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What careers use AI/Machine Learning?
I'm interested in getting a career in AI. Which jobs work with it regularly and what education should I get to get there?
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Praveen’s Answer
A career in AI presents thrilling possibilities across various sectors. Here are some job roles that often involve AI:
AI Engineer/Developer: These professionals concentrate on creating and applying AI models and algorithms. They tackle tasks like natural language processing, machine learning, deep learning, and computer vision.
Data Scientist: Data scientists employ AI methods to examine and derive insights from vast datasets. They utilize statistical models, machine learning algorithms, and data visualization techniques to address intricate problems and make data-informed decisions.
Machine Learning Engineer: These engineers construct and deploy machine learning models. They handle data preprocessing, feature engineering, model training, and optimization to establish robust and scalable ML systems.
AI Research Scientist: These scientists carry out comprehensive research on AI algorithms and techniques. They contribute to the advancement of groundbreaking AI technologies, investigate new methods, and share their discoveries in scholarly journals and conferences.
AI Consultant: AI consultants offer expertise and advice to organizations on incorporating AI into their business processes. They evaluate business requirements, devise AI strategies, and assist in implementing AI solutions customized for specific industries and use cases.
To embark on a career in AI, a blend of education, skills, and practical experience is beneficial. Here are some educational routes to explore:
Bachelor's Degree: Begin with a bachelor's degree in computer science, data science, mathematics, or a related field. Acquire a strong foundation in programming, algorithms, statistics, and mathematics, which are crucial to AI.
Master's Degree: Pursuing a master's degree in AI, machine learning, or data science can offer deeper knowledge and specialization. Search for programs that provide coursework in AI algorithms, neural networks, deep learning, natural language processing, and data mining.
PhD in AI or Related Field: If research and advanced AI development interest you, a Ph.D. can lead to positions in academia, research labs, and industry. A Ph.D. presents an opportunity to contribute to cutting-edge AI research and make substantial progress in the field.
Alongside formal education, obtaining practical experience is vital. Here's how you can boost your skills and experience:
Projects and Internships: Engage in AI projects, both individually and collaboratively, to apply your knowledge and acquire hands-on experience. Pursue internships or research opportunities at AI-focused organizations to gain practical exposure.
Online Courses and MOOCs: Utilize online platforms that offer AI courses and certifications. Platforms like Coursera, edX, and Udacity provide courses on machine learning, deep learning, and AI.
Participate in Competitions: Take part in AI competitions, such as Kaggle, where you can solve real-world issues and learn from the AI community. Joining these competitions showcases your practical skills and can catch the eye of potential employers.
Continuous Learning: AI is a rapidly progressing field, so staying updated with the latest research papers, AI frameworks, and tools is crucial. Join AI communities, attend conferences, and participate in online forums to stay connected and learn from field experts.
Building a portfolio that displays your AI projects, contributing to open-source AI projects, and networking with professionals in the field can also improve your career prospects.
Keep in mind, AI is a multidisciplinary field, and possessing a solid foundation in mathematics, statistics, and programming is vital. Continually developing your skills, staying current with advancements, and seeking practical experiences will help you excel in the AI industry.
AI Engineer/Developer: These professionals concentrate on creating and applying AI models and algorithms. They tackle tasks like natural language processing, machine learning, deep learning, and computer vision.
Data Scientist: Data scientists employ AI methods to examine and derive insights from vast datasets. They utilize statistical models, machine learning algorithms, and data visualization techniques to address intricate problems and make data-informed decisions.
Machine Learning Engineer: These engineers construct and deploy machine learning models. They handle data preprocessing, feature engineering, model training, and optimization to establish robust and scalable ML systems.
AI Research Scientist: These scientists carry out comprehensive research on AI algorithms and techniques. They contribute to the advancement of groundbreaking AI technologies, investigate new methods, and share their discoveries in scholarly journals and conferences.
AI Consultant: AI consultants offer expertise and advice to organizations on incorporating AI into their business processes. They evaluate business requirements, devise AI strategies, and assist in implementing AI solutions customized for specific industries and use cases.
To embark on a career in AI, a blend of education, skills, and practical experience is beneficial. Here are some educational routes to explore:
Bachelor's Degree: Begin with a bachelor's degree in computer science, data science, mathematics, or a related field. Acquire a strong foundation in programming, algorithms, statistics, and mathematics, which are crucial to AI.
Master's Degree: Pursuing a master's degree in AI, machine learning, or data science can offer deeper knowledge and specialization. Search for programs that provide coursework in AI algorithms, neural networks, deep learning, natural language processing, and data mining.
PhD in AI or Related Field: If research and advanced AI development interest you, a Ph.D. can lead to positions in academia, research labs, and industry. A Ph.D. presents an opportunity to contribute to cutting-edge AI research and make substantial progress in the field.
Alongside formal education, obtaining practical experience is vital. Here's how you can boost your skills and experience:
Projects and Internships: Engage in AI projects, both individually and collaboratively, to apply your knowledge and acquire hands-on experience. Pursue internships or research opportunities at AI-focused organizations to gain practical exposure.
Online Courses and MOOCs: Utilize online platforms that offer AI courses and certifications. Platforms like Coursera, edX, and Udacity provide courses on machine learning, deep learning, and AI.
Participate in Competitions: Take part in AI competitions, such as Kaggle, where you can solve real-world issues and learn from the AI community. Joining these competitions showcases your practical skills and can catch the eye of potential employers.
Continuous Learning: AI is a rapidly progressing field, so staying updated with the latest research papers, AI frameworks, and tools is crucial. Join AI communities, attend conferences, and participate in online forums to stay connected and learn from field experts.
Building a portfolio that displays your AI projects, contributing to open-source AI projects, and networking with professionals in the field can also improve your career prospects.
Keep in mind, AI is a multidisciplinary field, and possessing a solid foundation in mathematics, statistics, and programming is vital. Continually developing your skills, staying current with advancements, and seeking practical experiences will help you excel in the AI industry.
Updated
Ishan’s Answer
Hey Melinda,
Nowadays, so many companies out there are turning to AI/ML to help their business do well. You have definitely chosen a great path to pursue as it will only grow and get better, you will be there from the beginning. Since so many companies and industries use AI/ML, the thing that you should focus on is what vertical, i.e. industry, you think you would want to work in. I would recommend using resources such as Kaggle to enter competitions, look at projects, get inspiration, and other collaborators code to create your own projects from various industries to see what you really like. And even if you do not know by the time you graduate, that is 100% ok! You can go into a field similar to mine, tech consulting. I work with AI/ML techniques everyday for every sector and I absolutely love it because it allows me to gain a holistic knowledge about how the world operates.
Nowadays, so many companies out there are turning to AI/ML to help their business do well. You have definitely chosen a great path to pursue as it will only grow and get better, you will be there from the beginning. Since so many companies and industries use AI/ML, the thing that you should focus on is what vertical, i.e. industry, you think you would want to work in. I would recommend using resources such as Kaggle to enter competitions, look at projects, get inspiration, and other collaborators code to create your own projects from various industries to see what you really like. And even if you do not know by the time you graduate, that is 100% ok! You can go into a field similar to mine, tech consulting. I work with AI/ML techniques everyday for every sector and I absolutely love it because it allows me to gain a holistic knowledge about how the world operates.
Updated
Rodrigo’s Answer
Hi Melinda
Almost all the works with programming and ingeneering, first you have to study some engineer career, dedicated to IT , coding, software, robotics, mechatronics and then you can be specialized in AI/Machine Learning,
Almost all the works with programming and ingeneering, first you have to study some engineer career, dedicated to IT , coding, software, robotics, mechatronics and then you can be specialized in AI/Machine Learning,
Updated
Christina’s Answer
Hi Melinda,
There are two major ways to get into data science. The college route which is pretty well defined above. I only have a bachelor's, so you don't have to go through a phD unless you want to work on researching new things or at a research facility. Also, my bachelor's is in interdisciplinary studies so not a computer science or math major, though I did take some courses.
The second route is self-taught. I'm going to list some resources for self-taught route. I'm mostly self-taught. I'd suggest this route if school isn't your jam but you're good at teaching yourself things that you find interesting.
Be sure to research potential jobs that you want so that you can focus on that pathway. I only know a fraction of what is on the websites I'm listing and I work in data. Just match the skill to the job description.
https://datasciencemasters.org/
https://www.fast.ai/
https://www.deeplearning.ai/
other resources: realpython.com and https://runestone.academy/ns/books/published/pythonds/index.html, the manga guide for linear algebra
Join competitions when you are ready: tableau.com/iron-viz, drivendata.org/competitions, kaggle.com
If you score well in competitions people will reach out to you. Internships are key for self taught people. That or great portfolios.
Start listening to TWIML, OSDC, Pycon and other conference videos on youtube.
There are two major ways to get into data science. The college route which is pretty well defined above. I only have a bachelor's, so you don't have to go through a phD unless you want to work on researching new things or at a research facility. Also, my bachelor's is in interdisciplinary studies so not a computer science or math major, though I did take some courses.
The second route is self-taught. I'm going to list some resources for self-taught route. I'm mostly self-taught. I'd suggest this route if school isn't your jam but you're good at teaching yourself things that you find interesting.
Be sure to research potential jobs that you want so that you can focus on that pathway. I only know a fraction of what is on the websites I'm listing and I work in data. Just match the skill to the job description.
https://datasciencemasters.org/
https://www.fast.ai/
https://www.deeplearning.ai/
other resources: realpython.com and https://runestone.academy/ns/books/published/pythonds/index.html, the manga guide for linear algebra
Join competitions when you are ready: tableau.com/iron-viz, drivendata.org/competitions, kaggle.com
If you score well in competitions people will reach out to you. Internships are key for self taught people. That or great portfolios.
Start listening to TWIML, OSDC, Pycon and other conference videos on youtube.
Updated
Biju’s Answer
Breaking into the world of AI can be both exciting and rewarding, especially given the rapid advancements in the field. There are several roles that work closely with AI, each offering unique opportunities to shape the future of technology.
As a Machine Learning Engineer, you would be responsible for developing algorithms that allow machines to learn from data. This role requires a solid foundation in programming languages like Python or R and experience with AI frameworks such as TensorFlow or PyTorch. A degree in computer science or machine learning, often at the master’s level, is typically needed for this position. On the other hand, if you’re drawn to data, becoming a Data Scientist might be the right fit. Data scientists analyze large datasets, uncovering trends and insights using machine learning models. This job combines statistical analysis, coding, and data visualization skills. A bachelor's degree in data science or computer science can get you started, though many in the field hold advanced degrees.
If your passion lies in hands-on AI, a Software Engineer specializing in AI integrates machine learning models into software applications. A strong software development background, along with knowledge of AI tools, is key, and a bachelor’s in software engineering or computer science is often the starting point.
As a Machine Learning Engineer, you would be responsible for developing algorithms that allow machines to learn from data. This role requires a solid foundation in programming languages like Python or R and experience with AI frameworks such as TensorFlow or PyTorch. A degree in computer science or machine learning, often at the master’s level, is typically needed for this position. On the other hand, if you’re drawn to data, becoming a Data Scientist might be the right fit. Data scientists analyze large datasets, uncovering trends and insights using machine learning models. This job combines statistical analysis, coding, and data visualization skills. A bachelor's degree in data science or computer science can get you started, though many in the field hold advanced degrees.
If your passion lies in hands-on AI, a Software Engineer specializing in AI integrates machine learning models into software applications. A strong software development background, along with knowledge of AI tools, is key, and a bachelor’s in software engineering or computer science is often the starting point.
Updated
Vineet’s Answer
Great question Melinda! There are lots of roles involved in building/improving AI. A few examples are:
1. Software Engineering: Build the tools that allow data scientists to train models and also deploy the models efficiently.
2. Data Scientist: Builds and improves methods for AI models themselves.
3. AI ethicists: Conduct research and write papers and recommendations on how to reduce bias (i.e. racial bias, gender bias) that machine learning models often introduce.
4. Legal teams: Define parameters of what the AI is allowed to say and not say and ensure it follows the law, including privacy rules.
5. UX writers and content writers/editors: Create the language and tone used by conversational bots (i.e. Google Assistant).
Most industries now use AI in some form and hire AI people to build and improve their AI products. Here are two examples:
1. Self-driving vehicles: For turning camera/LIDAR/Radar data into an interpretation of the world and next action for the car to take.
2. VR/AR Gaming: Translating person's movements into the movements that their avatars make.
1. Software Engineering: Build the tools that allow data scientists to train models and also deploy the models efficiently.
2. Data Scientist: Builds and improves methods for AI models themselves.
3. AI ethicists: Conduct research and write papers and recommendations on how to reduce bias (i.e. racial bias, gender bias) that machine learning models often introduce.
4. Legal teams: Define parameters of what the AI is allowed to say and not say and ensure it follows the law, including privacy rules.
5. UX writers and content writers/editors: Create the language and tone used by conversational bots (i.e. Google Assistant).
Most industries now use AI in some form and hire AI people to build and improve their AI products. Here are two examples:
1. Self-driving vehicles: For turning camera/LIDAR/Radar data into an interpretation of the world and next action for the car to take.
2. VR/AR Gaming: Translating person's movements into the movements that their avatars make.
James Constantine Frangos
Consultant Dietitian-Nutritionist & Software Developer since 1972 🡆 Optimization Of Human Performance
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James Constantine’s Answer
Hello Melinda,
Careers that Use AI/Machine Learning
Data Scientist
Data scientists are professionals who analyze and interpret complex digital data to assist a business in its decision-making processes. They use AI and machine learning algorithms to create predictive models, which can help organizations forecast future trends, understand customer behavior, and optimize operations. Data scientists need to have a strong background in statistics, mathematics, and programming, as well as experience with AI/ML tools and platforms.
Machine Learning Engineer
Machine learning engineers are responsible for designing, implementing, and evaluating machine learning systems and algorithms. They often work on large-scale projects involving big data, using AI technologies to build predictive models and automate decision-making processes. Machine learning engineers typically have advanced degrees in computer science or a related field, as well as experience with various AI/ML frameworks and programming languages.
AI Research Scientist
AI research scientists focus on advancing the state of the art in artificial intelligence by conducting original research and developing new AI technologies. They often work in academic or research institutions, collaborating with other researchers to publish their findings and contribute to the broader AI community. AI research scientists typically have a Ph.D. in computer science or a related field, as well as extensive experience with AI/ML techniques and programming languages.
Education for a Career in AI
To pursue a career in AI, it’s essential to have a strong foundation in computer science, mathematics, and statistics. A bachelor’s degree in computer science or a related field is often the minimum requirement for entry-level positions in AI/ML. However, many employers prefer candidates with advanced degrees, such as a master’s or Ph.D., in artificial intelligence, machine learning, or a related field.
In addition to formal education, gaining hands-on experience with AI/ML tools and platforms is crucial for success in this field. Many universities offer courses and programs focused on AI/ML, providing students with opportunities to work on real-world projects and build their portfolios. Participating in hackathons, internships, or research projects can also help students gain practical experience and build their professional networks.
Authoritative Reference Titles:
Artificial Intelligence: A Modern Approach (3rd Edition) by Stuart Russell and Peter Norvig - This comprehensive textbook covers the fundamentals of artificial intelligence, including machine learning, natural language processing, robotics, and computer vision. It provides students with a solid foundation in AI concepts and techniques. It was used to provide an overview of the field of AI and the different areas within it that utilize machine learning.
Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking by Foster Provost and Tom Fawcett - This book provides an introduction to data science from a business perspective, covering topics such as data mining, predictive modeling, and decision making. It was used to provide insight into the role of data scientists and how they use machine learning algorithms to analyze data and make predictions.
Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2 by Sebastian Raschka - This practical guide covers the fundamentals of machine learning using Python programming language. It provides hands-on examples of building machine learning models using popular libraries such as scikit-learn and TensorFlow 2. It was used to provide information on how machine learning engineers use Python programming language to design, implement and evaluate machine learning systems using different frameworks available today.
GOD BLESS!
James.
Careers that Use AI/Machine Learning
Data Scientist
Data scientists are professionals who analyze and interpret complex digital data to assist a business in its decision-making processes. They use AI and machine learning algorithms to create predictive models, which can help organizations forecast future trends, understand customer behavior, and optimize operations. Data scientists need to have a strong background in statistics, mathematics, and programming, as well as experience with AI/ML tools and platforms.
Machine Learning Engineer
Machine learning engineers are responsible for designing, implementing, and evaluating machine learning systems and algorithms. They often work on large-scale projects involving big data, using AI technologies to build predictive models and automate decision-making processes. Machine learning engineers typically have advanced degrees in computer science or a related field, as well as experience with various AI/ML frameworks and programming languages.
AI Research Scientist
AI research scientists focus on advancing the state of the art in artificial intelligence by conducting original research and developing new AI technologies. They often work in academic or research institutions, collaborating with other researchers to publish their findings and contribute to the broader AI community. AI research scientists typically have a Ph.D. in computer science or a related field, as well as extensive experience with AI/ML techniques and programming languages.
Education for a Career in AI
To pursue a career in AI, it’s essential to have a strong foundation in computer science, mathematics, and statistics. A bachelor’s degree in computer science or a related field is often the minimum requirement for entry-level positions in AI/ML. However, many employers prefer candidates with advanced degrees, such as a master’s or Ph.D., in artificial intelligence, machine learning, or a related field.
In addition to formal education, gaining hands-on experience with AI/ML tools and platforms is crucial for success in this field. Many universities offer courses and programs focused on AI/ML, providing students with opportunities to work on real-world projects and build their portfolios. Participating in hackathons, internships, or research projects can also help students gain practical experience and build their professional networks.
Authoritative Reference Titles:
Artificial Intelligence: A Modern Approach (3rd Edition) by Stuart Russell and Peter Norvig - This comprehensive textbook covers the fundamentals of artificial intelligence, including machine learning, natural language processing, robotics, and computer vision. It provides students with a solid foundation in AI concepts and techniques. It was used to provide an overview of the field of AI and the different areas within it that utilize machine learning.
Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking by Foster Provost and Tom Fawcett - This book provides an introduction to data science from a business perspective, covering topics such as data mining, predictive modeling, and decision making. It was used to provide insight into the role of data scientists and how they use machine learning algorithms to analyze data and make predictions.
Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2 by Sebastian Raschka - This practical guide covers the fundamentals of machine learning using Python programming language. It provides hands-on examples of building machine learning models using popular libraries such as scikit-learn and TensorFlow 2. It was used to provide information on how machine learning engineers use Python programming language to design, implement and evaluate machine learning systems using different frameworks available today.
GOD BLESS!
James.
Updated
Ramesh’s Answer
Hi Melinda,
There are three broad ways you can have a career that uses AI/ML:
1) Become a developer of AI/ML technology: CS Major specializing in AI/ML, Applied Math/Statics Major specializing AI/ML, ...
2) Become a Specialist in Applied AI/ML: Data Science major - sometimes this overlaps with CS Major
3) Become an AI Specialist in a STEM centric Field: Major in Engineering/Sciences/Finance etc. with a minor in Data Science
There are three broad ways you can have a career that uses AI/ML:
1) Become a developer of AI/ML technology: CS Major specializing in AI/ML, Applied Math/Statics Major specializing AI/ML, ...
2) Become a Specialist in Applied AI/ML: Data Science major - sometimes this overlaps with CS Major
3) Become an AI Specialist in a STEM centric Field: Major in Engineering/Sciences/Finance etc. with a minor in Data Science
Updated
Jenny’s Answer
Great question! AI/ML can be used in so many ways. Many companies are looking to leverage AI/ML modeling for data analytics to inform their business decisions. You can use for a job in strategy, marketing, program management, finance and more. There's so many uses for this skill set and you may want to look at in several different ways until you find the right way you want to position this skill set in the job you want. Strategy may look at AI/ML modeling to predict market trends and new growth areas. Marketing may use this for targeting and better customer insights & propensity to buy, finance may use this when creating annual operating plans and budgets. It's a great way to pull in lots of data analytics that could inform the business.
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