Demystifying Machine Learning refers to the series of blogs in which I am going to explain the different algorithms of Machine Learning with behind the scenes mathematics and code implementation from scratch. This is an introductory article for the entire series about what the audience can expect, what are Algorithms we are going to cover and what prerequisites you need to fulfil in order to get the most out of this series. There are a few things you need to be comfortable with so that you can get the best out of the articles. Don't worry, if you're not a pro in maths. It's going to be a beginner-friendly introduction to all algorithms and mathematics with explaining every concept in brief.

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Machine Learning (ML) and Artificial Intelligence (AI) are buzzwords heard everywhere today, especially in the news about technology. It seems as everything today has some AI and ML properties. Marketers have even begun to use these terms to make their goods or services appear more attractive, making ML and AI such ubiquitous acronyms that it seems you can find them in the most unlikely of products.

Machine learning is an increasingly prevalent buzzword in the media. Its applications in science and the private sector are frequently discussed--but what about global development? Can it also help advance fields like health, agriculture, and financial inclusion? That's because it can help us uncover previously invisible patterns in data, to identify the most effective solutions and target them in the right way. Machine learning (ML) has been around for decades, but now is our chance to apply it to development challenges in new ways, for three reasons.

Before we jump into the definition of machine learning we first have to clarify its context in relation to a term much more common in popular culture today, artificial intelligence (AI). AI was a term first coined in 1956 by John McCarthy at Dartmouth College at a conference where the topic was first discussed. Put simply, artificial intelligence is the ability of a machine to perform tasks that are characteristic of human intelligence. With that definition in mind, how does a machine go about performing these kinds of tasks, which are so closely associated with human intelligence; planning, object and sound recognition, language understanding, problem solving and learning? Well, one of the ways a machine can go about doing this is through machine learning. Machine learning can be defined as the field of computer science that deals with algorithms that learn from, and make predictions on, data without needing to be explicitly programmed.

Have you ever had a credit card transaction declined when it shouldn't have been? Or been on the receiving end of a personalized email or web ad? Have you ever noticed a site giving you recommendations for things you might be interested in when you're shopping online? And my last example, have you ever had an offer from a company designed to stop you from leaving them as a customer? If any of these things have happened to you, then you've probably been on the receiving end of a machine learning algorithm, employed by a company you do business with (or in some cases, have merely considered doing business with). We're going to take you behind the scenes and give you a layman's view of machine learning so you can see what kind of problems they can solve.

We all have heard about machine learning and its path-breaking applications. So, what is machine unlearning and why the need for it? Well, let's first start with the basic understanding of what it means. Machine learning is a type of artificial intelligence that enables machines to analyse, learn, and adapt to the surroundings, which was earlier done on an entirely different data set. Machine unlearning is just the opposite of it.

Demystifying Machine Learning - How Do Machines Really Learn? Learn the answer and how it can affect your business. Autonomous cars taking us on our favorite and most efficient routes, virtual assistants serving up the exact data a doctor needs to diagnose an illness or that an engineer needs to identify a faulty part, customer support bots that are always available to answer your questions and book your appointments accurately and quickly. All of these use-cases are either here or will be soon, and they all rely on Machine Learning to be successful. But how do the machines learn?

In the utopian future, machines will know when they are about to break down, make adjustments to their operations to ward off permanent damage while they continue to work, order the parts needed to repair themselves, and schedule a qualified technician to perform the repairs. They will be able to schedule maintenance so that unplanned downtime is eliminated, and generate service plans guaranteed to fix the problem right the first time around. The continuing evolution of Industrial IoT (IIoT) technology is ushering in a new era of industrial automation that is driving dramatic advancements in understanding equipment health, and enabling use cases that provide value across the business. From predictive analytics to condition-based maintenance and asset optimization, machine learning is critical to an IIoT system that is able to scale across hundreds or thousands of different vendors' equipment at multiple locations. But what exactly is machine learning and what do you need to know in order to make it work for your business?

Demystifying Machine Learning - How Do Machines Really Learn? Learn the answer and how it can affect your business. Autonomous cars taking us on our favorite and most efficient routes, virtual assistants serving up the exact data a doctor needs to diagnose an illness or that an engineer needs to identify a faulty part, customer support bots that are always available to answer your questions and book your appointments accurately and quickly. All of these use-cases are either here or will be soon, and they all rely on Machine Learning to be successful. But how do the machines learn?