The First-Time Machine Learning Playbook.
(Read this if you want to efficiently learn machine learning, avoid frustration from searching for resources, and build a career in ML.)
#Ship30For30 #ML #MachineLearning
(Read this if you want to efficiently learn machine learning, avoid frustration from searching for resources, and build a career in ML.)
#Ship30For30 #ML #MachineLearning
What people think you need:
Most people think you need to be a math and stats expert to learn ML. ML can be math intensive, but it’s not a barrier to entry.
What you need:
• The Ability to code
• An Open and Curious Mind
• A Good starting point
Which leads me to …
Most people think you need to be a math and stats expert to learn ML. ML can be math intensive, but it’s not a barrier to entry.
What you need:
• The Ability to code
• An Open and Curious Mind
• A Good starting point
Which leads me to …
Where to Start:
If you can code, then start here: course.fast.ai
The @fastdotai Course is a fantastic place to start. Instead of going from the basics towards an application, it starts from an application and breaks it down piece by piece.
Did I mention it’s free?
If you can code, then start here: course.fast.ai
The @fastdotai Course is a fantastic place to start. Instead of going from the basics towards an application, it starts from an application and breaks it down piece by piece.
Did I mention it’s free?
@fastdotai What to do next:
Once you’ve completed the FastAI course, how do you use your newfound skills?
Simple Answer: Compete in @kaggle contests.
Just learning skills and concepts without putting them to use is like learning to ride a bike online and then not riding a bike outside
Once you’ve completed the FastAI course, how do you use your newfound skills?
Simple Answer: Compete in @kaggle contests.
Just learning skills and concepts without putting them to use is like learning to ride a bike online and then not riding a bike outside
@fastdotai @kaggle The Blue Pill:
You’re now good at applying ML (Kaggle will make sure of it) but you want to learn more theory?
• Try out the Deep learning Specialization on Coursera: deeplearning.ai/courses/deep-l…
• Read papers. Here’s how:
You’re now good at applying ML (Kaggle will make sure of it) but you want to learn more theory?
• Try out the Deep learning Specialization on Coursera: deeplearning.ai/courses/deep-l…
• Read papers. Here’s how:
@fastdotai @kaggle The Red Pill:
It’s not enough to know how ML works in theory. How do you deploy a model in the real world, test it, debug issues, etc. ?
Take the @full_stack_dl course here: fullstackdeeplearning.com/course/
While it is also free, they also have a paid cohort based version.
It’s not enough to know how ML works in theory. How do you deploy a model in the real world, test it, debug issues, etc. ?
Take the @full_stack_dl course here: fullstackdeeplearning.com/course/
While it is also free, they also have a paid cohort based version.
@fastdotai @kaggle @full_stack_dl Where to go from here:
Wait, there’s more? Based on your interests,
Specialize:
• #ComputerVision : pjreddie.com/courses/comput…
• #NLP: web.stanford.edu/class/cs224n/
• #Graphs: web.stanford.edu/class/cs224w/
• #ReinforcementLearning: deepmind.com/learning-resou…
Wait, there’s more? Based on your interests,
Specialize:
• #ComputerVision : pjreddie.com/courses/comput…
• #NLP: web.stanford.edu/class/cs224n/
• #Graphs: web.stanford.edu/class/cs224w/
• #ReinforcementLearning: deepmind.com/learning-resou…
@fastdotai @kaggle @full_stack_dl TL;DR
• Start with the FastAI Course
• Compete in Kaggle
• Read papers:
• Try the Deep Learning AI Specialization
• Complete the loop with Full Stack DL course
• Specialize
• Start with the FastAI Course
• Compete in Kaggle
• Read papers:
• Try the Deep Learning AI Specialization
• Complete the loop with Full Stack DL course
• Specialize
@fastdotai @kaggle @full_stack_dl If you found this thread useful:
• Drop me a follow @DSaience for more content on machine learning, being a productive researcher and the occasional photography content.
• Check out this other thread which you may find useful:
• Drop me a follow @DSaience for more content on machine learning, being a productive researcher and the occasional photography content.
• Check out this other thread which you may find useful:
https://twitter.com/DSaience/status/1558800611344355330?s=20&t=6KxNqV46HZJ-9Mz9icCDOg
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