A Beginner’s Guide to Machine Learning
Nowadays, programming is a source of power & the ability to acquire this knowledgeable power makes it closer to the human language. The involvement of coding in our everyday life helps to make this point more comprehensible. Think about the most mundane task of your routine like driving a car, playing a game, researching for a paper, chatting with your friend on WhatsApp, etc. All of these tasks tend to support the fact that how programming has influenced the world.
Have you ever noticed similar ads being displayed to you later on-screen after surfing on the Internet looking for some particular product like a pair of Puma shoes or a Fastrack watch? This is called remarketing & the process includes the study of algorithms. This study of algorithms is one of the applications of Machine Learning.
You must be familiar with the concept of Machine Learning. Let’s break the concept into chunks and discuss the nitty-gritty of it.
According to IDC prediction, 75% of the organization and ISV developers will utilize machine learning in somewhere around one of the applications they develop.
In 2019, 40% of digital transformation will include AI services and 75% of all business applications will incorporate some type of AI by 2022.
Machine Learning (ML) & Artificial Intelligence (AI) are two hot assets that are going to become the driving force of the tech-world in the coming time.
What is Machine Learning?
Let’s start from scratch!
In 1959, Arthur Samuel defined Machine Learning as a “field of study that gives computers the ability to learn without being explicitly programmed”.
We know that humans have the tendency to perceive things & retain them in their memory. A machine follows the command given by humans. Therefore, Machine Learning includes training of the machine to analyze the data & leading to a prediction or probability of an event to happen. ML is the subset of AI that is continuously learning & processing from different types of human experiences. More data helps the machine to build better logic with a higher accuracy rate.
How does Machine Learning work?
· Appropriately assimilating the data for furthering Data Processing is necessary. The data should be of better quality for modeling.
· The data that has been collected in the raw form is required to be pre-processed, and this incorporates Data Processing. For example, a data structure (tuple) might have an absent value and that can be occupied with the appropriate values to process Machine Learning or Data Mining. The idea is to make the data relevant to ease the Machine Learning performance. A data in text or image format needs to be converted into a numerical format like list, array, or matrix, a format that is easily comprehensible for the machine.
· Segregation of the input data is done into training, cross-validation, and test sets- in the ratio 6:2:2.
· During the training set, suitable algorithms and techniques are employed to facilitate the building of models.
· The theorized model with apt data that was not part of the training process is tested and its performance using metrics such as F1 score, precision, and recall are evaluated.
Pre-requisites to learn ML:
· Statistics and Probability
· Programming Skills – languages such as Python, R, MATLAB, C++, or Octave
If you are a tech-enthusiast with a basic knowledge of coding and are keenly interested to learn Machine Learning from scratch, you can join a special course at Coding Blocks, Master Machine Learning, and become a master of your dreams to ace the human mind with technology.
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