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Machine Learning is one of the most well-known subcategories of artificial intelligence. At a high level computers learn just as human do through 1. ingesting 2. learning 3. categorizing and 4. taking action. For example computer image recognition, similar to human vision, is a specific type of Machine Learning that uses Deep Learning. Deep Learning removes human computation from the loop. When a computer is shown a picture of a car, it may not know how to initially makes sense of the data. Over time, with more images of cars, the computer is able to accurately label the images.

Photo credit: Prowess Consulting

Machine Learning is one of the most well-known subcategories of artificial intelligence. At a high level computers learn just as human do through 1. ingesting 2. learning 3. categorizing and 4. taking action. For example computer image recognition, similar to human vision, is a specific type of Machine Learning that uses Deep Learning. Deep Learning removes human computation from the loop. When a computer is shown a picture of a car, it may not know how to initially makes sense of the data. Over time, with more images of cars, the computer is able to accurately label the images.

Photo Credit: XenonStack

The feature extraction and classification seen in the image above is known as a Neural Network. An artificial neural network is an interconnected group of data points, similar to the network of neurons in a brain.

Using statistical conclusions to find patterns in data is known as Data Science. Statistical Machine Learning uses the same math as Data Science, but integrates it into algorithms that get better on their own. Most AI actions are initiated through Algorithms, procedures or formulas for solving problems, based on conducting a sequence of specified actions. There are numerous algorithms used by Data Scientists. We’ll dive in deeper to each of them in a future article, but here is a chart of their frequency of use:

Photo Credit: KD Nuggets

Decision Tree algorithms composed of hard if-then rules were the initial tools used for Natural Language Processing (NLP). NLP overlaps with Machine Learning under the artificial intelligence umbrella, but focuses on associative connections between written or spoken languages. Probabilistic statistics are now the primary algorithms used for NLP as they allow more room for creativity in associating similar words.

Finally it’s important to understand the difference between supervised and unsupervised learning. Supervised Learning uses data with known labels to create models then makes predictions based on the input data. Unsupervised Learning works of unlabeled data to differentiate the given input data.

Photo Credit: Leonardo Araujo dos Santos

As you likely recognized, the key to success in AI is having a large amount of data as your foundation. From there you can work with experts like our team at ELE.ai to recommend the best approach to leverage that data to solve current pain points or generate new lines of business. Get in touch today by email us at Info@ELE.ai.