WHAT IS MACHINE LEARNING?

        Technique of Machine Learning  

Machine learning is used to make sense of large datasets using automated algorithms. It's one of the most important technologies in artificial intelligence (AI). Machine learning uses statistical techniques to allow computers to learn from previous experience without being explicitly programmed. Instead, machine learning provides computer systems with the ability to learn new information on their own.

Machine Learning Algorithms Is a basic idea behind machine learning is that if you know enough about a certain process, you can figure out how to automate it yourself. Then, once you've built self-learning algorithms into your software, machines will need less supervision on some tasks and complete them faster than humans could every possibly. But how far away are we from actually achieving that?

We're already pretty close. For example, machine learning is used in spam filters to detect unwanted content and properly classify it as such without human supervision. It's also used to suggest accurate responses when you get a phone call from an unknown number or provide automated translations of texts received in a different language than the one you speak (though Google's Pixel Buds are now taking this idea to live conversation). Machine learning can even be used for voice recognition by directing the algorithm through enough audio samples. After seeing tens of thousands of examples where specific words are spoken, the software will become adept at recognizing those same words if they are detected later on.

In the broadest sense, machine learning can be defined as a branch of artificial intelligence where systems are designed to evolve behaviors based on empirical data, rather than being explicitly programmed. Put another way: machines learn from data and experience, adjusting their behaviors accordingly, rather than following strictly static rules set by a programmer. By no means an exact science, "Machine Learning" is a term that encompasses a wide range of techniques for achieving AI. Some examples include statistical regression techniques such as Support Vector Machines (SVMs) and k-Nearest-Neighbors (KNN), Artificial Neural Networks Deep Learning, etc. In general, these techniques provide software libraries which take parameters and return predictions or actions over some input data. Inference techniques like SVMs, Random Forests, etc. are one of the more popular ways to extract information from data that arrives in the form of multi-dimensional arrays. Neural networks are also increasingly common because they have demonstrated some success in tasks like computer vision and speech.

1. Introduction

Today, machine learning is all around us. Whether it be on Facebook or amazon, whether its Netflix's recommendation system or Google's search engine - machine learning is what powers some of our favorite technology services. It has vast implications for how we view the world around us and even allows us to accomplish tasks which were hitherto not possible!

I personally find this field fascinating because it combines disciplines ranging from mathematics (linear algebra, probability theory) to computer science (algorithms, data structures) to engineering (programming). Fortunately, it is not necessary to have a degree in any of these subjects in order to get started. This article provides an introduction of the field by answering three main questions:

What different types of problems can machine learning solve? What are some general concepts that are useful when approaching problems with machine learning models? How does one get started doing machine learning on their own without having a PhD or being part of Google's research department?

2. The Problems solved by Machine Learning:

Broadly speaking, there are two sorts of tasks where machine-learning techniques shine compared to traditional software engineering approaches. First, building complex software for solving specific narrow problems, and second, building software that adapts to changes in the problem. Let us look at some examples of each type:

2.1 Narrow Tasks

Machine learning is not only all around you, but also inside your phone! Almost every modern smartphone includes a number of features powered by machine learning such as autocorrect for spelling mistakes, face detection, photo tagging, background noise suppression while talking on the phone etc. For example, detecting faces in an image is made easy with OpenCV's Hair feature-based cascade classifiers which are run against grayscale images captured by webcams or smart devices' cameras.

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