Machine learning algorithms, models, strategies, and other influential features are assisting us in unlocking a wide range of applications. These computer systems are capable of self-learning and making business decisions, as well as assisting research and improving technology.
As machine learning finds new applications across various sectors, the demand for professionals in the field is growing. According to the US Bureau of Labor Statistics, the job outlook will rise 22 percent until 2030 for computer and information research scientists.
Whichever area of machine learning interests you more, you must first familiarize yourself with machine learning terminology. This article will provide you with a list of machine learning terms and a machine learning terminology cheat sheet for the advanced terms you must know.
What Is Machine Learning?
Machine learning is a branch of computer science in which machines can learn a wide variety of concepts by analyzing samples of data, finding patterns, and constructing models. They learn from experience, just like humans, without using a predetermined equation. For example, machine learning algorithms teach cars to drive themselves.
Who Uses Machine Learning Terminology?
Machine learning terminology is used by professionals such as machine learning engineers, data scientists, or artificial intelligence specialists. Machine learning is also appealing to business decision-makers and forecasters because it may assist them in solving complicated problems involving several variables, complex decisions, and large data sets.
List of Machine Learning Terms: Things Every Machine Learning Engineer Should Know
- Absolute errors
- Bias
- Categorical variable
- Classification model
- Clustering
- Cost function
- Continuous variable
- Dependent variable
- Feature selection
- Input layer
- Learning rate
- Loss function
- Natural language processing
- Neural networks
- Overfitting
- Pattern recognition
- Regression
- Supervised learning
- Training models
- Transfer learning
Glossary of Machine Learning Terminology: 5 Common Machine Learning Terms
You will frequently encounter common terminologies for the basic machine learning concepts you should know as a beginner. Below are five common machine learning terms for you to learn or review.
Bias
In machine learning, bias is an error or fault that occurs when the machine learning model doesn’t match the training set closely. This means that some data set elements have a higher weighting, representation, or both. When it comes to achieving good outcomes, bias reduces accuracy and causes analytical errors.
Why Machine Learning Engineers Need to Know About Biases
Machine learning engineers design machine learning models for various applications. If there is human bias in the training data set, this will cause the model to also be biased. In such a case, the sample data is incomplete and inconsistent.
Classification
Classification is a type of predictive modeling that pairs discrete output variables called labels or categories with input variables. When you enter data, the model assigns a label to help you categorize it. In a classification model, algorithms such as logistic regression, Naive Bayes, k-nearest neighbors, and support vector machines are utilized.
Why Machine Learning Engineers Need to Know About Classification
Machine learning engineers need to know about various classification techniques to help organize their data sets. This way, they are able to maintain enormous amounts of data and make it accessible and meaningful.
Clustering
Clustering is a machine learning unsupervised learning strategy that divides unlabeled data into multiple classes or clusters. The goal is to group data points that are as similar to each other as possible while remaining as distinct as possible from the points in the other groups. To identify more within-cluster similarities and lower inter-cluster similarities in the data set, clustering is used.
Why Machine Learning Engineers Need to Know About Clustering
In the future, machine learning engineers will need to understand clustering to locate features for categorization. Clustering can lead to the discovery of previously unknown subclasses. Clustering rather than labeling a vast quantity of data saves money as well.
Dependent Variable
A dependent variable will shift as a function changes. A machine learning algorithm is trained using variables with known values. Supervised learning algorithms utilize a model to determine the output variables for other data sets. On the other hand, an independent variable will take its own value without relying on any other output variables like categorical variables.
Why Machine Learning Engineers Need to Know About Dependent Variables
Engineers who deal with machine learning look at dependent variables to understand how they affect results. This helps them choose the best approach and elements for experiments and models.
Regression
In machine learning, logistic regression analysis is used for predictive modeling and supervised learning. It determines the most appropriate mapping function from input variables to a continuous output, such as integers and real values. One of the most simple and commonly used regression techniques is linear regression.
Why Machine Learning Engineers Need to Know About Regression
Machine learning engineers need to know about regression algorithms to understand relationships between variables. A linear model predicts continuous responses like variations in temperatures. Numeral data and linear numbers are used in regression analysis to help businesses make decisions based on clearly interpreted data.
Machine Learning Terminology Cheat Sheet: 5 Advanced Machine Learning Terms
Machine learning is advancing and finding applications in various industries. However, some concepts and topics are more complex than others. Below you will find a machine learning terminology cheat sheet with five advanced machine learning terms that engineers who work in this field should know.
Neural Networks
Neural networks are the essential concepts and powerful mechanisms in deep learning algorithms, a popularly growing subfield of machine learning. Also called artificial or simulated neural networks, they are designed to replicate human brain activity, and can assist computers in solving a variety of problems.
Why Machine Learning Engineers Should Know About Neural Networks
Machine learning engineers work with a variety of networks such as recurrent, convolutional, or feedforward neural networks. These networks process data for classification and clustering at a very high speed making it possible to handle great volumes of data.
Overfitting
Overfitting is a concept related to the quality of sample data for training models when algorithms start memorizing instead of learning. When more sample data is needed, the algorithm starts aligning with random data points. As a result, the relevance of information is compromised, making it difficult to make predictions.
Why Machine Learning Engineers Should Know About Overfitting
Machine learning engineers overseeing artificial intelligence systems must be careful of overfitting. Overfitting enhances model complexity and affects a system’s performance by increasing noise in data.
Pattern Recognition
Pattern recognition is finding patterns and regularities in data by the application of machine learning algorithms. With the help of statistical information and data representation, the algorithms sort, categorize, and find missing labels and data points. A data set and system are trained to recognize patterns.
Why Machine Learning Engineers Should Know About Pattern Recognition
Machine learning engineers use pattern recognition as classifiers and automation tools. Pattern recognition techniques have applications in image classification, robotics, face and biometric detection, and medical imaging. This helps engineers in modeling concepts, objects, and systems.
Training Models
A training model includes the data sample used to train machine learning algorithms. The quality of this data set empowers the models. It is also used in models which involve running sample data to acquire an output or response variable. This is used to understand the influence on a possible output and to modify the model.
Why Machine Learning Engineers Should Know About Training Models
Machine learning engineers work with training models to train, evaluate, and fine-tune machine learning models. They work extensively with training models, which are the foundation of developing machine learning algorithms. Skilled professionals are familiar with model training and avoid the overfitting of data.
Supervised Learning
Supervised learning algorithms are used to develop models with the help of known input and output data. The model is trained to respond and make predictions for a new set of data. Regression techniques, classifications, and forecasting are examples of supervised learning where a system is trained through examples. One key means of classification in supervised learning is a decision tree.
Why Machine Learning Engineers Should Know About Supervised Learning
Machine learning engineers use supervised learning techniques for computational calculations, data collection, and performance optimization. They fit models through validation methods to make useful predictions in various fields.
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How Can I Learn Machine Learning Terminology in 2022?
You can learn machine learning concepts and terminology by taking a machine learning bootcamp. Bootcamps help students gain a comprehensive knowledge of machine learning concepts and disciplines and allow them to practice essential skills in short intensive programs.
For example, you can brush up on probability distributions and matrices utilized in machine learning approaches and focus on the key features of a Jacobian matrix or a Hessian matrix.
You can also learn a lot from online resources like machine learning blogs or online machine learning courses. You can also test your knowledge with interview-prep applications like MLExpert and keep your learning journey exciting through YouTube videos.
Machine Learning FAQ
Python is the popular programming language among machine learning experts because of its flexible coding style, platform independence, and numerous libraries and packages. Code readability and straightforward techniques of code manipulation allow machine learning engineers to easily work on complex problems, such as those related to biological systems.
Machine learning and AI are not the same. Machine learning is the idea and process where machines learn and adapt to act like humans. It includes neural networks inverse reinforcement learning. Artificial intelligence is a broader field encompassing technologies that enable machine learning and data science.
Machine learning is a key feature applied to search engines, speech recognition, self-driving cars, online advertising and product recommendations, bioinformatics, medical diagnostics, fraud detections, automated stock trading, and financial market analysis.
In machine learning, you will encounter supervised, unsupervised, semi-supervised, and reinforcement learning. In supervised learning, all training data have labels, and unsupervised learning doesn’t have any labels. Semi-supervised learning has some data labels, and reinforcement learning is the training of models through rewarding and punishing actions.
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