10 Machine Learning Terms Explained in Simple English

@machinelearnbot

If you're relatively new to Machine Learning and it's applications, you'll more than likely have come across some pretty technical terms that are often difficult for the novice mathematician/scientist to get their head around. Following on from a previous blog, (10 Common NLP Terms Explained for the Text Analysis Novice), we decided to put together a list of 10 Machine Learning terms which have been broken down in simple English, making them easier to understand. So, if you're struggling to understand the difference between Supervised and Un-supervised Learning you'll enjoy this post. A subfield of computer science and artificial intelligence (AI) that focuses on the design of systems that can learn from and make decisions and predictions based on data. Machine learning enables computers to act and make data-driven decisions rather than being explicitly programmed to carry out a certain task.


Unearthing the Layers of Machine Learning

#artificialintelligence

Data is the pathway upon which the future is being built. Data is a language spoken everywhere, and being able to translate and transform data is becoming an increasingly pivotal skill in all areas. Data is used for a myriad of things from predicting stock prices to recognizing that a tumour is malignant. The power of data has grown immensely, and algorithms are now a companies strongest asset. In order to take advantage of this data, we must learn how to harness and understand it.


10 Machine Learning Terms Explained in Simple English

@machinelearnbot

If you're relatively new to Machine Learning and it's applications, you'll more than likely have come across some pretty technical terms that are often difficult for the novice mathematician/scientist to get their head around. Following on from a previous blog, (10 Common NLP Terms Explained for the Text Analysis Novice), we decided to put together a list of 10 Machine Learning terms which have been broken down in simple English, making them easier to understand. So, if you're struggling to understand the difference between Supervised and Un-supervised Learning you'll enjoy this post. A subfield of computer science and artificial intelligence (AI) that focuses on the design of systems that can learn from and make decisions and predictions based on data. Machine learning enables computers to act and make data-driven decisions rather than being explicitly programmed to carry out a certain task.


Neural network? Machine Learning? Here's all you need to know about AI

#artificialintelligence

One method of AI is machine learning – programs that perform better over time and with more data input. Deep learning is among the most promising approaches to machine learning. It uses algorithms based on neural networks – a way to connect inputs and outputs based on a model of how we think the brain works – that find the best way to solve problems by themselves, as opposed to by the programmer or scientist writing them. Training is how deep learning applications are "programmed" – feeding them more input and tuning them. Inference is how they run, to perform analysis or make decisions.