Every day a large portion of the population is at the mercy of rising technology, yet few understand what it is. Each generation has formed its fantasy of a world ruled or at least served by robots. We have been conditioned to expect flying cars that steer clear of traffic and robotic maids whipping up our weekday dinner. But if the age of Artificial Intelligence is here, why don’t we now get to see what we have expected for a long time?
But technology has now started to groom itself with time. If you’ve ever browsed Netflix movie suggestions or told Alexa to order a pizza, you are probably interacting with Artificial Intelligence more than you realize. And that’s kind of the point.
Artificial Intelligence is designed so you don’t realize there is a computer calling the shots. But that also makes understanding what AI is, and what it is not. And this one is a little complicated. In this article, I’ll be helping you with the subset of Artificial Intelligence and Machine Learning. These are the technical terms that beginners trying to research may find difficult to grasp. But here, I’ve tried to explain it in as simple a way as I can.
Let us now see what Machine Learning is and what is the use of Machine learning in data science in conclusion.
What is Machine Learning?
Each one of us learns from our past experiences and machines follow us and our instructions. But what if we train the machines to learn from their past data and make them learn what we can do, but faster than us and in an effective way? Well, this is what is called Machine Learning.
Though, it is not only about learning but also about understanding and reasoning. Here, I’ll help you to know and learn the basic information for Machine Learning.
There are multiple algorithms used in the machine learning sector to solve challenging problems. All these algorithms can be categorized into specific learning. A machine can learn in many ways and the three most well-known and important machine-learning algorithms are:
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Let’s find out what are three ways of learning are.
Supervised Learning
Supervised Learning is specifically used to train the system using labeled data. Now, what is labeled data? Labeled data is the data where you already know the output. The model is introduced here to match the inputs with the result. For instance, supervised learning is used to identify the image of an animal. In addition, let us now see some of the machine-learning algorithms that fall under these learning categories, and these are.
Linear Regression
Logistic Regression
Support Vector Machines
K Nearest Neighbors
Decision Tree
Unsupervised Learning
Unsupervised Learning uses unlabeled data to train the machine models. Here, the unlabeled data is the data where there is no fixed output variable. The model is trained in such a way that it learns from the data, discovers patterns and features in the data itself, and releases the output in the same data. For unsupervised learning, the algorithms are:
K Means Clustering
Hierarchical Clustering
DBSCAN
Principal Component Analysis
Reinforcement Learning
Lastly comes reinforcement learning which trains a machine model to take certain actions and maximize the rewards in a particular situation. An agent and environment are used to produce actions and rewards. The agent comes with the two states as Start State and End State. But there might be chances of having different parts for reaching the end state. In this form of learning, there are no pre-determined targets variable involved. The algorithms falling under reinforcement learning are:
Q-Learning
SARSA
Monte Carlo
Deep Q Network
Conclusion
Machine Learning is a subpart of Artificial Intelligence that allows a computer system to learn from the data. Also, machine learning algorithms entirely depend on data as they are trained on information that is delivered by data science. Mark that without the involvement of data science, machine learning algorithms would not present any output as they are trained over the datasets.