Forwarded from Artem Ryblov’s Data Science Weekly
Google Machine Learning Education
Learn to build ML products with Google's Machine Learning Courses.
Foundational courses
The foundational courses cover machine learning fundamentals and core concepts. They recommend taking them in the order below.
1. Introduction to Machine Learning
A brief introduction to machine learning.
2. Machine Learning Crash Course
A hands-on course to explore the critical basics of machine learning.
3. Problem Framing
A course to help you map real-world problems to machine learning solutions.
4. Data Preparation and Feature Engineering
An introduction to preparing your data for ML workflows.
5. Testing and Debugging
Strategies for testing and debugging machine learning models and pipelines.
Advanced Courses
The advanced courses teach tools and techniques for solving a variety of machine learning problems. The courses are structured independently. Take them based on interest or problem domain.
- Decision Forests
Decision forests are an alternative to neural networks.
- Recommendation Systems
Recommendation systems generate personalized suggestions.
- Clustering
Clustering is a key unsupervised machine learning strategy to associate related items.
- Generative Adversarial Networks
GANs create new data instances that resemble your training data.
- Image Classification
Is that a picture of a cat or is it a dog?
- Fairness in Perspective API
Hands-on practice debugging fairness issues.
Guides
Their guides offer simple step-by-step walkthroughs for solving common machine learning problems using best practices.
- Rules of ML
Become a better machine learning engineer by following these machine learning best practices used at Google.
- People + AI Guidebook
This guide assists UXers, PMs, and developers in collaboratively working through AI design topics and questions.
- Text Classification
This comprehensive guide provides a walkthrough to solving text classification problems using machine learning.
- Good Data Analysis
This guide describes the tricks that an expert data analyst uses to evaluate huge data sets in machine learning problems.
- Deep Learning Tuning Playbook
This guide explains a scientific way to optimize the training of deep learning models.
Link: https://developers.google.com/machine-learning?hl=en
Navigational hashtags: #armknowledgesharing #armcourses
General hashtags: #machinelearning #ml #google #course #courses #featureengineering #recsys #clustering #gan
@data_science_weekly
Learn to build ML products with Google's Machine Learning Courses.
Foundational courses
The foundational courses cover machine learning fundamentals and core concepts. They recommend taking them in the order below.
1. Introduction to Machine Learning
A brief introduction to machine learning.
2. Machine Learning Crash Course
A hands-on course to explore the critical basics of machine learning.
3. Problem Framing
A course to help you map real-world problems to machine learning solutions.
4. Data Preparation and Feature Engineering
An introduction to preparing your data for ML workflows.
5. Testing and Debugging
Strategies for testing and debugging machine learning models and pipelines.
Advanced Courses
The advanced courses teach tools and techniques for solving a variety of machine learning problems. The courses are structured independently. Take them based on interest or problem domain.
- Decision Forests
Decision forests are an alternative to neural networks.
- Recommendation Systems
Recommendation systems generate personalized suggestions.
- Clustering
Clustering is a key unsupervised machine learning strategy to associate related items.
- Generative Adversarial Networks
GANs create new data instances that resemble your training data.
- Image Classification
Is that a picture of a cat or is it a dog?
- Fairness in Perspective API
Hands-on practice debugging fairness issues.
Guides
Their guides offer simple step-by-step walkthroughs for solving common machine learning problems using best practices.
- Rules of ML
Become a better machine learning engineer by following these machine learning best practices used at Google.
- People + AI Guidebook
This guide assists UXers, PMs, and developers in collaboratively working through AI design topics and questions.
- Text Classification
This comprehensive guide provides a walkthrough to solving text classification problems using machine learning.
- Good Data Analysis
This guide describes the tricks that an expert data analyst uses to evaluate huge data sets in machine learning problems.
- Deep Learning Tuning Playbook
This guide explains a scientific way to optimize the training of deep learning models.
Link: https://developers.google.com/machine-learning?hl=en
Navigational hashtags: #armknowledgesharing #armcourses
General hashtags: #machinelearning #ml #google #course #courses #featureengineering #recsys #clustering #gan
@data_science_weekly
Google for Developers
Machine Learning | Google for Developers
Educational resources for machine learning.
#trading #balch #course
Вот хороший ютуб-курс для новичков по питону, пандас, нампай, ML с прицелом на трейдинг и другие финансовые приложения. Лучший, что я знаю. Читает небезызвестный Tucker Balch.
https://www.youtube.com/playlist?list=PLAwxTw4SYaPnIRwl6rad_mYwEk4Gmj7Mx
Вот хороший ютуб-курс для новичков по питону, пандас, нампай, ML с прицелом на трейдинг и другие финансовые приложения. Лучший, что я знаю. Читает небезызвестный Tucker Balch.
https://www.youtube.com/playlist?list=PLAwxTw4SYaPnIRwl6rad_mYwEk4Gmj7Mx