Santos Ozoemena is a Data Scientist, author. He has worked and consulted for many startups and big tech companies namely Disney, CVS and T-mobile and so much more. You can subscribe to future updates and projects here - santosbooks.com

Machine learning is a tool that is used to extract information from data. The most common machine learning algorithms automate processes by generalizing "some rules" based on some examples. In more general terms, the machine learning algorithm is given pairs of inputs and desired outputs, and the algorithm finds a way to produce the desired output given some unseen inputs. This is called supervised learning because the user "supervises" the machine learning algorithm by giving it the desired output for each learned example. Unsupervised learning is the second type of machine learning algorithm where input data is known, but no output data is given to the algorithms.

The focus of this article will be on explaining the concept, intuition and a simple implementation of a active learning pipeline. The pipeline will be built out in Python and we'll be using active learning to improve the performance of a binary classification model.

Machine learning is a branch of artificial intelligence that focuses on the use of algorithms to make decisions. These algorithms are trained with historical data and based on what they infer from that data, are able to make predictions, classifications, and numerous other decisions, all without being explicitly programmed to do so. While the term machine learning has only recently become a buzzword, it has been around as far back as 1959, when it was coined by Arthur Samuel, a pioneer in the field. One of the earliest applications was in a game of checkers, in which self-proclaimed checkers master, Robert Nealey, lost against a computer on an IBM 7094. From those humble beginnings, technological developments around storage and processing power have enabled more powerful and widespread applications of machine learning, such as Amazon's recommendation engine and Google's self-driving cars.

Supervised vs Reinforcement Learning: In Supervised Learning we have an external supervisor who has sufficient knowledge of the environment and also shares the learning with a supervisor to form a better understanding and complete the task, but since we have problems where the agent can perform so many different kind of subtasks by itself to achieve the overall objective, the presence of a supervisor is unnecessary and impractical. We can take up the example of a chess game, where the player can play tens of thousands of moves to achieve the ultimate objective. Creating a knowledge base for this purpose can be a really complicated task. Thus, it is imperative that in such tasks, the computer learn how to manage affairs by itself. It is hence more feasible and pertinent for the machine to learn from its own experience. Once the machine has started learning from its own experience, it can then gain knowledge from these experiences to implement in the future moves.

"Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed" defines Expert System. In the last decade, machine learning has offered us many things such as better knowledge of the human genes, a more effective web search, driverless cars and much more. Clearly, Machine Learning is so pervasive today that we probably use it numerous times every day without knowing it. Researchers think that Machine learning is our best bet in making progress towards a human-level Artificial Intelligence. Machine Learning, in simple words is about using data you have to make predictions.

Traditional programming requires human to define set of instructions which requires a tons of code and leave a plenty of room for error. With machine learning, we just need data, a tons of data to be precise. Lucky for us, thanks to internet and smartphones, we have a tons of data. In machine learning, instead of following hard coded instructions, a program can learn from data or adapt its behavior according to experience. We can divide Machine Learning into three broad categories.

Machine Learning is guiding Artificial Intelligence capabilities. Image Classification, Recommendation Systems, and AI in Gaming, are popular uses of Machine Learning capabilities in our everyday lives. How can we better understand Supervised, Unsupervised, and Reinforcement Learning? Let's start with Supervised Learning, which makes up most of the uses for Machine Learning today. In Supervised Learning, the machine already knows the output of the algorithm before it starts working on it.

The answer is, in one word, algorithms. Algorithms are sets of rules that a computer is able to follow. Think about how you learned to do long division -- maybe you learned to take the denominator and divide it into the first digits of the numerator, then subtracting the subtotal and continuing with the next digits until you were left with a remainder. Well, that's an algorithm, and it's the sort of thing we can program into a computer, which can perform these sorts of calculations much, much faster than we can

Once we start delving into the concepts behind Artificial Intelligence (AI) and Machine Learning (ML), we come across copious amounts of jargon related to this field of study. Understanding this jargon and how it can have an impact on the study related to ML goes a long way in comprehending the study that has been conducted by researchers and data scientists to get AI to the state it now is. In this article, I will be providing you with a comprehensive definition of supervised, unsupervised and reinforcement learning in the broader field of Machine Learning. You must have encountered these terms while hovering over articles pertaining to the progress made in AI and the role played by ML in propelling this success forward. Understanding these concepts is a given fact, and should not be compromised at any cost.