How is Machine Learning used?
Zippia reviewed thousands of resumes to understand how machine learning is used in different jobs. Explore the list of common job responsibilities related to machine learning below:
- Designed and implemented a machine learning based fund classification.
- Worked on modeling breast cancer from mammogram images, as well as time series analysis and machine learning for hospital data.
- Design create and manage Big Data Systems that improve healthcare quality through the use of analytics and machine learning.
- Use of machine learning approaches, as well as dynamic modeling for project.
- Research related to clinical decision support algorithms Useful employment of multiple machine learning techniques to achieve common prediction goal
- Implement machine learning algorithms for intelligence video analytics applications.
Are Machine Learning skills in demand?
Yes, machine learning skills are in demand today. Currently, 34,035 job openings list machine learning skills as a requirement. The job descriptions that most frequently include machine learning skills are space sciences director, research staff member, and computational biologist.
How hard is it to learn Machine Learning?
Based on the average complexity level of the jobs that use machine learning the most: space sciences director, research staff member, and computational biologist. The complexity level of these jobs is challenging.
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What jobs can you get with Machine Learning skills?
You can get a job as a space sciences director, research staff member, and computational biologist with machine learning skills. After analyzing resumes and job postings, we identified these as the most common job titles for candidates with machine learning skills.
Space Sciences Director
Job description:
A Space Science Director's primary responsibility is to lead a science department on the right research course. Space Science Director's input is important for the progress of space research, and the future of organizations focusing on aerospace research and atmospheric monitoring stations rests upon their judgment.
- Data Science
- Machine Learning
- Python
- Visualization
- Mathematics
- SQL
Research Staff Member
Job description:
A Research Staff Member is a part of the research team who will assist in the entire research project. The staff member will work on various research stages and assist in collating information from different sources. One of the research staff members' responsibilities is to understand all facets of conducting research, get all government policies regarding the particular topic in research, and ensure that all procedures are legal and ethical. The research staff member is a broad term for a person part of a research team and is often given specific tasks when one is officially part of the team.
- Python
- Machine Learning
- Research Projects
- DOE
- Data Analysis
- Java
Computational Biologist
Job description:
A computational biologist is an expert in computational management, high-performance computing, data algorithm, and biological and statistical processes. You study to understand biological systems using computational theoretical principles. Your duties typically include doing research and computer programming, developing a theory, mathematical models, and computer simulations to organize and analyze your findings, and building databases. To become a successful computational biologist, you must have extensive knowledge of bioinformatics, biochemistry, and computational biology, excellent communication skills, and problem-solving skills.
- Python
- Machine Learning
- Next-Generation Sequencing
- C++
- Visualization
- Biological Data
Computer Scientist
Job description:
A computer scientist is responsible for analyzing network infrastructure and designing efficient computer systems according to clients' specifications and business functions. Computer scientists inspect the safety and security of the organization's database, ensuring its protection from malicious activities and unauthorized access. They also manage a large scale of software applications, identifying technical opportunities to improve optimal performance and navigations. A computer scientist must have excellent knowledge of the technology industry, strong command on programming languages and system codes, and the ability to solve technical complexities for conceptualization.
- Python
- Computer System
- Machine Learning
- Architecture
- DOD
- Java
Image Scientist
Job description:
An imaging scientist specializes in designing and developing imaging systems through extensive image studies. Among their responsibilities include conducting research and analysis, coordinating with staff and other experts, gathering and analyzing data through various scientific procedures, maintaining records, and summarizing findings. They typically develop prototypes and test structure to ensure its imaging quality. Moreover, as an image scientist, it is essential to maintain an active communication line with team members for a smooth and efficient workflow.
- Image Processing
- MATLAB
- Machine Learning
- PET
- Data Analysis
- Remote Sensing
Director Of Quantitative Research
Job description:
A director of quantitative research is a management professional who is responsible for managing a group of experts who are involved in quantitative research. This director must develop data analysis strategies to assess projects while providing reports to their senior management to monitor the effectiveness of their current development activities. The director must conduct a quantitative analysis of the organization's financial data to track and monitor the performance of their operations. This director must also coordinate collaborative data collection efforts across the organization.
- Python
- Machine Learning
- Quantitative Research
- Data Analysis
- MATLAB
- Data Collection
Quantitative Analyst Internship
Job description:
As a quantitative analyst intern, you will work in software development and quantitative analysis. You will interact with different trading groups and work on a wide range of real-world projects and collaborate with developers and other senior staff. Your duties will typically include writing software that needs to be developed, analyzing statistical models for automated financial trading strategy, and identifying new trading ideas by researching financial literature. You will also be responsible for creating computer simulations to test these ideas.
- Python
- R
- Machine Learning
- MATLAB
- C++
- Risk Management
How much can you earn with Machine Learning skills?
You can earn up to $98,256 a year with machine learning skills if you become a space sciences director, the highest-paying job that requires machine learning skills. Research staff members can earn the second-highest salary among jobs that use Python, $110,035 a year.
| Job title | Average salary | Hourly rate |
|---|---|---|
| Space Sciences Director | $98,256 | $47 |
| Research Staff Member | $110,035 | $53 |
| Computational Biologist | $61,449 | $30 |
| Computer Scientist | $103,238 | $50 |
| Image Scientist | $87,934 | $42 |
Companies using Machine Learning in 2025
The top companies that look for employees with machine learning skills are Meta, Intel, and Deloitte. In the millions of job postings we reviewed, these companies mention machine learning skills most frequently.
Departments using Machine Learning
| Department | Average salary |
|---|---|
| Research & Development | $117,802 |
| Engineering | $108,011 |
20 courses for Machine Learning skills
1. Machine Learning DevOps Engineer
Up your game by streamlining the integration of machine-learning models and deploying them to a production-level environment...
2. AWS Machine Learning Engineer
You’ll master the skills necessary to become a successful ML engineer. Learn the data science and machine learning skills required to build and deploy machine learning models in production using Amazon SageMaker...
3. Intro to Machine Learning with PyTorch
Learn foundational machine learning techniques -- from data manipulation to unsupervised and supervised algorithms...
4. Intro to Machine Learning with Tensorflow
Learn foundational machine learning techniques - from data manipulation to unsupervised and supervised algorithms...
5. Machine Learning Engineer for Microsoft Azure
Strengthen your machine learning skills and build practical experience by training, validating, and evaluating models using Azure Machine Learning...
6. Machine Learning
The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications.\n\nThis Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field.\n\nThis 3-course Specialization is an updated version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012.\n\nIt provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.)\n\nBy the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start...
7. Machine Learning
This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data...
8. IBM Machine Learning
Prepare for a career in the field of machine learning. In this program, you’ll learn in-demand skills like AI and Machine Learning to get job-ready in less than 3 months.\n\nMachine Learning is the use and development of computer systems that are able to learn and adapt by using algorithms and statistical models to analyze and draw inferences from patterns in data. Machine Learning is a branch of Artificial Intelligence (AI) where computers are taught to imitate human intelligence in that they solve complex tasks. Roles available to those proficient in Machine Learning include machine learning engineer, NLP scientist, and data engineer.\n\nThis program consists of courses that provide you with a solid theoretical understanding and considerable practice of the main algorithms, uses, and best practices related to Machine Learning. Topics covered include Supervised and Unsupervised learning, Regression, Classification, Clustering, Deep learning and Reinforcement learning.\n\nYou will follow along and code your own projects using some of the most relevant open-source frameworks and libraries, and you will apply what you have learned in various courses by completing a final capstone project.\n\nUpon completion, you’ll have a portfolio of projects and a Professional Certificate from IBM to showcase your expertise. You’ll also earn an IBM Digital badge and will gain access to career resources to help you in your job search, including mock interviews and resume support...
9. Unsupervised Machine Learning
This course introduces you to one of the main types of Machine Learning: Unsupervised Learning. You will learn how to find insights from data sets that do not have a target or labeled variable. You will learn several clustering and dimension reduction algorithms for unsupervised learning as well as how to select the algorithm that best suits your data. The hands-on section of this course focuses on using best practices for unsupervised learning. By the end of this course you should be able to: Explain the kinds of problems suitable for Unsupervised Learning approaches Explain the curse of dimensionality, and how it makes clustering difficult with many features Describe and use common clustering and dimensionality-reduction algorithms Try clustering points where appropriate, compare the performance of per-cluster models Understand metrics relevant for characterizing clusters Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience with Unsupervised Machine Learning techniques in a business setting. What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics...
10. Machine Learning Algorithms
In this course you will: a) understand the naïve Bayesian algorithm. b) understand the Support Vector Machine algorithm. c) understand the Decision Tree algorithm. d) understand the Clustering. Please make sure that you’re comfortable programming in Python and have a basic knowledge of mathematics including matrix multiplications, and conditional probability...
11. Machine Learning: Regression
Case Study - Predicting Housing Prices In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). This is just one of the many places where regression can be applied. Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high-performance computing, to analyzing which regulators are important for gene expression. In this course, you will explore regularized linear regression models for the task of prediction and feature selection. You will be able to handle very large sets of features and select between models of various complexity. You will also analyze the impact of aspects of your data -- such as outliers -- on your selected models and predictions. To fit these models, you will implement optimization algorithms that scale to large datasets. Learning Outcomes: By the end of this course, you will be able to: -Describe the input and output of a regression model. -Compare and contrast bias and variance when modeling data. -Estimate model parameters using optimization algorithms. -Tune parameters with cross validation. -Analyze the performance of the model. -Describe the notion of sparsity and how LASSO leads to sparse solutions. -Deploy methods to select between models. -Exploit the model to form predictions. -Build a regression model to predict prices using a housing dataset. -Implement these techniques in Python...
12. Machine Learning: Complete Maths for Machine Learning
Congratulations if you are reading this. That simply means, you have understood the importance of mathematics to truly understand and learn Data Science and Machine Learning. In this course, we will cover right from the foundations of Algebraic Equations, Linear Algebra, Calculus including Gradient using Single and Double order derivatives, Vectors, Matrices, Probability and much more. Mathematics form the basis of almost all the Machine Learning algorithms. Without maths, there is no Machine Learning. Machine Learning uses mathematical implementation of the algorithms and without understanding the math behind it is like driving a car without knowing what kind of engine powers it. You may have studied all these math topics during school or universities and may want to freshen it up. However, many of these topics, you may have studied in a different context without understanding why you were learning them. They may not have been taught intuitively or though you may know majority of the topics, you can not correlate them with Machine Learning. This course of Math For Machine Learning, aims to bridge that gap. We will get you upto speed in the mathematics required for Machine Learning and Data Science. We will go through all the relevant concepts in great detail, derive various formulas and equations intuitively. This course is divided into following sections, Algebra FoundationsIn this section, we will lay the very foundation of Algebraic Equations including Linear Equations and how to plot them. We will understand what are Exponents, Logs, Polynomial and quadratic equations. Almost all the Machine Learning algorithms use various functions for loss measurement or optimization. We will go through the basics of functions, how to represent them and what are continuous and non-continuous functions. CalculusIt is said that without calculus and differential equations, Machine Learning would have never been possible. The Gradient Descent using derivatives is essence of minimizing errors for a Machine Learning algorithm. We will understand various terms of Rate of Change, Limits, What is Derivative, including Single, Double and Partial Derivatives. I will also explain with an example, how machine learning algorithms use calculus for optimization. Linear AlgebraLinear Algebra is the mathematics of the 21st Century. Every record of data is bound by some form of algebraic equation. However, it's nearly impossible for humans to create such an equation from a dataset of thousands of records. That's where the ability of vectors and matrices to crunch those numerical equations and create meaningful insights in the form of linear equations help us. We will see, right from the foundations of Vectors, Vector Arithmetic, Matrices and various arithmetic operations on them. We will also see, how the vectors and matrices together can be used for various data transformations in Machine Learning and Data Science. ProbabilityProbability plays an important role during classification type of machine learning problems. It is also the most important technique to understand the statistical distribution of the data. Conditional probability also helps in classification of the dependent variable or prediction of a class. With all of that covered, you will start getting every mathematical term that is taught in any of the machine learning and data science class. Mathematics has been my favorite subject since the childhood and you will see my passion in teaching maths as you go through the course. I firmly believe in what Einstein said, If you can not explain it simple enough, You have not understood it enough.. I hope I can live upto this statement. I am super excited to see you inside the class. So hit the ENROLL button and I will see you inside the course. You will truly enjoy Mathematics For Machine Learning......
13. MLOps | Machine Learning Operations
This comprehensive course series is perfect for individuals with programming knowledge such as software developers, data scientists, and researchers. You'll acquire critical MLOps skills, including the use of Python and Rust, utilizing GitHub Copilot to enhance productivity, and leveraging platforms like Amazon SageMaker, Azure ML, and MLflow. You'll also learn how to fine-tune Large Language Models (LLMs) using Hugging Face and understand the deployment of sustainable and efficient binary embedded models in the ONNX format, setting you up for success in the ever-evolving field of MLOps\n\nThrough this series, you will begin to learn skills for various career paths:\n\n1. Data Science - Analyze and interpret complex data sets, develop ML models, implement data management, and drive data-driven decision making.\n\n2. Machine Learning Engineering - Design, build, and deploy ML models and systems to solve real-world problems.\n\n3. Cloud ML Solutions Architect - Leverage cloud platforms like AWS and Azure to architect and manage ML solutions in a scalable, cost-effective manner.\n\n4. Artificial Intelligence (AI) Product Management - Bridge the gap between business, engineering, and data science teams to deliver impactful AI/ML products...
14. Machine Learning for Marketing
Understand the structure and techniques used in Machine Learning, Text Mining, and Decision Science for Marketing. Explore the fascinating world of Machine Learning and its transformative applications in marketing. Explain how analytics and decision science approaches for marketing can enhance the quality of marketing decision-making. Foundation in digital marketing analytics to understand the consumer journey, intent, and activity on your business website...
15. Machine Learning for Trading
This 3-course Specialization from Google Cloud and New York Institute of Finance (NYIF) is for finance professionals, including but not limited to hedge fund traders, analysts, day traders, those involved in investment management or portfolio management, and anyone interested in gaining greater knowledge of how to construct effective trading strategies using Machine Learning (ML) and Python. Alternatively, this program can be for Machine Learning professionals who seek to apply their craft to quantitative trading strategies. By the end of the Specialization, you'll understand how to use the capabilities of Google Cloud to develop and deploy serverless, scalable, deep learning, and reinforcement learning models to create trading strategies that can update and train themselves. As a challenge, you're invited to apply the concepts of Reinforcement Learning to use cases in Trading. This program is intended for those who have an understanding of the foundations of Machine Learning at an intermediate level. To successfully complete the exercises within the program, you should have advanced competency in Python programming and familiarity with pertinent libraries for Machine Learning, such as Scikit-Learn, StatsModels, and Pandas; a solid background in ML and statistics (including regression, classification, and basic statistical concepts) and basic knowledge of financial markets (equities, bonds, derivatives, market structure, and hedging). Experience with SQL is recommended...
16. Mathematics for Machine Learning
For a lot of higher level courses in Machine Learning and Data Science, you find you need to freshen up on the basics in mathematics - stuff you may have studied before in school or university, but which was taught in another context, or not very intuitively, such that you struggle to relate it to how it’s used in Computer Science. This specialization aims to bridge that gap, getting you up to speed in the underlying mathematics, building an intuitive understanding, and relating it to Machine Learning and Data Science.\n\nIn the first course on Linear Algebra we look at what linear algebra is and how it relates to data. Then we look through what vectors and matrices are and how to work with them.\n\nThe second course, Multivariate Calculus, builds on this to look at how to optimize fitting functions to get good fits to data. It starts from introductory calculus and then uses the matrices and vectors from the first course to look at data fitting.\n\nThe third course, Dimensionality Reduction with Principal Component Analysis, uses the mathematics from the first two courses to compress high-dimensional data. This course is of intermediate difficulty and will require Python and numpy knowledge.\n\nAt the end of this specialization you will have gained the prerequisite mathematical knowledge to continue your journey and take more advanced courses in machine learning...
17. Supervised Machine Learning: Regression
This course introduces you to one of the main types of modelling families of supervised Machine Learning: Regression. You will learn how to train regression models to predict continuous outcomes and how to use error metrics to compare across different models. This course also walks you through best practices, including train and test splits, and regularization techniques. By the end of this course you should be able to: Differentiate uses and applications of classification and regression in the context of supervised machine learning Describe and use linear regression models Use a variety of error metrics to compare and select a linear regression model that best suits your data Articulate why regularization may help prevent overfitting Use regularization regressions: Ridge, LASSO, and Elastic net Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience with Supervised Machine Learning Regression techniques in a business setting. What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics...
18. Machine Learning: an overview
The course provides a general overview of the main methods in the machine learning field. Starting from a taxonomy of the different problems that can be solved through machine learning techniques, the course briefly presents some algorithmic solutions, highlighting when they can be successful, but also their limitations. These concepts will be explained through examples and case studies...
19. Supervised Machine Learning: Classification
This course introduces you to one of the main types of modeling families of supervised Machine Learning: Classification. You will learn how to train predictive models to classify categorical outcomes and how to use error metrics to compare across different models. The hands-on section of this course focuses on using best practices for classification, including train and test splits, and handling data sets with unbalanced classes. By the end of this course you should be able to: -Differentiate uses and applications of classification and classification ensembles -Describe and use logistic regression models -Describe and use decision tree and tree-ensemble models -Describe and use other ensemble methods for classification -Use a variety of error metrics to compare and select the classification model that best suits your data -Use oversampling and undersampling as techniques to handle unbalanced classes in a data set Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience with Supervised Machine Learning Classification techniques in a business setting. What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics...
20. Data for Machine Learning
This course is all about data and how it is critical to the success of your applied machine learning model. Completing this course will give learners the skills to: Understand the critical elements of data in the learning, training and operation phases Understand biases and sources of data Implement techniques to improve the generality of your model Explain the consequences of overfitting and identify mitigation measures Implement appropriate test and validation measures. Demonstrate how the accuracy of your model can be improved with thoughtful feature engineering. Explore the impact of the algorithm parameters on model strength To be successful in this course, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode). This is the third course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute...