7 Key Terms Every Machine Learning Beginner Should Know

If you’re new to machine learning, understanding basic terms is crucial. Knowing key terms can help you understand the basics better. Here are 7 essential terms every beginner should know. These terms will give you a solid foundation to build your machine learning knowledge.

1. Algorithm

An algorithm is a set of rules a computer uses to solve a problem. It finds patterns in data and makes predictions.

There are several types of algorithms in machine learning:

1. Supervised Learning: Learn from labelled examples to predict or classify new data.
2. Unsupervised Learning: Discover patterns in data without labels.
3. Reinforcement Learning: Make decisions by taking actions in an environment

2. Model

A model is created by training an algorithm with data. It finds the patterns and relationships found in the data. This lets the model predict new data.

For example:

• Linear Regression Model: Predicts values by fitting a line to the data.
• Decision Tree Model: Makes predictions by splitting data into groups based on features.
• Support Vector Machine (SVM) Model: Finds the best boundary to separate different categories.

3. Features

Features are input data used to make predictions. They are measurable properties or characteristics of the data. They can be numerical or categorical.
For example, consider a model that predicts house prices. Features could be the size, location, and age of the house. Each feature helps the model understand how these aspects influence the price.

4. Labels

Labels are the outcomes that a machine learning model tries to predict. Each set of features is paired with a label in supervised learning. Similar to features, they can be numerical or categorical.
Consider a model that classifies emails as “spam” or “not spam”. The label is either “spam” or “not spam.” The model learns patterns from these features to predict the label for new emails.

5. Overfitting

Overfitting happens when a machine learning model learns the training data too well, including noise and outliers. This makes the model perform well on training data but poorly on new data. This occurs because the model is too complex and memorizes the training data rather than generalizes it. To prevent overfitting, techniques like cross-validation, pruning, and regularization are used.

6. Underfitting

Underfitting happens when a machine learning model is too simple to understand the data patterns. As a result, it performs poorly on both training data and new data. This usually occurs if the model lacks complexity or hasn’t been trained long enough. Increase the model’s complexity or add more features to fit underfitting.

7. Hyperparameters

Hyperparameters are settings that guide the learning process and the model’s structure. They are chosen before training starts. In contrast, parameters are learned from the data during training,

Common hyperparameters include:

• Learning Rate: Controls how much the model’s weights are updated during each training step.
• Number of Hidden Layers: Specifies the number of layers between the input and output layers in the network.
• Batch Size: Defines how many training examples are used in each iteration.
• Number of Epochs: Determines how many times the entire training dataset is passed through the model.

Conclusion

Understanding these key terms is crucial for starting in machine learning. They form the foundation of your learning journey. Remember these terms as you learn more advanced concepts.