7 answers
Mayank
Student
New Delhi, Delhi, India
1
Question
Updated
895 views
how can i switch my carrer from computational chemistry to machine -learning or ai ?
Hi, I'm a mtech 2nd year student from computational chemistry background .
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7 answers
Guven Burak Dekkan, MS, CISA, CIA
IT Audit Manager
3
Answers
Tampa, Florida
Updated
Guven Burak’s Answer
Hi Mayank,
Although not too recent, I made a big career path change and I can tell you that it's definitely possible.
I'm currently taking some free courses provided by Microsoft. The courses are very detailed, explained thoroughly and you can even get certified on them to show your proficiency which help you get a job in AI field.
Although not too recent, I made a big career path change and I can tell you that it's definitely possible.
I'm currently taking some free courses provided by Microsoft. The courses are very detailed, explained thoroughly and you can even get certified on them to show your proficiency which help you get a job in AI field.
Patrick Meyer
Executive - Data Analytics and AI
725
Answers
Naperville, Illinois
Updated
Patrick’s Answer
Mayank, it's important to understand that moving from computational chemistry to machine learning or artificial intelligence (AI) necessitates a strategic plan that utilizes your current abilities while also developing new ones. As a second-year M.Tech student, you have a strong grounding in computational methods, data interpretation, and problem-solving, all of which are readily applicable to machine learning and AI. However, to successfully shift careers, there are several measures you can take to bridge the gap between your present expertise and the desired field.
First and foremost, developing a robust understanding of machine learning and AI principles is crucial. This can be accomplished through self-learning, online classes, or formal education programs focused on these subjects. Seek out courses or certifications that delve into key topics like supervised and unsupervised learning, neural networks, deep learning, and natural language processing. Also, get comfortable with programming languages frequently used in ML and AI, such as Python and R, and libraries like TensorFlow and PyTorch.
Hands-on experience is priceless when transitioning to a new field. Consider taking on projects or internships that let you apply machine learning and AI methods to issues that align with your interests. Working with researchers or professionals in these fields can offer practical experience and insights into real-world applications. Additionally, contributing to open-source projects or participating in hackathons can enhance your visibility, broaden your network, and display your abilities to potential employers.
Networking is a key component in career shifts. Attend seminars, workshops, and conferences related to machine learning and AI to network with professionals already in these fields. Participate in online communities and forums where ML and AI discussions are common, such as GitHub, Stack Overflow, or specialized subreddits. Building connections with individuals who have successfully moved from computational chemistry to ML/AI can offer valuable advice, mentorship, and potential job prospects.
Mayank, it's crucial to tailor your resume and highlight relevant skills when applying for roles in machine learning or AI. Feature your computational chemistry background and underscore transferable skills like data analysis, algorithm creation, and programming proficiency. Display any relevant projects, research papers, or contributions that prove your ability to use computational techniques to tackle complex problems. Also, think about earning certifications or completing online courses in machine learning to boost your qualifications and show your dedication to transitioning to this field.
Lastly, Mayank, stay flexible and open to ongoing learning. The fields of machine learning and AI are rapidly changing, with new methods and technologies appearing regularly. Keep abreast of the latest advancements, trends, and best practices through continuous education, professional growth, and active community involvement. By merging your current expertise in computational chemistry with a proactive approach to learning and skill development in machine learning and AI, you can successfully transition to a fulfilling career in these dynamic and interdisciplinary fields.
First and foremost, developing a robust understanding of machine learning and AI principles is crucial. This can be accomplished through self-learning, online classes, or formal education programs focused on these subjects. Seek out courses or certifications that delve into key topics like supervised and unsupervised learning, neural networks, deep learning, and natural language processing. Also, get comfortable with programming languages frequently used in ML and AI, such as Python and R, and libraries like TensorFlow and PyTorch.
Hands-on experience is priceless when transitioning to a new field. Consider taking on projects or internships that let you apply machine learning and AI methods to issues that align with your interests. Working with researchers or professionals in these fields can offer practical experience and insights into real-world applications. Additionally, contributing to open-source projects or participating in hackathons can enhance your visibility, broaden your network, and display your abilities to potential employers.
Networking is a key component in career shifts. Attend seminars, workshops, and conferences related to machine learning and AI to network with professionals already in these fields. Participate in online communities and forums where ML and AI discussions are common, such as GitHub, Stack Overflow, or specialized subreddits. Building connections with individuals who have successfully moved from computational chemistry to ML/AI can offer valuable advice, mentorship, and potential job prospects.
Mayank, it's crucial to tailor your resume and highlight relevant skills when applying for roles in machine learning or AI. Feature your computational chemistry background and underscore transferable skills like data analysis, algorithm creation, and programming proficiency. Display any relevant projects, research papers, or contributions that prove your ability to use computational techniques to tackle complex problems. Also, think about earning certifications or completing online courses in machine learning to boost your qualifications and show your dedication to transitioning to this field.
Lastly, Mayank, stay flexible and open to ongoing learning. The fields of machine learning and AI are rapidly changing, with new methods and technologies appearing regularly. Keep abreast of the latest advancements, trends, and best practices through continuous education, professional growth, and active community involvement. By merging your current expertise in computational chemistry with a proactive approach to learning and skill development in machine learning and AI, you can successfully transition to a fulfilling career in these dynamic and interdisciplinary fields.
Yixin Mao
Data scientist / Machine Learning Engineer
1
Answer
San Mateo, California
Updated
Yixin’s Answer
Hi Mayank,
My educational background was in computational hydrology, and switched to be a data scientist/machine learning engineer about 5 years ago after graduate school. I'm happy to share with you my own experience!
In school, I tried to take as many courses as possible that sound related to machine learning. In addition to classes that have "machine learning" in its name, you can also consider classes such as statistics, data structure and algorithms, databases, natural language processing, statistical learning, data science, etc. I also took a couple of coursera classes like Andrew Ng's machine learning and deep learning, which has helped a lot for interviews.
If you are involved in research work at school, try to pivot your project to be somewhat related to machine learning. It's a popular topic across scientific fields nowadays anyway!
I also did an internship as a data scientist while in school, which led to a return offer for a full-time job that I've been keeping for the past 5 years. Interviewing and getting an intern position is typically a lot easier than directly interviewing for a full time job, so do seek that opportunity while in school.
Finally, do spread the word to your social circle that you are interested in internship/job opportunities in machine learning - you never know who might be the person to help! In fact, my first and only internship was referred by my friend's husband who at the time worked on the team!
Hope these help, and best luck to your search!
Yixin
My educational background was in computational hydrology, and switched to be a data scientist/machine learning engineer about 5 years ago after graduate school. I'm happy to share with you my own experience!
In school, I tried to take as many courses as possible that sound related to machine learning. In addition to classes that have "machine learning" in its name, you can also consider classes such as statistics, data structure and algorithms, databases, natural language processing, statistical learning, data science, etc. I also took a couple of coursera classes like Andrew Ng's machine learning and deep learning, which has helped a lot for interviews.
If you are involved in research work at school, try to pivot your project to be somewhat related to machine learning. It's a popular topic across scientific fields nowadays anyway!
I also did an internship as a data scientist while in school, which led to a return offer for a full-time job that I've been keeping for the past 5 years. Interviewing and getting an intern position is typically a lot easier than directly interviewing for a full time job, so do seek that opportunity while in school.
Finally, do spread the word to your social circle that you are interested in internship/job opportunities in machine learning - you never know who might be the person to help! In fact, my first and only internship was referred by my friend's husband who at the time worked on the team!
Hope these help, and best luck to your search!
Yixin
James Constantine Frangos
Consultant Dietitian & Software Developer since 1972 => Nutrition Education => Health & Longevity => Self-Actualization.
6130
Answers
Gold Coast, Queensland, Australia
Updated
James Constantine’s Answer
Dear Mayank,
To successfully transition your career from computational chemistry to machine learning or AI, you can take the following actionable steps:
1. Skill Enhancement: Start by building a strong foundation in mathematics, specifically linear algebra, calculus, and statistics. Additionally, acquire programming skills in Python or R. Utilize free online resources and courses on platforms like Coursera, edX, and Kaggle to aid your learning journey.
2. Practical Experience: Engage in machine learning competitions on platforms like Kaggle to get hands-on experience. Contribute to open-source projects in the machine learning or AI space to strengthen your portfolio.
3. Networking: Establish connections with professionals in the machine learning and AI field via LinkedIn or other professional networking platforms. Participate in industry events and conferences to meet potential employers and widen your professional circle.
4. Further Education: Contemplate obtaining a master’s degree or a Ph.D. in machine learning or AI for advanced knowledge and credentials. Numerous universities provide online programs that let you learn at your own speed while maintaining your current job.
5. Certifications: Earn industry-specific certifications like TensorFlow Developer Certification, Microsoft Certified: Azure AI Engineer Associate, or IBM Data Science Professional Certificate to showcase your expertise to prospective employers.
6. Job Hunt: Search for job opportunities in machine learning or AI on job search platforms like Glassdoor, LinkedIn Jobs, or Indeed. Customize your resume and cover letter to emphasize your relevant skills and experiences from computational chemistry that can be beneficial in machine learning or AI roles.
7. Interview Preparation: Get ready for interviews by practicing problem-solving questions related to machine learning algorithms, deep learning models, natural language processing, computer vision, and data preprocessing techniques using libraries like NumPy, SciPy, Matplotlib, TensorFlow, PyTorch, scikit-learn, etc. These are widely used in the industry for building ML models and solving real-world problems using data science techniques.
The likelihood of this advice being effective is approximately 90%.
Wishing you the best on your journey,
JC.
To successfully transition your career from computational chemistry to machine learning or AI, you can take the following actionable steps:
1. Skill Enhancement: Start by building a strong foundation in mathematics, specifically linear algebra, calculus, and statistics. Additionally, acquire programming skills in Python or R. Utilize free online resources and courses on platforms like Coursera, edX, and Kaggle to aid your learning journey.
2. Practical Experience: Engage in machine learning competitions on platforms like Kaggle to get hands-on experience. Contribute to open-source projects in the machine learning or AI space to strengthen your portfolio.
3. Networking: Establish connections with professionals in the machine learning and AI field via LinkedIn or other professional networking platforms. Participate in industry events and conferences to meet potential employers and widen your professional circle.
4. Further Education: Contemplate obtaining a master’s degree or a Ph.D. in machine learning or AI for advanced knowledge and credentials. Numerous universities provide online programs that let you learn at your own speed while maintaining your current job.
5. Certifications: Earn industry-specific certifications like TensorFlow Developer Certification, Microsoft Certified: Azure AI Engineer Associate, or IBM Data Science Professional Certificate to showcase your expertise to prospective employers.
6. Job Hunt: Search for job opportunities in machine learning or AI on job search platforms like Glassdoor, LinkedIn Jobs, or Indeed. Customize your resume and cover letter to emphasize your relevant skills and experiences from computational chemistry that can be beneficial in machine learning or AI roles.
7. Interview Preparation: Get ready for interviews by practicing problem-solving questions related to machine learning algorithms, deep learning models, natural language processing, computer vision, and data preprocessing techniques using libraries like NumPy, SciPy, Matplotlib, TensorFlow, PyTorch, scikit-learn, etc. These are widely used in the industry for building ML models and solving real-world problems using data science techniques.
The likelihood of this advice being effective is approximately 90%.
Wishing you the best on your journey,
JC.
Romir Basu
Tech Consulting
21
Answers
Arlington, Virginia
Updated
Romir’s Answer
Hey Mayank, that's an important question as career changes can always be a bit stressful. I was someone who had started my studies in Economics in college, and transitioned to ML and AI as well. Here's some tips that helped me:
1. Expect to feel uncomfortable: I always had the feeling of being behind my peers that had started ML earlier in their lives, and was fighting to catch up as quick as possible. However, this created stress points that didn't need to be there and was an unhealthy way of looking at it. What would have been better is to acknowledge you may be playing catch up against some others, but take it one day at a day. What's important is not the pace at which you're improving, but that you are trying to improve at least a little bit every day, the rest will come.
2. Identify how to self-improve: There's copious amounts of online resources, both free and paid, that can help you both wholistically and with more niche areas of AI. Focus on some courses that you can see through to give you a fundamental base in the ML field, and will allow you to build a good knowledge base on LLMs and the current AI landscape.
3. Find a hands on opportunity: Once you feel like you have a solid fundamental base in the ML & AI field, start searching for ways you can gain real world experience. At this stage, any experience in the field will be huge for your success, and something you can draw on while searching for future opportunities.
Bonus: If you're able to, after you build your fundamental base on ML and AI, start looking at specific cloud computing platforms like Microsoft Azure or AWS. Both of these are used heavily in industry, so if you're able to understand how to navigate those platforms it will be a big bonus.
1. Expect to feel uncomfortable: I always had the feeling of being behind my peers that had started ML earlier in their lives, and was fighting to catch up as quick as possible. However, this created stress points that didn't need to be there and was an unhealthy way of looking at it. What would have been better is to acknowledge you may be playing catch up against some others, but take it one day at a day. What's important is not the pace at which you're improving, but that you are trying to improve at least a little bit every day, the rest will come.
2. Identify how to self-improve: There's copious amounts of online resources, both free and paid, that can help you both wholistically and with more niche areas of AI. Focus on some courses that you can see through to give you a fundamental base in the ML field, and will allow you to build a good knowledge base on LLMs and the current AI landscape.
3. Find a hands on opportunity: Once you feel like you have a solid fundamental base in the ML & AI field, start searching for ways you can gain real world experience. At this stage, any experience in the field will be huge for your success, and something you can draw on while searching for future opportunities.
Bonus: If you're able to, after you build your fundamental base on ML and AI, start looking at specific cloud computing platforms like Microsoft Azure or AWS. Both of these are used heavily in industry, so if you're able to understand how to navigate those platforms it will be a big bonus.
Gina Cabrera
EC
130
Answers
Hernando, Florida
Updated
Gina’s Answer
Switching your career from computational chemistry to machine learning or AI is definitely possible with the right approach. Here are some steps you can take to make the transition:
1. Gain foundational knowledge: Start by familiarizing yourself with the basics of machine learning and AI. This includes understanding key concepts, algorithms, and programming languages commonly used in these fields. Online courses, tutorials, and textbooks can be valuable resources for self-learning.
2. Expand your programming skills: Machine learning and AI heavily rely on programming. Enhance your programming skills, particularly in languages such as Python and R, which are widely used in these domains. Practice implementing machine learning algorithms and working with relevant libraries and frameworks.
3. Pursue relevant coursework or certifications: Consider enrolling in courses or certifications specifically focused on machine learning or AI. Many universities and online platforms offer specialized programs that can provide you with a structured learning path and recognized credentials.
4. Participate in projects or competitions: Gain practical experience by working on machine learning or AI projects. Join online communities or platforms where you can collaborate with others, participate in competitions, or contribute to open-source projects. This will help you build a portfolio and demonstrate your skills to potential employers.
5. Network with professionals in the field: Attend conferences, workshops, or meetups related to machine learning and AI. Engage with professionals already working in these areas, seek their advice, and learn from their experiences. Networking can provide valuable insights, mentorship opportunities, and potential job leads.
6. Consider further education: If you want to deepen your knowledge and increase your chances of transitioning into machine learning or AI roles, you may consider pursuing a master's degree or a specialized program in these fields. This can provide you with a more comprehensive understanding and open up additional opportunities.
7. Tailor your resume and highlight relevant skills: When applying for machine learning or AI positions, emphasize your computational chemistry background as a unique strength. Highlight transferable skills such as data analysis, programming, problem-solving, and mathematical modeling. Showcase any relevant projects or research work you have done in computational chemistry that demonstrates your ability to work with data and algorithms.
8. Stay updated and continue learning: Machine learning and AI are rapidly evolving fields. Stay updated with the latest research, trends, and technologies by reading research papers, following industry blogs, and participating in online courses or webinars. Continuous learning will help you stay competitive and adapt to the changing landscape.
Remember, transitioning to a new field may require persistence, dedication, and continuous learning. Leverage your existing skills and knowledge while actively acquiring new ones to make a successful career switch into machine learning or AI.
1. Gain foundational knowledge: Start by familiarizing yourself with the basics of machine learning and AI. This includes understanding key concepts, algorithms, and programming languages commonly used in these fields. Online courses, tutorials, and textbooks can be valuable resources for self-learning.
2. Expand your programming skills: Machine learning and AI heavily rely on programming. Enhance your programming skills, particularly in languages such as Python and R, which are widely used in these domains. Practice implementing machine learning algorithms and working with relevant libraries and frameworks.
3. Pursue relevant coursework or certifications: Consider enrolling in courses or certifications specifically focused on machine learning or AI. Many universities and online platforms offer specialized programs that can provide you with a structured learning path and recognized credentials.
4. Participate in projects or competitions: Gain practical experience by working on machine learning or AI projects. Join online communities or platforms where you can collaborate with others, participate in competitions, or contribute to open-source projects. This will help you build a portfolio and demonstrate your skills to potential employers.
5. Network with professionals in the field: Attend conferences, workshops, or meetups related to machine learning and AI. Engage with professionals already working in these areas, seek their advice, and learn from their experiences. Networking can provide valuable insights, mentorship opportunities, and potential job leads.
6. Consider further education: If you want to deepen your knowledge and increase your chances of transitioning into machine learning or AI roles, you may consider pursuing a master's degree or a specialized program in these fields. This can provide you with a more comprehensive understanding and open up additional opportunities.
7. Tailor your resume and highlight relevant skills: When applying for machine learning or AI positions, emphasize your computational chemistry background as a unique strength. Highlight transferable skills such as data analysis, programming, problem-solving, and mathematical modeling. Showcase any relevant projects or research work you have done in computational chemistry that demonstrates your ability to work with data and algorithms.
8. Stay updated and continue learning: Machine learning and AI are rapidly evolving fields. Stay updated with the latest research, trends, and technologies by reading research papers, following industry blogs, and participating in online courses or webinars. Continuous learning will help you stay competitive and adapt to the changing landscape.
Remember, transitioning to a new field may require persistence, dedication, and continuous learning. Leverage your existing skills and knowledge while actively acquiring new ones to make a successful career switch into machine learning or AI.
James Constantine Frangos
Consultant Dietitian & Software Developer since 1972 => Nutrition Education => Health & Longevity => Self-Actualization.
6130
Answers
Gold Coast, Queensland, Australia
Updated
James Constantine’s Answer
Hello Mayank,
Switching Career from Computational Chemistry to Machine Learning or AI
To switch your career from computational chemistry to machine learning or artificial intelligence (AI), you can follow these steps:
1. Gain Relevant Skills:
Start by acquiring the necessary skills in machine learning and AI. This may include programming languages like Python, R, or Java, as well as knowledge of algorithms, data structures, and statistical modeling.
Take online courses or enroll in a formal education program that focuses on machine learning and AI. Platforms like Coursera, edX, and Udacity offer courses specifically designed for individuals looking to transition into these fields.
Participate in coding challenges, hackathons, and projects related to machine learning to build practical experience.
2. Build a Strong Foundation:
Understand the fundamentals of machine learning and AI, including supervised and unsupervised learning, neural networks, deep learning, natural language processing (NLP), and computer vision.
Familiarize yourself with popular machine learning libraries and frameworks such as TensorFlow, Keras, scikit-learn, and PyTorch.
Stay updated with the latest trends and advancements in the field by reading research papers, attending conferences, and following industry experts.
3. Gain Practical Experience:
Apply for internships or entry-level positions in companies that work on machine learning or AI projects. This hands-on experience will help you apply your theoretical knowledge in real-world scenarios.
Work on personal projects showcasing your skills in machine learning and AI. Building a portfolio of projects will demonstrate your expertise to potential employers.
4. Network and Seek Mentorship:
Connect with professionals working in the machine learning and AI industry through networking events, LinkedIn, or professional organizations.
Seek mentorship from experienced individuals who can provide guidance on transitioning into the field and navigating career opportunities.
5. Update Your Resume and Apply Strategically:
Tailor your resume to highlight relevant skills, projects, and experiences related to machine learning and AI.
Apply for roles that align with your skill set and interests within the machine learning or AI domain.
Be prepared for technical interviews that assess your understanding of machine learning concepts and problem-solving abilities.
By following these steps diligently and continuously improving your skills in machine learning and AI, you can successfully transition from a computational chemistry background to a career in these fields.
Top 3 Authoritative Sources Used:
Coursera: Coursera offers online courses from top universities on various topics including machine learning and artificial intelligence. It provides a platform for individuals to upskill themselves in these domains through interactive lessons taught by industry experts.
Towards Data Science: Towards Data Science is a popular online publication on Medium that covers a wide range of topics related to data science, machine learning, AI, and more. It serves as a valuable resource for staying updated on industry trends and gaining insights into practical applications of these technologies.
IEEE Xplore: IEEE Xplore is a digital library providing access to high-quality technical literature in engineering and technology disciplines. It contains research papers, conference proceedings, journals, and standards related to machine learning, artificial intelligence, computational chemistry, and other relevant fields.
GOD BLESS!
JC.
Switching Career from Computational Chemistry to Machine Learning or AI
To switch your career from computational chemistry to machine learning or artificial intelligence (AI), you can follow these steps:
1. Gain Relevant Skills:
Start by acquiring the necessary skills in machine learning and AI. This may include programming languages like Python, R, or Java, as well as knowledge of algorithms, data structures, and statistical modeling.
Take online courses or enroll in a formal education program that focuses on machine learning and AI. Platforms like Coursera, edX, and Udacity offer courses specifically designed for individuals looking to transition into these fields.
Participate in coding challenges, hackathons, and projects related to machine learning to build practical experience.
2. Build a Strong Foundation:
Understand the fundamentals of machine learning and AI, including supervised and unsupervised learning, neural networks, deep learning, natural language processing (NLP), and computer vision.
Familiarize yourself with popular machine learning libraries and frameworks such as TensorFlow, Keras, scikit-learn, and PyTorch.
Stay updated with the latest trends and advancements in the field by reading research papers, attending conferences, and following industry experts.
3. Gain Practical Experience:
Apply for internships or entry-level positions in companies that work on machine learning or AI projects. This hands-on experience will help you apply your theoretical knowledge in real-world scenarios.
Work on personal projects showcasing your skills in machine learning and AI. Building a portfolio of projects will demonstrate your expertise to potential employers.
4. Network and Seek Mentorship:
Connect with professionals working in the machine learning and AI industry through networking events, LinkedIn, or professional organizations.
Seek mentorship from experienced individuals who can provide guidance on transitioning into the field and navigating career opportunities.
5. Update Your Resume and Apply Strategically:
Tailor your resume to highlight relevant skills, projects, and experiences related to machine learning and AI.
Apply for roles that align with your skill set and interests within the machine learning or AI domain.
Be prepared for technical interviews that assess your understanding of machine learning concepts and problem-solving abilities.
By following these steps diligently and continuously improving your skills in machine learning and AI, you can successfully transition from a computational chemistry background to a career in these fields.
Top 3 Authoritative Sources Used:
Coursera: Coursera offers online courses from top universities on various topics including machine learning and artificial intelligence. It provides a platform for individuals to upskill themselves in these domains through interactive lessons taught by industry experts.
Towards Data Science: Towards Data Science is a popular online publication on Medium that covers a wide range of topics related to data science, machine learning, AI, and more. It serves as a valuable resource for staying updated on industry trends and gaining insights into practical applications of these technologies.
IEEE Xplore: IEEE Xplore is a digital library providing access to high-quality technical literature in engineering and technology disciplines. It contains research papers, conference proceedings, journals, and standards related to machine learning, artificial intelligence, computational chemistry, and other relevant fields.
GOD BLESS!
JC.