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Machine Learning is a hot topic right now and everyone is trying to get their hands on any information they can get about ML.

Machine Learning falls under the umbrella of AI that provided systems with the ability to automatically learn and improve from experience without being explicitly programmed.

The process of learning begins with observation of data, such as examples, direct experience or instruction, in order to look for patterns in data and make better decisions in the future based on the example we provided.

Machine learning, deep learning, artificial intelligence... The science of getting machines to perform actions without explicitly programming them to do so can be intimidating for the uninitiated. These machine learning articles aim to unpack the black box for beginners, with introductions to overall concepts.

Machine Learning, computers find insightful information without being told where to look. Instead, they do this by leveraging algorithms that learn from data in an iterative process.

Machine Learning is the ability to adapt to new data independently and through iterations. Basically, applications learn from previous computations and transactions and use “pattern recognition” to produce reliable and informed results.

Machine Learning is, undoubtedly, one of the most exciting subsets of Artificial Intelligence. It completes the task of learning from data with specific inputs to the machine. It’s important to understand what makes Machine Learning work and, thus, how it can be used in the future.

The Machine Learning process starts with inputting training data into the selected algorithm. Training data being known or unknown data to develop the

final Machine Learning algorithm. The type of training data input does impact the algorithm, and that concept will be covered further momentarily.

Types of Machine Learning
Supervised Learning- Supervised learning is the most popular paradigm for machine learning. It is the easiest to understand and the simplest to implement. It is very similar to teaching a child with the use of flash cards.

Unsupervised Learning- Unsupervised learning is very much the opposite of supervised learning. It features no labels. Instead, our algorithm would be fed a lot of data and given the tools to understand the properties of the data. From there, it can learn to group, cluster, and/or organize the data in a way such that a human (or other intelligent algorithm) can come in and make sense of the newly organized data.