Machine Learning has become a powerful tool in solving complex problems and providing answers, from translation apps to autonomous vehicles. It involves training an algorithm or model to make accurate predictions based on data.
This technology is considered to be extremely thrilling and fascinating, as it endows machines with a capability that is very similar to that of humans. This area of research enables computers to acquire the ability to learn without requiring explicit programming. In today’s post, we will explore different categories of machine learning problems and the associated terminologies commonly used in the field.
Types of machine learning problems
There are various ways to classify machine learning problems. Here, we discuss the most obvious ones.
Supervised learning involves presenting a model or algorithm with example inputs and their corresponding desired outputs, allowing it to discover patterns and connections between the input and output. The objective is to learn a general rule that can map inputs to outputs. Training is continued until the model achieves the desired level of accuracy on the training data. Real-life examples are:
Image Classification: You train with images/labels. Then in the future, you give a new image expecting that the computer will recognize the new object.
Market Prediction/Regression: You train the computer with historical market data and ask the computer to predict the new price in the future.
Unsupervised learning, on the other hand, does not involve any labeled data, leaving the algorithm to discover structure in the input data on its own. This type of learning is used for clustering populations into different groups, and can also be used to uncover hidden patterns in data. Some examples include:
Clustering: You ask the computer to separate similar data into clusters; this is essential in research and science.
High-Dimension Visualization: Use the computer to help us visualize high-dimension data.
Generative Models: After a model captures the probability distribution of your input data, it will be able to generate more data. This can be very useful to make your classifier more robust.
[Source: Geeksforgeeks.org]
As you can see clearly, the data in supervised learning is labeled, whereas data in unsupervised learning is unlabelled.
Terminologies of Machine Learning
Model: A representation learned from data using machine learning algorithms.
Feature: An individual measurable property of data used as input for the model. Feature vectors are used to represent a set of features numerically.
Target/Label: A target variable or the label is the value to be predicted by the model.
Training: Giving a set of inputs and expected outputs to the model, resulting in a hypothesis that can map new data to categories trained on.
Prediction: Using the trained model to provide a predicted output for a set of inputs. Performance on unseen data is important for evaluating the model's effectiveness.
Here are the steps to get started with machine learning:
Define the problem: Determine if machine learning can be used to solve the problem.
Collect data: Gather and clean data to use for model training.
Explore data: Analyze data structure and relationships using statistical methods and visualization.
Pre-process data: Normalize, transform, and clean data as necessary to prepare it for modeling.
Split data: Divide data into training and test datasets for model validation.
Choose model: Select a suitable machine learning model for the problem and data.
Train model: Use training data to train the model, adjusting its parameters for accuracy.
Evaluate model: Use test data to evaluate the model's performance and accuracy.
Fine-tune model: Adjust model parameters based on evaluation results to improve accuracy.
Deploy model: Integrate the model into the application or system for use.
Monitor model: Continuously monitor the model's performance to ensure accuracy over time.
In conclusion, machine learning has rapidly become an essential technology with a wide range of applications across various fields. It’s now widely used in various applications, and its usage is far more prevalent than one might anticipate. By training algorithms or models to make accurate predictions from data, it provides a powerful tool for problem-solving and decision-making. Understanding the different categories of machine learning problems and the associated terminologies is crucial for effectively utilizing this technology.
If you want to learn more in debt about machine learning, visit Geeksforgeeks.org.
Reminder: None of this is financial advice. Do your own research. Don’t trust. Verify.