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A Beginner’s Guide to Machine Learning: What Aspiring Data Scientists Should Know

A Beginner's Guide to Machine Learning

Before choosing a machine learning algorithm, it's important to know their characteristics to generate desired outputs and build smart systems.

Data science is growing super fast. As the demand for AI-enabled solutions is increasing, delivering smarter systems for industries has become essential. And the correctness and efficiency through machine learning operations must be fulfilled to ensure the developed solutions complete all demands. Hence, applying machine learning algorithms on the given dataset to produce righteous results and train the intelligent system is one of the most essential steps from the entire process.

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Anomaly Detection Using the Bag-of-Words Model

I am going to show in detail one use case of unsupervised learning: behavioral-based anomaly detection. Imagine you are collecting daily activity from people. In this example, there are six people (S1-S6). When all the data are sorted and pre-processed, the result may look like this list:

  • S1 = eat, read book, ride bicycle, eat, play computer games, write homework, read book, eat, brush teeth, sleep
  • S2 = read book, eat, walk, eat, play tennis, go shopping, eat snack, write homework, eat, brush teeth, sleep
  • S3 = wake up, walk, eat, sleep, read book, eat, write homework, wash bicycle, eat, listen music, brush teeth, sleep
  • S4 = eat, ride bicycle, read book, eat, play piano, write homework, eat, exercise, sleep
  • S5 = wake up, eat, walk, read book, eat, write homework, watch television, eat, dance, brush teeth, sleep
  • S6 = eat, hang out, date girl, skating, use mother's CC, steal clothes, talk, cheating on taxes, fighting, sleep

S1 is the set of the daily activity of the first person, S2 of the second, and so on. If you look at this list, then you can pretty easily recognize that activity of S6 is somehow different from the others. That's because there are only six people. What if there were six thousand? Or six million? Unfortunately, there is no way you could recognize the anomalies. But machines can. Once a machine can solve a problem on a small scale, it can usually handle the large scale relatively easily. Therefore, the goal here is to build an unsupervised learning model that will identify S6 as an anomaly.

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Primary Methods of Approaching Unsupervised Learning

In this article, we’ve outlined the core clustering and anomaly detection methods that are used to set up an unsupervised machine learning algorithm.

There are a variety of ways to create a new machine learning model. Supervised learning is the simplest of these learning processes, but it requires human input and curated data sets. For a supervised learning process, you classify data with labels, then build a machine learning (ML) model around it. This ML model can then be used to classify new data in real time.

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Intro to Machine Learning for Developers

Welcome to the world of machine learning with scikit-learn. Machine learning can be overwhelming at times, and this is partly due to a large number of tools that are available on the market. This post will simplify this process of tool selection down to one — scikit-learn.

In this series, you will learn how to construct an end-to-end machine learning pipeline using some of the most popular algorithms that are widely used in industry and professional competitions, such as Kaggle.