Data Science Projects with Linear Regression, Logistic Regression, Random Forest, SVM, KNN, KMeans, XGBoost, PCA etc
InstructorKGP Talkie | Laxmi Kant
Coupon
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.