Learning is a universal skill that is achieved by every living organism on the planet. Learning can be defined by the acquisition of knowledge or skills through experience, study, or by being taught. Whether it be a plant learning how to act according to light and temperature, a monkey learning how to peel a banana, or humans learning how to ride a bike. This functionality is what makes us unique and evolve over time.

Machine Learning

We are in the age where machines are no different. Machine Learning is still a new concept. We can teach machines how to learn and some machines can even learn on their own. This magical phenomenon is called Machine Learning.

Hopefully, this article will provide us some useful insights and open your mind to what computers can do now-a- days. Following is a high-level overview about machine learning

What is Machine Learning

Machine Learning include a computer to recognize patterns by examples, as compared to programming it with specific rules. These patterns are found in Data.

The meaning of machine learning is listed below:

Machine= Your Computer or Machine
Learning = Searching patterns in data

Machine Learning is about:

Creating algorithms that learns from complex functions from data to make predictions on it.
The process can be summarized in 3 Steps:

  1. It takes some data.
  2. Find a pattern from the data.
  3. Predicts new pattern from the data.
    Applications of Machine Learning
    Before we dive in to the depth of process, here is a quick overview of what machine learning is capable of doing:
    Healthcare: For predicting patient diagnostics for reviewing.
    Social Network: Predicting match preferences on a dating website for compatibility.
    Finance: Predicting fraudulent activity on credit card.
    E-commerce: Predicting customer churn.
    Biology: Finding patterns in gene mutations that can tell about cancer.

How Do Machine Learning?

In simple words, just know that machines “learn” by finding patterns in similar data. Data is the information that you acquire from the world. The more data you give to a machine, the “smarter” it gets.

But remember one thing that not all data are the same. Just imagine that you are a pirate and your life mission were to locate the buried treasure somewhere in the island. To find out treasure you need a proper amount of information. Like data, this information will lead you to the right direction or the wrong direction. The enhanced information/data that is obtained, the more uncertainty is reduced, and vice versa. So, it is important to keep in mind the type of data you are giving to your machine to learn

3 Types of Machine Learning

The three main categories of machine learning are listed below:

Supervised learning: In this, the machine learns from labeled data. Normally, the data in this type of learning is labeled by humans.

Unsupervised learning: In this, the machine learns from unlabeled data. This means, there is no “right” answer given to the machine to learn, but the machine hopefully finds patterns from the data to come up with an answer.

Support learning: In this, the machine learns through a reward-based framework.

Now, we will discuss each type in detail as discussed below:

1- Supervised Machine Learning
Supervised learning is the most common and studied type of learning because in this type of learning it is easy to train a machine to learn with labeled data as compared to un-labeled data. Depending on what you want to predict, supervised learning can help you to solve two types of problems: regression or classification.

Regression: If you are going to predict continuous values like predicting the cost of a house or the weather outside in degrees, you will use regression. This type of problem does not have specific value constraint because the value could be any number without no limits.

Classification: If you want to predict discrete values, like classifying something into categories, you will use classification. For instance, in a problem like where the question is “will he make this purchase” The answer to this query lies in two specific categories: yes or no. This is also called a binary classification problem.

2-Unsupervised Machine Learning
In this, there is no labeled data for machines to learn from, the goal for unsupervised machine learning is to analyze patterns in the data and to group them. Unsupervised learning machines trying to learn “on their own”, without any help. Depending on what you want to group together, unsupervised learning can assemble the data together by clustering or association.

Clustering Problem: Unsupervised learning try to solve this problem by looking for similarities in the data. If there is a common cluster or group, then the algorithm will categorize them in a certain form. For instance, grouping the data of customers based on their past buying behavior.
Association Problem: Unsupervised learning try to solve the problem by understanding the rules and meaning behind different groups.

Stores may need to inquire what type of products were purchased together and they can possibly use this information to organize the placement of these products for easier access. One store found out that there was an association between customers buying beer and diapers. A statement can be deduced from this statement that males who had gone out to buy diapers for their babies also buy the beer as well.

3-Reinforcement Machine Learning
This type of machine learning uses a reward/penalty system. The goal is to reward the machine when it correctly learns and penalize the machine when it learns incorrectly.

Support Machine Learning is a subset of Artificial Intelligence. With the wide range of possible answers from data, the process of this type of learning involves a mathematical calculation.

Examples of Reinforcement Learning:
• Training a machine about how to learn and play Super Mario.
• Self-driving cars

Final Thoughts
Hope that, now you have better understanding of machine learning. If there is anything that you can share about this blog, we will be happy to add. Please do share it with people because sharing is caring.