(10) – Into the Forest: How “Ensemble Methods” Supercharge Decision Trees
From Bagging to Boosting—discovering the wisdom of the crowd in Machine Learning. Last week in my Machine Learning Professional Certificate, we took…
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(9) – The “White Box” of AI: Demystifying Decision Trees
From If-Then logic to Information Gain—my deep dive into Module 9. Two weeks ago, I started Module 9 of my Machine Learning…
Stop Applying, Start Connecting: A Guide to the 2-Hour Job Search
“The 2-Hour Job Search” by Steve Dalton provides a data-driven, systematic approach to bypass the “black hole” of online job applications. Most…
(8) – Tell Me Who Your Neighbors Are: The Power and Pitfalls of k-NN
Learning from similarity, the “Curse of Dimensionality,” and why you should always scale your data. This week marks the beginning of Phase…
(7) – Leveling Up: Surviving Imbalanced Data and Advanced Model Evaluation
How to handle rare events, master k-fold cross-validation, and the million-dollar Zillow prize. Last week, I reached the final instructional module of…
(6) – The Judge and Jury: Evaluating How Good Your Model Actually Is
From Confusion Matrix to Lift Charts—my deep dive into measuring predictive performance. This week, I tackled Module 6: Basics of Predictive Performance…
Docker, n8n, and Gemini: Building the Ultimate Job Hunting Machine
How I built a workflow that fetches emails, analyzes fit against my CV, and writes cover letters while I sleep. The Problem:…
(5) – The Bias-Variance Balancing Act: Can Machines Actually Learn?
Why memorizing isn’t learning, and how to spot a model that’s cheating. In Module 5 of my Machine Learning Professional Certificate, I…
(4) – The Statistics Survival Guide: Probing the Data and Quantifying the Unknown
From the Challenger disaster to Bootstrapping: How stats keep ML models honest In Module 4 of my Machine Learning journey, I moved…
Web Cleaners: The Hidden Side of Generative AI
I watched that YouTube video from a Swiss media called RTS and I wanted to share that as I found it very…