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Table Of Contents
- Introduction
- Most Frequently Terms Used In Machine Learning
- Summary
- References
Introduction
In this post, we discuss the Most Frequently Terms Used In Machine Learning. As machine-learning engineers, we have to know some basic terms used in machine learning.
Most Frequently Terms Used In Machine Learning
In machine learning, there are some frequently used terms that are important for us to understand. Here are some of the most common terms that are used in machine learning:
Algorithm
- A set of rules and procedures followed by a computer to solve a specific problem or perform a task.
- There are many algorithms used in machine learning, here are some of them
- KNN
- Linear Regression
- Logistic Regression
- etc
Model
- A representation or approximation of a real-world process, system, or phenomenon used for prediction, inference, or decision-making.
- A mathematical representation of a system that can be used to make predictions or decisions.
- Here are some examples of models in machine learning
- car price prediction model
- stock price prediction model
- etc
Training data
- The Training data is used to train a machine-learning model. It consists of input features and corresponding output labels or target values.
- Using a labeled dataset to “train” a machine learning model to make accurate predictions or decisions.
- From the below URL, you can find some examples of datasets
- https://www.kaggle.com/datasets
Test data
- The Test data is a set of data used to evaluate the performance of a trained machine-learning model.
- After creating a machine learning model we have to evaluate it for that we use a test data set for that.
Feature
- In machine learning, features are part of our dataset that are used as input variables to train a model.
- A measurable property or characteristic of a data sample that is used as an input to a machine learning model.
Label
- The output or target value is associated with a particular set of input features in a supervised learning problem. It represents the desired prediction or outcome.
Classification
- A type of supervised learning problem that aims to assign input data to a set of predefined classes or categories.
Regression
- A type of supervised learning problem where the goal is to predict a continuous numerical value based on input features.
Neural network
- Neural network: A type of machine learning model inspired by the structure and function of the human brain. It consists of interconnected artificial neurons that process and transmit information.
Deep learning
- A subfield of machine learning that involves training artificial neural networks with multiple layers to learn representations of data.
Overfitting
- A phenomenon where a machine learning model performs well on the training data but fails to generalize to new, unseen data due to memorizing noise or irrelevant patterns.
- when a model is trained too well on the training data and performs poorly on new data.
Underfitting
- we can say that the model is Underfitting where a machine learning model is too simple or has not been trained enough to capture the underlying patterns in the data, resulting in poor performance.
- There are a number of reasons why a model might underfit, including:
- Insufficient data: If the model is not trained on enough data, it may not be able to learn the underlying relationships in the data.
- Too simple of a model: If the model is too simple, it may not be able to capture the complexity of the data.
- Noise in the data: If the data contains noise, it can make it difficult for the model to learn the underlying relationships.
Cross-validation
- A technique used to evaluate the performance of a machine learning model by splitting the data into multiple subsets and iteratively training and testing the model on different combinations.
Feature engineering
- The process of selecting, transforming, or creating input features to improve the performance and effectiveness of a machine learning model.
Gradient descent
- An optimization algorithm is used to find the values of the parameters of a model that minimize a loss function.
Loss function
- A loss function is a function that measures the difference between the predicted output of a machine learning model and the actual output. The loss function is used to train the model by adjusting the model’s parameters so that the loss is minimized.
Regularization
- Techniques used to prevent overfitting by adding a penalty term to the loss function.
- Regularization is a technique used in machine learning to prevent overfitting.
- Overfitting occurs when a model learns the training data too well and is unable to generalize to new data.
- Regularization adds a penalty to the loss function that is proportional to the complexity of the model. This penalty encourages the model to be simpler and less likely to overfit
Hyperparameter
- Parameters that are not learned from data, but are set before training a model, examples are the learning rate and the number of hidden layers in a neural network.
Batch
- In machine learning, a batch is a set of data points that are processed together during training. The batch size is the number of data points in a batch. The batch size is a hyperparameter that can affect the training time and accuracy of a machine-learning model.
Epoch
- In machine learning, an epoch is one complete pass through the entire training dataset. This means that each data point in the dataset is used to train the model once. The number of epochs is a hyperparameter that can be tuned to improve the accuracy of the model.
Iteration
- In machine learning, iteration is the process of repeating a process until a desired outcome is achieved.
- In the context of machine learning, iteration is typically used to train a model. The model is trained on a dataset, and then the model is evaluated on a test dataset.
- If the model does not perform well on the test dataset, then the model is iterated. This means that the model is trained on the dataset again, using the feedback from the previous iteration. The process is repeated until the model performs well on the test dataset.
Summary
This is not a full list, and there are many more terms used in machine learning, but these are some of the most fundamental and commonly used ones.
If you want to learn more about machine learning and artificial intelligence then read our articles,