Introduction
Machine learning is a powerful tool that can greatly help in the development of business applications. Each machine learning algorithm has its own characteristics that make it suitable for a particular use case or scenario, but there are many paths to competency when it comes to getting started with ML. In this post we’ll explore the key steps for going from curiosity to competency in machine learning.
Machine Learning is a powerful tool that can greatly help in the development of business applications.
Machine learning is a powerful tool that can greatly help in the development of business applications. It is a subset of artificial intelligence and is used to solve problems that are too complex for people to solve manually.
Each machine learning algorithm has its own characteristics that make it suitable for a particular use case or scenario.
Each machine learning algorithm has its own characteristics that make it suitable for a particular use case or scenario. Some algorithms are better suited to certain types of data, while others are better at finding patterns in data. Some algorithms can handle high volumes of data, while others are better at handling low volumes.
Some common machine learning algorithms include:
- Decision Trees – This algorithm uses tree-like structures to predict outcomes based on previous examples. Decision trees allow you to make predictions about new observations by examining their similarities with previous observations (i.e., how much does this new observation resemble existing ones?).
- Linear Regression – Linear regression is used for modeling relationships between variables (e.g., how does temperature affect sales?). This type of model uses weighted averages from observed values as predictors of future values from those same variables
Unsupervised learning is a type of machine learning where models are built on unlabeled data and are used to find structure in unlabeled data.
Unsupervised learning is a type of machine learning where models are built on unlabeled data and are used to find structure in unlabeled data. Unsupervised learning can be used to discover patterns in data without the use of labels or human intervention, which makes it an ideal tool for exploratory analysis.
Unsupervised learning is often contrasted with supervised learning, where a model learns from labeled examples (i.e., training sets) provided by humans instead of discovering patterns on its own through unsupervised methods.
Anomaly detection is used to detect deviations from the ordinary, expected behavior.
Anomaly detection is used to detect deviations from the ordinary, expected behavior. It can be used to detect fraud, predict failure, or monitor performance. In addition to these more traditional applications, anomaly detection has become an important tool for predicting future events such as stock prices and disease outbreaks.
Anomaly detection algorithms are often categorized by their use of historical data: supervised learning and unsupervised learning. Supervised learning requires extensive labeling of the training set with known examples so that it can learn what normal looks like and then identify anomalies when they occur in new data (unlabeled). Unsupervised algorithms don’t require any labels; instead they find patterns within their input data through clustering or grouping similar items together (clustering) or finding outliers based on some measure such as distance from other points in space (outlier).
Supervised Learning algorithms take a set of labeled data and use it to build predictive models for future data.
Supervised learning algorithms take a set of labeled data and use it to build predictive models for future data. The best way to understand how they work is by example, so let’s look at how supervised learning works with a simple regression problem.
In this scenario we have some data points that represent our car model’s weight and its top speed (in miles per hour). Our goal is to use these two features as inputs into an algorithm that can predict the car’s top speed given its weight. To do this we need three things: 1) A training set of labeled examples; 2) An appropriate algorithm; 3) Some parameters that control the behavior of our algorithm.
Let’s start by loading some libraries and defining some helper functions:
The key steps for going from curiosity to competency in machine learning are to evaluate your requirements, determine where ML fits into your workflow, choose an algorithm, implement and test your model, then deploy
In order to go from curiosity to competency in machine learning, there are several key steps. First, evaluate your requirements and determine where ML fits into your workflow. Then choose an algorithm that suits those needs and implement it in code. Once you have a working model, test it by measuring its performance on known datasets (or even better: creating new datasets for this purpose). Finally, deploy the best performing models into production so that they can be used by others in their decision making processes
Conclusion
The key steps for going from curiosity to competency in machine learning are to evaluate your requirements, determine where ML fits into your workflow, choose an algorithm, implement and test your model, then deploy. It can be a daunting process at first, but once you get started there’s no stopping!
