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Machine Learning: A Simple Introduction
Patterns to Predictions: Exploring Machine Learning
This article is for people wanting to learn more about Machine Learning. Before you start developing "Stock Prediction Algorithms", it is truly important to understand what exactly happens in machine learning and when you should employ machine learning algorithms.
ML is generalizing patterns
A pattern is an occurrence that repeats based on a set rule or condition. Learning often is an idea or a change that substitutes something old with something new. With the help of learning, we can identify patterns and our mind over time stores the new information. Unlike our mind, however, for a machine to learn it needs to find patterns in numbers. In simple words:
Machine Learning is the process of converting data into numbers and finding patterns.
You might wonder why we need to decipher these patterns, well patterns make our life easier by making our tasks simpler. These patterns can help us solve the problems at hand by pointing in a direction toward the solution.
How do we find these patterns?
Algorithms, what did you expect? In Machine Learning, an algorithm is a procedure or a series of tasks run on the data to create a machine learning "model". The "model" identifies patterns appropriate to the dataset representing what the algorithm has learned during its application. You can imagine a model as a function that can adapt to new data by tweaking its parameters.
Fundamentally machine learning models are developed to predict or classify patterns from a given dataset.
For those curious about Deep Learning, it is a form of Machine Learning inspired by the human brain where Artificial Neural Networks are employed to solve complex tasks.
There is a whole bunch of stuff that can be discussed about Machine Learning: its applications, techniques, effectiveness, constraints and more...This article was solely focused on giving an introduction to Machine Learning and nothing more. Taking things one step at a time can help you build patterns helping you break down complex concepts into simpler ones.
Hope this article helps you dive a little deeper into the world of ML!