Data Science Projects with Linear Regression, Logistic Regression, Random Forest, SVM, KNN, KMeans, XGBoost, PCA etc
InstructorKGP Talkie | Laxmi Kant
One sampled review suggests that the inclusion of Colab notebooks allows learners to follow along with full code implementations, from dataset loading through model evaluation.
The curriculum spans a wide range of techniques including Linear/Logistic Regression, SVM, KNN, Decision Trees, Random Forest, and K-Means clustering.
Instructional signals suggest the course focuses heavily on coding steps without sufficient explanation of why specific processes, such as scaling or one-hot encoding, are necessary. This signal predates the displayed course update; the update may have addressed it, but the update label does not prove that it was corrected.
A signal from late 2024 indicates that certain libraries used in the lessons may no longer be available or easily accessible; however, the displayed update date of August 2026 does not prove this has been corrected.
The curriculum aligns with the goal of teaching Python and ML basics to beginners.