Machine Learning – Learning VS Designing

In this article, we will learn about Learning and Designing and what are the main differences between them.

In Machine learning, the term learning refers to any process by which a system improves performance by using experience and past data. It is kind of an iterative process and every time the system gets improved though one may not see a drastic change but yeah every time the system gets improved. Learning is very important because we don’t have to build or create the system from scratch.

There are several types of machine learning, including:

1. Supervised Learning: In supervised learning, the machine learning algorithm learns from labeled data, which means the training data has predefined input-output pairs. The algorithm learns to predict the output for new inputs based on this training data. Examples of supervised learning include classification and regression.
2. Unsupervised Learning: In unsupervised learning, the machine learning algorithm learns from unlabeled data, which means the training data has no predefined output. The algorithm learns to identify patterns or groupings in the data without any supervision. Examples of unsupervised learning include clustering and anomaly detection.
3. Semi-supervised Learning: In semi-supervised learning, the machine learning algorithm learns from a combination of labeled and unlabeled data. The algorithm uses the labeled data to guide its learning and the unlabeled data to identify patterns or groupings.
4. Reinforcement Learning: In reinforcement learning, the machine learning algorithm learns by interacting with the environment and receiving rewards or punishments for its actions. The algorithm learns to maximize its rewards over time by taking actions that lead to the highest reward.
5. Deep Learning: Deep learning is a subset of machine learning that uses neural networks with many layers to learn from data. Deep learning has achieved remarkable success in tasks such as image recognition, natural language processing, and speech recognition.

Designing: As the name suggests Designing is the process to make a scratch system based on particular requirements. Since the system is being designed from scratch so one needs lots of data or I would say training data. After making the system, it has to be tested so far that testing data is required. The ratio of training data and testing data is about 80% and 20%.

Table of differences between Learning vs Designing:

Now let’s see the differences between them :

Learning	Designing
It is a process by which a system improves performance from the past experience.	It is a process to design a system based on various requirements.
Learning does not require testing.	Designing requires testing
It gains experience from past data.	It gains experience when data is fed to the design.
As such no representation of the data is required.	It represents the data with the help of various functions.
At times it preprocesses the data and does filtering of noisy data.	It doesn’t preprocess the data at all.
It does require a measuring device.	It requires a problem description.
Learning skills is required.	Designing skills are required.
Clustering, Description, and Regression is used in the learning process.	Decision tree, table are used while the designing process.