algorithms, with the three primary types being supervised learning, unsupervised learning and reinforcement learning. 

=> 3 types of machine learning algorithms

As with the different types of AI, these different types of machine learning cover a range of complexity. And while there are several other types of machine learning algorithms, most are a combination of—or based on—these primary three.

Supervised learning is the simplest of these, and, like it says on the box, is when an AI is actively supervised throughout the learning process. Researchers or data scientists will provide the machine with a quantity of data to process and learn from, as well as some example results of what that data should produce (more formally referred to as inputs and desired outputs).

The result of supervised learning is an agent that can predict results based on new input data. The machine may continue to refine its learning by storing and continually re-analyzing these predictions, improving its accuracy over time.

Supervised machine learning applications include image-recognition, media recommendation systems, predictive analytics and spam detection.

                                           

                                               source by : geeksforgeeks.org

Unsupervised learning involves no help from humans during the learning process. The agent is given a quantity of data to analyze, and independently identifies patterns in that data. This type of analysis can be extremely helpful, because machines can recognize more and different patterns in any given set of data than humans. Like supervised machine learning, unsupervised ML can learn and improve over time.

Unsupervised machine learning applications include things like determining customer segments in marketing data, medical imaging, and anomaly detection.

     source by : geeksforgeeks.org

Reinforcement learning is the most complex of these three algorithms in that there is no data set provided to train the machine. Instead, the agent learns by interacting with the environment in which it is placed. It receives positive or negative rewards based on the actions it takes, and improves over time by refining its responses to maximize positive rewards.

                                               source by : geeksforgeeks.org

Application of Machine learning :

• Automatic Language Translation
• Medical Diagnosis
• Stock Market trading
• Online fraud detection
• Virtual personal assistant
• Email spam and malware filtering
• Self driving cars
• Product recommendations