OpenLearn

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📚 Just completed "Task B: Rewrite a C-style `for(i=0; i<len; i++)` loop into a Pythonic `enumerate` loop" in ML League (1.5) on @OpenLearn_nitj! #Learning #Progress #OpenLearn code: online-python.com/AXORbw8g0E

📚 Just completed "Task A: Write a script that takes a sentence and prints it backwards without using a loop (Hint: Slicing [::-1] )" in ML League (1.5) on @OpenLearn_nitj! #Learning #Progress #OpenLearn code: online-python.com/yokFdxRfnO

📚 Just completed "Slicing & Indexing: Boolean masking" in ML League (1.5) on @OpenLearn_nitj! #Learning #Progress #OpenLearn #haneri_lya_dyage

📚 Just completed "Broadcasting: Adding a scalar to a vector without a loop" in ML League (1.5) on @OpenLearn_nitj! #Learning #Progress #OpenLearn #haneri_lya_dyage

Week 2 - day 6 Completed "Task B: Check the feature_importances_ attribute of your forest to see what truly drives the predictions" in ML League (1.5) on @OpenLearn_nitj! #Learning #Progress #OpenLearn

Week 2 - day 6 Completed "Task A: Train both a single Decision Tree and a Random Forest on the same dataset. Compare their performance on a "Test" set" in ML League (1.5) on @OpenLearn_nitj! #Learning #Progress #OpenLearn

Week 2, Day 5 @OpenLearn_NITJ ✅ Today, the focus shifted to decision trees. Wrapped up with the "Composition of a Perfect Wine" challenge—extracting Feature Importance to visualize exactly how the model makes decisions. @VatsalKhanna55, I have something for you 👇 #Python

📚 Just completed "The Array: Creating arrays" in ML League (1.5) on @OpenLearn_nitj! #Learning #Progress #OpenLearn #haneri_lya_dyage

Week 2, Day 4 @OpenLearn_NITJ ✅ Today was deep dive into the geometry of Machine Learning with K-Nearest Neighbors. 1) Learned KNN 2) Feature Scaling is non-negotiable 3) Used Elbow Plot to find the optimal hyperparameter. Looks like K=9 is the sweet spot for my dataset!🎯📉

📚 Just completed "Map, Filter, Reduce: Functional programming basics" in ML League (1.5) on @OpenLearn_nitj! #Learning #Progress #OpenLearn #haneri_lya_dyage

Week 2 - day 5 Completed "Task B: Experiment with min_samples_split and observe how it affects the complexity of the tree" in ML League (1.5) on @OpenLearn_nitj! #Learning #Progress #OpenLearn

Week 2 - day 5 Completed "Task A: Train a Decision Tree and export the visualization as a PNG" in ML League (1.5) on @OpenLearn_nitj! #Learning #Progress #OpenLearn

Week 2 - day 5 Completed "Splitting Criteria: How the tree decides which feature to split on first", "Tree Depth" and "Visualization: Using plot_tree to see the logic of your model" in ML League (1.5) on @OpenLearn_nitj! #Learning #Progress #OpenLearn

Week 2 Day 3 took...well, 3 days @OpenLearn_NITJ 😅 Finally wrapped up Logistic Regression. It sounds fancy, but it's basically teaching a computer to draw an 'S' curve instead of a straight line. Slow progress is still progress. #Python

Week 2 - day 4 Completed "Task B: Compare model accuracy on a dataset before and after using StandardScaler" in ML League (1.5) on @OpenLearn_nitj! #Learning #Progress #OpenLearn

Week 2 -day 4 Completed "Task A: Implement a basic KNN classifier from scratch using only NumPy (no Scikit-Learn)" in ML League (1.5) on @OpenLearn_nitj! #Learning #Progress #OpenLearn

Week 2 - day 3 Completed "The Sigmoid Function", "Evaluation Metrics: Confusion Matrix, Precision, Recall, and the F1-Score" and "Thresholding: Deciding when a 0.6 probability counts as a "Yes."" in ML League (1.5) on @OpenLearn_nitj! #Learning #Progress #OpenLearn

Week 2, Day 1 @OpenLearn_NITJ : DONE. ✅ Ended the day building my first Linear Regression model from scratch. Today's Stack: Scikit-Learn for the Model 📊Matplotlib for Visualization The Reality Check:I fed the model only Area to predict Price. 📉 Accuracy (R²): 0.27(27%) LOL😂

📚 Just completed "Functions" in ML League (1.5) on @OpenLearn_nitj! #Learning #Progress #OpenLearn #haneri_lya_dyage

Week 2 - day 2 Completed "L2 Regularization (Ridge)", "L1 Regularization (Lasso)" and "Elastic Net: The hybrid approach that combines L1 and L2 penalties" in ML League (1.5) on @OpenLearn_nitj! #Learning #Progress #OpenLearn