TODAY'S ISSUE
TODAY’S DAILY DOSE OF DATA SCIENCE
Pandas Mind Map
Here’s a mind map we created that lists all Pandas methods grouped by operation types:
It covers:
- I/O methods
- DataFrame creation methods
- DataFrame statistical info
- Renaming methods
- Plotting methods
- Time-series methods
- Grouping methods
- Pivot methods
- Categorical data methods
- and many more.
I am sure it will help you discover new Pandas methods as well :)
Find the complete mind map here: Pandas Mind Map.
👉 Over to you: What other mind maps do you want me to create?
CRASH COURSE (56 mins)
Graph Neural Networks
- Google Maps uses graph ML for ETA prediction.
- Pinterest uses graph ML (PingSage) for recommendations.
- Netflix uses graph ML (SemanticGNN) for recommendations.
- Spotify uses graph ML (HGNNs) for audiobook recommendations.
- Uber Eats uses graph ML (a GraphSAGE variant) to suggest dishes, restaurants, etc.
The list could go on since almost every major tech company I know employs graph ML in some capacity.
Becoming proficient in graph ML now seems to be far more critical than traditional deep learning to differentiate your profile and aim for these positions.
A significant proportion of our real-world data often exists (or can be represented) as graphs:
- Entities (nodes) are connected by relationships (edges).
- Connections carry significant meaning, which, if we knew how to model, can lead to much more robust models.
The field of graph neural networks (GNNs) intends to fill this gap by extending deep learning techniques to graph data.
Learn sophisticated graph architectures and how to train them on graph data in this crash course →
PRODUCTION ML (WITH IMPLEMENTATION)
5 techniques to test ML models in production
Despite rigorously testing an ML model locally (on validation and test sets), it could be a terrible idea to instantly replace the previous model with the new model.
A more reliable strategy is to test the model in production (yes, on real-world incoming data).
While this might sound risky, ML teams do it all the time, and it isn’t that complicated.
There are many ways to do this.
Learn five must-know strategies; how they work, when to use them, advantages and considerations, and their implementations here →
THAT'S A WRAP
No-Fluff Industry ML resources to
Succeed in DS/ML roles
At the end of the day, all businesses care about impact. That’s it!
- Can you reduce costs?
- Drive revenue?
- Can you scale ML models?
- Predict trends before they happen?
We have discussed several other topics (with implementations) in the past that align with such topics.
Here are some of them:
- Learn sophisticated graph architectures and how to train them on graph data in this crash course.
- So many real-world NLP systems rely on pairwise context scoring. Learn scalable approaches here.
- Run large models on small devices using Quantization techniques.
- Learn how to generate prediction intervals or sets with strong statistical guarantees for increasing trust using Conformal Predictions.
- Learn how to identify causal relationships and answer business questions using causal inference in this crash course.
- Learn how to scale and implement ML model training in this practical guide.
- Learn 5 techniques with implementation to reliably test ML models in production.
- Learn how to build and implement privacy-first ML systems using Federated Learning.
- Learn 6 techniques with implementation to compress ML models.
All these resources will help you cultivate key skills that businesses and companies care about the most.
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