Articles

Why Python Is Best for Machine Learning

,

Today, most companies are using Python for AI and Machine Learning. With predictive analytics and pattern recognition becoming more popular than ever, Python development services are a priority for high-scale enterprises and startups. Python developers are in high-demand — mostly because of what can be achieved with the language. AI programming languages need to be powerful, scalable, and readable. Python code delivers on all three.

While there are other technology stacks available for AI-based projects, Python has turned out to be the best programming language for this purpose. It offers great libraries and frameworks for AI and Machine Learning (ML), as well as computational capabilities, statistical calculations, scientific computing, and much more. 

Source de l’article sur DZONE

/par

Machine Learning and Pattern Recognition

,

Introduction

In very simple language, Pattern Recognition is a type of problem while Machine Learning is a type of solution. Pattern recognition is closely related to Artificial Intelligence and Machine Learning. Pattern Recognition is an engineering application of Machine Learning. Machine Learning deals with the construction and study of systems that can learn from data, rather than follow only explicitly programmed instructions whereas Pattern recognition is the recognition of patterns and regularities in data.

  1. Machine Learning

The goal of Machine Learning is never to make "perfect" guesses because Machine Learning deals in domains where there is no such thing. The goal is to make guesses that are good enough to be useful. Machine Learning is a method of data analysis that automates analytical model building. Machine Learning is a field that uses algorithms to learn from data and make predictions. A Machine Learning algorithm then takes these examples and produces a program that does the job. Machine Learning builds heavily on statistics. For example, when we train our machine to learn, we have to give it a statistically significant random sample as training data. If the training set is not random, we run the risk of the Machine Learning patterns that aren’t actually there.

Source de l’article sur DZONE

/0 Commentaires/par