Day 30: K-Nearest Neighbors and Distance-Based ClassificationHello everyone, and welcome to Day 30 of “100 Days of Data Science”! Today, we’re wrapping up our journey with a deep dive into K-Nearest…14h ago14h ago
Day 29: Support Vector Machines — Concepts and Use-CasesHello everyone, and welcome to Day 29 of “100 Days of Data Science”! Today, we’re exploring Support Vector Machines (SVMs) — a powerful…1d ago1d ago
Day 28: Ensemble Methods — Random Forests ExplainedHello everyone, and welcome to Day 28 of “100 Days of Data Science”! Today, we’re diving into ensemble methods, focusing on Random Forests…2d ago2d ago
Day 27: Decision Trees — How They WorkHello, everyone! Imagine you’re navigating a series of choices, each leading you down a different path, ultimately guiding you to a…3d ago3d ago
Day 26: Hands-on with Logistic RegressionLet’s assume a world where every decision you make is backed by a clear, data-driven model — where predictions aren’t just guesses but…4d ago4d ago
Day 25: Classification Overview — Logistic RegressionSuppose a world where every decision is a fork in the road: one path leads to a positive outcome, the other to a negative one. Today…5d ago5d ago
Day 24: Regression Diagnostics — Residual Analysis and Error MetricsImagine if every model you built could give you honest, constructive feedback — telling you not just when it’s right, but also when and…6d ago6d ago
Day 23: Implementing Linear Regression in PythonHello everyone, and welcome to Day 23 of “100 Days of Data Science”! Yesterday, we discussed the theory behind linear regression — what it…Mar 27Mar 27
Day 22: Regression Analysis — Understanding Linear RegressionHello everyone, and welcome to Day 22 of “100 Days of Data Science”! Today, we’re diving into one of the most fundamental techniques in…Mar 26Mar 26
Day 21: Introduction to Machine Learning — Supervised vs. UnsupervisedHello everyone, and welcome to Day 21 of “100 Days of Data Science”! Today, we’re taking our first deep dive into the world of Machine…Mar 25Mar 25
