Reading time - 4 mins
1. Technical ML Section
Complete roadmap to learn Gradient Boosting in-depth.
The full article is HERE (reading time - 6 mins).
2. Career ML Section
3 tips to improve your ML portfolio to increase chances for a higher paying job.
1. Technical ML Section:
(For a more detailed discussion, read the full blog post!)
Gradient Boosting (GB) is one of the most important ML algorithms—especially for tabular data. But many people just fit GB models blindly without realizing the mistakes they make.
From my experience, a solid understanding of how GB actually works makes a huge difference in real-world projects.
Below is a structured roadmap to master GB and tune it effectively.
đź“Ś Step 1: Deep overview
Start with a well-structured intro covering the main GB principles.
đź“Ś Step 2: Deep Dive into Hyperparameters
This tutorial goes to the algo parameters and structure deeper including family of loss functions and weak learners.
đź“Ś Step 3: From-Scratch Implementation
You don’t need to write it from scratch, but reading this implementation example line by line will solidify your understanding of how theory is done in practice.
đź“Ś Step 4: Hyperparameters Visualization
The next great step to solidify understanding of how hyperparameters influence the fitting process is to see it visually. For that, these 2 demos do a great job:
đź“Ś Step 5: Hyperparameter Optimization
Learn how to properly tune GB using Random Search and Bayesian Optimization (Hyperopt) instead of relying on defaults.
đź“Ś Step 6: CatBoost, XGBoost, and LightGBM
Each has strengths and weaknesses. Understanding the differences helps in choosing the right one and answering ML interview questions.
đź“Ś Step 7: GB for Forecasting
GB is widely used in real-world multivariate forecasting systems—yet many tutorials skip this part. Learning it will give you an extra edge.
2. Career ML Section
3 tips to improve your ML portfolio
âś… Tip 1: Present project as a website, not as a GitHub repo
People are too busy to check the code in detail. You need:
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Visible clickable link in the CV
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Simple website about the project
âś… Tip 2: Deploy a live UI dashboard with real-time / batch inference
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Product sense (UI design is hard)
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End-to-end skills
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Ability to communicate results
Use Streamlit for simple UI Plotly Dash for complex UI
Here is an example how a dashboard can look like:
âś… Tip 3: Create an end-to-end application architecture with tools
This makes a shocking impression. It shows:
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ML Engineering skills (in demand)
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Ability to structure code
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T-shape skills profile
That is it for this week!
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