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Machine Learning - The Hitchhiker's Guide to Python

#artificialintelligence

Machine learning is undoubtedly on the rise, slowly climbing into'buzzword' territory. This is in large part due to misuse and simple misunderstanding of the topics that come with the term. Take a quick glance at the chart below and you'll see this illustrated quite clearly thanks to Google Trends' analysis of interest in the term over the last few years. However, the goal of this article is not to simply reflect on the popularity of machine learning. It is rather to explain and implement relevant machine learning algorithms in a clear and concise way.


Machine Learning For Grannies

#artificialintelligence

She just finished over-feeding you with her delicious food, and now wants you to fix her Skype account. "It's not working" she complains. Turns out she somehow managed to get the trojan horse virus. You restore her computer, create a new Skype account and everything is fine. "Tell me what you're doing in school!" she asks.


Exploring the different types of Machine Learning Algorithms

#artificialintelligence

Machine Learning algorithms are being used more often than we can imagine and there is a good reason for that. Let's see what kind of different Machine Learning algorithms exist and how they can help us in solving everyday life problems. Many different Machine Learning algorithms are widely used in many areas of our life and they help us to solve some everyday problems. Algorithms can help us not only to recognize images, videos, and texts, but are also used to fortify cyber security, improve medical solutions, customer service, and marketing. Supervised learning is the type of ML algorithms that presupposes both input and output data is initially provided.


Modern Machine Learning Algorithms: Strengths and Weaknesses

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In this guide, we'll take a practical, concise tour through modern machine learning algorithms. While other such lists exist, they don't really explain the practical tradeoffs of each algorithm, which we hope to do here. We'll discuss the advantages and disadvantages of each algorithm based on our experience. Categorizing machine learning algorithms is tricky, and there are several reasonable approaches; they can be grouped into generative/discriminative, parametric/non-parametric, supervised/unsupervised, and so on. However, from our experience, this isn't always the most practical way to group algorithms.


The Hitchhiker's Guide to Machine Learning in Python

#artificialintelligence

Machine learning is undoubtedly on the rise, slowly climbing into'buzzword' territory. This is in large part due to misuse and simple misunderstanding of the topics that come with the term. Take a quick glance at the chart below and you'll see this illustrated quite clearly thanks to Google Trends' analysis of interest in the term over the last few years. However, the goal of this article is not to simply reflect on the popularity of machine learning. It is rather to explain and implement relevant machine learning algorithms in a clear and concise way.