- March 17, 2020
- Posted by: SouTech Team
- Category: Blog
Supervised machine learning is a subset of machine learning, which is a discipline within the field of artificial intelligence. This picture puts the contents of this course into context:
In the field of artificial intelligence (AI), we build models that represent and solve a particular problem. Machine learning semi-automates the creation of these models for particular AI tasks. For example, you can automate the building of a model that forecasts the weather.
Supervised learning uses examples of the thing you are trying to predict like pictures of rabbits to build a model that can find other pictures of rabbits.
Unsupervised learning builds models without examples to discover structure within data.
We will focus on two types of supervised learning tasks: classification, for predicting a category, and regression, for predicting a continuous numeric feature.
Here are some real-world classification problems:
Filtering out spam emails.
Determining which patients suffer from a particular disease.
Recognizing hand-written letters and numbers.
Here are some real-world regression problems:
Estimating property prices.
Forecasting stock prices.
Predicting sales for a retail store.
Some multiple algorithms and techniques can be used within each approach, and through skill, experimentation, and lots of practice, the data scientist can craft useful machine learning models.
Your models will be built using algorithms, which are computational techniques or rules used to solve a problem in a particular way. An algorithm is like a recipe that provides a finite list of instructions to perform a task. You will also need a dataset to build models, which is a sample of data. Generally, the rows contain data points (or sample points), which are the individual examples in your data. The columns represent features, which are the characteristics of the data points.
Below is a case study of how machine learning can be applied to search engine optimization- SEO
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