{"id":7101,"date":"2019-04-30T12:54:55","date_gmt":"2019-04-30T19:54:55","guid":{"rendered":"https:\/\/www.springboard.com\/?p=7101"},"modified":"2023-07-08T02:35:59","modified_gmt":"2023-07-08T09:35:59","slug":"machine-learning-terminology","status":"publish","type":"post","link":"https:\/\/www.springboard.com\/blog\/data-science\/machine-learning-terminology\/","title":{"rendered":"The Most Common Machine Learning Terms, Explained"},"content":{"rendered":"\n

“Machine learning” is one of the current technology buzzwords, often used in parallel with artificial intelligence, deep learning, and big data, but what does it actually mean? And what other machine learning terminology is important to understand?<\/span><\/p>\n\n\n\n

McKinsey has defined machine learning as \u201calgorithms that can learn from data without relying on rules-based programming.\u201d Carnegie Mellon University offers this: \u201cThe field of Machine Learning seeks to answer the question \u2018How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?\u2019\u201d<\/span><\/p>\n\n\n\n

Machine learning means that the accuracy of the system is improving over time, with the addition of more data and feedback. You probably encounter many examples of machine learning every day without realizing it. When Facebook suggests \u201cpeople you might know\u201d or when Amazon emails you recommendations of products you might like based on previous purchases, they are using machine learning algorithms to customize your results.<\/span><\/p>\n\n\n\n

Machine learning deepens the work of artificial intelligence. In AI, researchers originally created rules for computers to make decisions. With machine learning, computers actually \u201clearn\u201d for themselves and design new rules through practice and repetition.<\/span><\/p>\n\n\n\n

\"learning<\/figure>\n\n\n\n

(Source<\/a>.)<\/p>\n\n\n\n

From medical diagnoses to fraud detection, machine learning is improving our capability to solve societal problems. <\/span>Paypal<\/span><\/a> acquired the ML startup Simility to analyze millions of transactions and flag anomalies to prevent money laundering. <\/span>Machine learning<\/span><\/a> can also predict hypoxaemia (low oxygen levels) during surgery; recognize cardiovascular risk factors like high blood pressure, age, and smoking in retinal images; and identify abnormal growths during colonoscopies.<\/span><\/p>\n\n\n\n

Adopting machine learning at scale is not without challenges. Machine learning can lead to overfitting (when the machine predicts outcomes based on historical data, but does not adapt to new variables) and sometimes it is difficult to find large enough data samples for the machines to train with. <\/span><\/p>\n\n\n\n

Although many people worry about ML replacing humans, people and machines complement each other in many ways. According to the <\/span>Harvard Business Review<\/span><\/a>, \u201cthrough\u2026 collaborative intelligence, humans and AI actively enhance each other\u2019s complementary strengths: the leadership, teamwork, creativity, and social skills of the former, and the speed, scalability, and quantitative capabilities of the latter.\u201d <\/span><\/p>\n\n\n\n

Humans can help train and operate machines and explain their behavior. When Microsoft developed the ML bot Cortana, it required substantial data points and human insights to create a personality that was \u201cconfident, caring, and helpful but not bossy.\u201d A poet, a novelist, and a playwright were all part of the team that trained the bot on how to communicate effectively with humans.<\/span><\/p>\n\n\n\n

Machine learning is full of interesting variants and subfields, so let\u2019s start decoding other machine learning terminology.<\/span><\/p>\n\n\n\n

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Machine Learning Terminology<\/h2>\n\n\n\n

Classification<\/h3>\n\n\n\n

Classification is a part of <\/span>supervised learning<\/b> (learning with labeled data) through which data inputs can be easily separated into categories. In machine learning, there can be <\/span>binary classifiers<\/b> with only two outcomes (e.g., spam, non-spam) or <\/span>multi-class classifiers<\/b> (e.g., types of books, animal species, etc.).<\/span><\/p>\n\n\n\n

One of the most popular classification algorithms is a <\/span>decision tree<\/b> (essential for both data scientists<\/a> and machine learning engineers), whereby repeated questions leading to precise classifications can build an \u201cif-then\u201d framework for narrowing down the pool of possibilities over time. <\/span><\/p>\n\n\n\n

You can learn about other classification algorithms, like naive Bayes, k-nearest neighbor, and artificial neural networks, <\/span>here<\/span><\/a>.<\/span><\/p>\n\n\n\n

\"decision<\/figure>\n\n\n\n

(<\/span>Source<\/span><\/a>.)<\/span><\/p>\n\n\n\n

Clustering<\/h3>\n\n\n\n

Clustering is a form of <\/span>unsupervised learning<\/b> (learning with unlabeled data) that involves grouping data points according to features and attributes. <\/span><\/p>\n\n\n\n

Clustering can be used to organize customer demographics and purchasing behavior into specific segments for targeting and product positioning. It can also analyze housing quality and geographic locations to create real estate valuations and plan the layout of new city developments. It can classify information by topics within libraries or web pages and compile an easily accessible directory for users. <\/span><\/p>\n\n\n\n

The most common kind of clustering is <\/span>K-means clustering<\/b>, which involves representing each cluster by a variable \u201ck\u201d and then defining the centroid of those clusters. All data points are then assigned to a particular cluster and, through this process, we identify the centroid of the new clusters. Here are a few <\/span>examples<\/span><\/a> of what K-means clustering looks like in practice:<\/span>
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