A roadmap connecting many of the most important concepts in machine learning, how to learn them and what tools to use to perform them.
Namely:
- 🤔 Machine Learning Problems - what does a machine learning problem look like?
- ♻️ Machine Learning Process - once you’ve found a problem, what steps might you take to solve it?
- 🛠 Machine Learning Tools - what should you use to build your solution?
- 📘 Machine Learning Mathematics - what exactly is happening under the hood of all the machine learning code you're writing?
- 📚 Machines Learning Resources - okay, this is cool, how can I learn all of this?
See the full interactive version.
Watch a feature-length film video walkthrough (yes, really, it's longer than most movies).
Many of the materials in this roadmap were inspired by Daniel Formoso's machine learning mindmaps,so if you enjoyed this one, go and check out his. He also has a mindmap specifically for deep learning too.
Source: Daniel Bourke
Made With ML -Community Website for learning ML
Mathematics for Machine Learning
Full Stack Development Deep Learning
For Testing : Kaggle & Workera.ai
For Datasets : Kaggle Datasets & DataQuest
For Deployment : use any of these.
Google Cloud
AWS -Sagemaker
Microsoft Azure
Google Colab
Heroku