Followers
Views
0 Likes

Intorduction to Machine Learning

What is Machine Learning ?

ML is an application/discipline of Artificial Intelligence, that provides system the ability to automatically learn and improve itself, without being explicitly programmed. And AI aims at making machine smarter by giving those capabilities to think on their own feet to make decisions or mimic human activities and one can think of ML as a subset of AI.

It focuses on the development of computer programs that can access data and use it to learn themselves. The learning process begins with observations of data, experience or instructions, this process helps to look for a pattern in data and make better decisions in the future.

Applications of ML :

  1. Creating our own digital assistants.
  2. Integrating them in CCTV cameras.
  3. Used in finding out about fake news.

Now a question arises, how does machine learning works ?

It comprises of different algorithms where it performs various amount of tasks by using input data. The more amount of data is fed to these algorithms, the more accurate the predicted data is.

When we get our output, we find out the accuracy of the algorithm according to the input data given.

Let's understand this with some example, suppose we want to predict the price of a new model of bike. So the steps will be like this:

  1. We will have some input data, consisting of Model Names, Model Specifications, Bike Color, Price of those models, and many more according to our need.
  2. A predefined model that we have to create before entering input data, now these models are the techniques of machine learning, about which we will talk later.
  3. After fetching these inputs into these models, we will start it and will start predicting the price of a new model. Remember this, it will learn from past mistakes, without being programmed.
  4. And voila, we have got our first machine learning model. And now we have to find out the accuracy of our model which we can find out easily by using error rate.

Types of Machine Learning:

Below attached image is a brief overview on different types of machine learning along with some examples to improve the understanding behind them.


Supervised Learning:

Now it can be understood by its name only, this learning works under some supervisor. In this learning, the training dataset contains both input data and the value we want to predict. And here the training data is labeled.

Examples of supervised learning are, naive bayes, gradient boosting.

Unsupervised Learning:

This learning does not use output data. And it is mostly used to pre-process data, pre-train supervised learning algorithms. Examples are, PCA, LCA, K-Means. There is no labeled data.

Semi-Supervised Learning:

It combines small amount of labelled data to large amount of unlabeled data during training. Applications of this learning includes, Content Classification, Speech Analysis.

Reinforcement Learning:

It is the best way to earn the greatest reward. This learning follows various steps like the model (agent) will choose the action to maximize the reward based on what kind of environment we are choosing to implement. These actions will change the state of the model and environment. They may be interpreted to reward the model. By performing this in loop, the behavior of the model will be improved automatically and the accuracy of our model will surely show some improvement.

This kind of learning performs better on dynamic systems.

Now remember, this article contains only the introductory part, you will learn about these algorithms properly in upcoming articles.

Till then, keep learning !

By-

0 likes followers Views

HelpFeaturesMade with in INDPrivacyAbout
© 2020 Peppychunk.com