Machine learning allows computers to continuously improve accuracy and learn from data without being programmed to do so. Machine learning algorithms enable computers/machines to look for and recognize insights when viewing new data sets.
Machine learning is used for everything from quickly calculating complicated math equations to recommending content to users on platforms such as Netflix. Machine learning is also used as a powerful tool for fraud protection. In this article, we’ll discuss how the machine learning process works and common applications.
Machine learning: a 7-step process
The typical machine learning process consists of the following steps:
- Gather data: Relevant data is collected, where higher levels of quality and quantity of data typically provide a better data set to build a model.
- Prepare data: The data is prepared and normalized, and bias and errors are removed. Data visualizations can help see if the correct data has been collected, if something is missing, or if patterns are detected.
- Select a model: The right model is chosen according to the desired business outcome. You should keep in mind the amount of preparation required for each model and its scalability. Common machine learning model algorithms include linear regression, logistic regression, decision tree, and SVM algorithms.
- Train the model: Training the model involves using the training data to improve the model’s predictions. Each cycle of updating the predictions is its own training step.
- Evaluate and refine the model: Testing the model is when the machine learning is evaluated against a control data set to gauge performance. The goal is to see how the model will work in the real world.
- Parameter Tuning: Once the evaluation is complete, the original parameters are tested to optimize the AI. Additional training cycles can help make the results more accurate.
- Application and prediction: You’ll apply your machine learning model to real-life scenarios and use it to infer results.
How machine learning works
Machine learning relies on two techniques: supervised and unsupervised learning.
- Supervised learning: Supervised learning allows you to produce or collect data from an earlier machine learning model. The computer is given a set of labeled data points.
- Unsupervised learning: Unsupervised learning works to find new patterns in data. Here, the computer uses unlabeled data for tasks like clustering and dimensionality reduction. As a computer can process millions of data points, this machine learning method can sift through data to find the crucial correlations that can impact your business or application.
Benefits of machine learning in business
Machine learning can extract insights from raw data to solve complex business problems quickly. This helps improve business scalability and improve business systems over time. For example, machine learning can be used to predict customer behavior and purchasing patterns.
How machine learning is commonly used
Typical applications of machine learning include:
- Chatbots
- Insurance risk assessments
- Reducing manual workloads
- Recommending products
- Fraud detection
- Data entry
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Machine learning questions and answers
What is overfitting?
Overfitting is when a model learns the data set too well, which affects the model’s ability to generalize.
How is missing data handled in a data set?
Missing data is handled by dropping the rows/columns or replacing them entirely with a different value.
What is deep learning?
Deep learning is a part of machine learning involving systems that use artificial neural networks to replicate human thinking and learning.
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