How do you go from a data scientist or machine learning researcher to building full-stack applications? In this episode of Machine Learning Monthly, I cover resources to answer that question such as Lj Miranda's blog post on improving your software engineering skills as a researcher. Plus plenty more from the ML space during February 2021.

Links:
Read the post - https://zerotomastery.io/blog/machine...

My courses:
Learn Machine Learning (beginner) - https://dbourke.link/mlcourse
Learn TensorFlow and Deep Learning - https://dbourke.link/ZTMTFcourse

Connect elsewhere:
Web - https://www.mrdbourke.com/
Live coding on Twitch -   / mrdbourke  
Get email updates on my work - https://www.mrdbourke.com/newsletter

Timestamps:
0:00 - Intro/dancing/hello
1:01 - Where to sign up for ML Monthly emails
1:30 - My new TensorFlow course
2:45 - See me code live on Twitch (  / mrdbourke  )
3:00 - My ML deployment tutorial for CS329s
4:11 - Using deep learning to read brainwaves
7:07 - Alvaro’s guide to TensorFlow Serving
9:05 - Chip Huyen’s MLOps Tooling Landscape v2 blog post
11:08 - ML is going real-time by Chip Huyen
14:04 - Using GitHub for MLOps
15:42 - Upgrade your software engineering skills as a machine learning practitioner
17:42 - freeCodeCamp’s upcoming DS + ML curriculum
19:22 - Transformers from scratch by Peter Bloem
21:44 - Estimating training data influence with TracIn
24:40 - Learn fundamental statistics with Python
26:25 - The most important statistical ideas of the past 50 years
28:56 - Outro/submit your work/more dancing

#machinelearning #datascience