ML_Journey

By Ayush

Why This Exists

This is my personal lab notebook — a messy, evolving record of my machine learning journey.
I started this repo to:

Learn by building, not just reading.
Document experiments that often fail, sometimes work, and occasionally surprise me.
Create a reference I can revisit when I forget why learning_rate matters or how to debug overfitting.

No corporate buzzwords. Just code, math, and curiosity.

What You’ll Find

From Scratch: Implementations of algorithms (because understanding the basics feels like magic).
Real-World Projects: Messy pipelines on public datasets (Kaggle, research papers, etc.).
Experiments: Results of "What happens if I try...?" moments (spoiler: usually overfitting).
Notes: Quick references for concepts I’m actively learning (e.g., PCA, Bayesian optimization).

Think of this as my digital brain dump for ML.

How I Work

No strict rules: Add notebooks when I learn something new. Delete them if they’re nonsense.
No perfection: Code is messy where it doesn’t matter. Clean where it does.
No deadlines: This grows at my pace.

A Note to My Future Self

Dear Future Ayush,
When you look back here:

Celebrate progress, not perfection.
Revisit the notebooks that made you go "Oh right, that’s how that works!"
Keep adding the stuff you’re scared to admit you don’t know yet.

— Past Ayush

For Visitors

Feel free to borrow ideas, tweak code, or laugh at my early attempts.
If you find this useful, let me know — it’ll make my day.

"Learn by doing. Fail by trying. Grow by iterating."

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ML_Journey

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A Note to My Future Self

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Name		Name	Last commit message	Last commit date
Latest commit History 130 Commits
50_Q&A_in_ML.ipynb		50_Q&A_in_ML.ipynb
Adaboost_Hyperparameters.ipynb		Adaboost_Hyperparameters.ipynb
Adaboost_vs_GradientBoosting.ipynb		Adaboost_vs_GradientBoosting.ipynb
Assumptions_in_Linear_Regression.ipynb		Assumptions_in_Linear_Regression.ipynb
Bagging_Ensemble{Work}.ipynb		Bagging_Ensemble{Work}.ipynb
Bagging_VS_RandomForest_(ensemble).ipynb		Bagging_VS_RandomForest_(ensemble).ipynb
Bagging_Vs_Boosting.ipynb		Bagging_Vs_Boosting.ipynb
Bagging{Classifier_&_Regressor}.ipynb		Bagging{Classifier_&_Regressor}.ipynb
Bayesian_Analysis_in_ML.ipynb		Bayesian_Analysis_in_ML.ipynb
Bayesian_Data_Analysis{Cameo}.ipynb		Bayesian_Data_Analysis{Cameo}.ipynb
Bayesian_Linear_Regression.ipynb		Bayesian_Linear_Regression.ipynb
Bayesian_Optimization_in_ML.ipynb		Bayesian_Optimization_in_ML.ipynb
Bias_Variance_Tradeoff_in_Machine_Learning.ipynb		Bias_Variance_Tradeoff_in_Machine_Learning.ipynb
ChurnPrediction.ipynb		ChurnPrediction.ipynb
DBSCAN_Working.ipynb		DBSCAN_Working.ipynb
DecisionTreesAndRandomForests_learning.ipynb		DecisionTreesAndRandomForests_learning.ipynb
Dimensionality_Reduction{Unsupervised}.ipynb		Dimensionality_Reduction{Unsupervised}.ipynb
Elastic_Net_Regression.ipynb		Elastic_Net_Regression.ipynb
Ensemble_Learning_And_its_Types.ipynb		Ensemble_Learning_And_its_Types.ipynb
Essentials_of_Machine_Learning(Data_Leakage).ipynb		Essentials_of_Machine_Learning(Data_Leakage).ipynb
Evaluation_Metrics_for_Multi_Target_Classification.ipynb		Evaluation_Metrics_for_Multi_Target_Classification.ipynb
Evaluation_metrics_in_{Unsupervised_Learning}.ipynb		Evaluation_metrics_in_{Unsupervised_Learning}.ipynb
F_E(Binning&Binarization).ipynb		F_E(Binning&Binarization).ipynb
F_E(ColumnTransformer).ipynb		F_E(ColumnTransformer).ipynb
F_E(Date_And_Time).ipynb		F_E(Date_And_Time).ipynb
F_E(FunctionTransfomer).ipynb		F_E(FunctionTransfomer).ipynb
F_E(HandlingMixedVariables).ipynb		F_E(HandlingMixedVariables).ipynb
F_E(One_Hot_Encoding).ipynb		F_E(One_Hot_Encoding).ipynb
F_E(Ordinal_AND_Label_Encoding).ipynb		F_E(Ordinal_AND_Label_Encoding).ipynb
F_E(Pipelines).ipynb		F_E(Pipelines).ipynb
F_E(PowerTransfomer).ipynb		F_E(PowerTransfomer).ipynb
F_E(PrincipalComponentAnalysis).ipynb		F_E(PrincipalComponentAnalysis).ipynb
Fairness_In_ML.ipynb		Fairness_In_ML.ipynb
FeatureEngineering(Normalization).ipynb		FeatureEngineering(Normalization).ipynb
FeatureEngineering(Standardized).ipynb		FeatureEngineering(Standardized).ipynb
Feature_Construction_&_Feature_Splitting.ipynb		Feature_Construction_&_Feature_Splitting.ipynb
Feature_Importance_in_Machine_Learning.ipynb		Feature_Importance_in_Machine_Learning.ipynb
GradientBoostingClassification(Steps_&_intuition).ipynb		GradientBoostingClassification(Steps_&_intuition).ipynb
Gradient_Boosting_Machines_(GBMs)_with_XGBoost_learning.ipynb		Gradient_Boosting_Machines_(GBMs)_with_XGBoost_learning.ipynb
Gradient_Boosting_Process.ipynb		Gradient_Boosting_Process.ipynb
Gradient_Descent_From_Math_to_Implementation.ipynb		Gradient_Descent_From_Math_to_Implementation.ipynb
Gradient_Descent_in_ML.ipynb		Gradient_Descent_in_ML.ipynb
Graph_in_Bayesian_Framework.ipynb		Graph_in_Bayesian_Framework.ipynb
Heart_Disease_Prediction{0_99}.ipynb		Heart_Disease_Prediction{0_99}.ipynb
Hierarchical_Clustering_&_their_Types.ipynb		Hierarchical_Clustering_&_their_Types.ipynb
Hierarchical_Clustering{Unsupervised}.ipynb		Hierarchical_Clustering{Unsupervised}.ipynb
How_KNN_Works_{Supervised}.ipynb		How_KNN_Works_{Supervised}.ipynb
Imbalanced_data_CODE.ipynb		Imbalanced_data_CODE.ipynb
IrisDatasets{Clustering}.ipynb		IrisDatasets{Clustering}.ipynb
Iterative_Imputation_with_Linear_Regression.ipynb		Iterative_Imputation_with_Linear_Regression.ipynb
KMeans_Clustering_demo.ipynb		KMeans_Clustering_demo.ipynb
KMeans_from_Scratch.ipynb		KMeans_from_Scratch.ipynb
KNN(Imputation).ipynb		KNN(Imputation).ipynb
K_Nearest_Neighbors_(KNN).ipynb		K_Nearest_Neighbors_(KNN).ipynb
Kernel_Trick_SVM.ipynb		Kernel_Trick_SVM.ipynb
L1_vs_L2_Regularization_(Lasso_vs_Ridge).ipynb		L1_vs_L2_Regularization_(Lasso_vs_Ridge).ipynb
Lazy_Predict{Supervised}.ipynb		Lazy_Predict{Supervised}.ipynb
LearningRate_vs_Alpha_in_Algo(ML).ipynb		LearningRate_vs_Alpha_in_Algo(ML).ipynb
Leave_One_Out_Cross_Validation_In_ML.ipynb		Leave_One_Out_Cross_Validation_In_ML.ipynb
LinearRegression_learning.ipynb		LinearRegression_learning.ipynb
Linear_Regression_with_Gradient_Descent.ipynb		Linear_Regression_with_Gradient_Descent.ipynb
LogisticRegression_learning.ipynb		LogisticRegression_learning.ipynb
Logistic_vs_SVM.ipynb		Logistic_vs_SVM.ipynb
LogisticsRegression(Demo).ipynb		LogisticsRegression(Demo).ipynb
Loss_Function_vs_Cost_Function_in_Machine_Learning.ipynb		Loss_Function_vs_Cost_Function_in_Machine_Learning.ipynb
ML_01(California_Housing).ipynb		ML_01(California_Housing).ipynb
ML_02(Predicting_Customer_Churn_).ipynb		ML_02(Predicting_Customer_Churn_).ipynb
ML_03(Pancreatic_Cancer).ipynb		ML_03(Pancreatic_Cancer).ipynb
Machine_Learning_Using_R.ipynb		Machine_Learning_Using_R.ipynb
Mistake_Done_in_ML.ipynb		Mistake_Done_in_ML.ipynb
MultipleLinearRegression(CodeFromScratch).ipynb		MultipleLinearRegression(CodeFromScratch).ipynb
Naive_Bayes_in_ML.ipynb		Naive_Bayes_in_ML.ipynb
New_York_City_Taxi_Fare_Prediction.ipynb		New_York_City_Taxi_Fare_Prediction.ipynb
Optuna_basics_Working.ipynb		Optuna_basics_Working.ipynb
Outlier_Handling_in_ML.ipynb		Outlier_Handling_in_ML.ipynb
OutliersDetection.ipynb		OutliersDetection.ipynb
P_C_A(Demo[EMNIST]).ipynb		P_C_A(Demo[EMNIST]).ipynb
Polynomial_Logistic_Regression.ipynb		Polynomial_Logistic_Regression.ipynb
Predict_Calorie_Expenditure_[kaggle_Notebook_explain].ipynb		Predict_Calorie_Expenditure_[kaggle_Notebook_explain].ipynb
README.md		README.md
ROC_AUC in ML.ipynb		ROC_AUC in ML.ipynb
RandomForest(Tuning).ipynb		RandomForest(Tuning).ipynb
RandomForest{Working}.ipynb		RandomForest{Working}.ipynb
Regression_Tree_example.ipynb		Regression_Tree_example.ipynb
Ridge_Regularization.ipynb		Ridge_Regularization.ipynb
SHAP_(SHapley_Additive_exPlanations).ipynb		SHAP_(SHapley_Additive_exPlanations).ipynb
SimpleRegression(Own).ipynb		SimpleRegression(Own).ipynb
Softmax_Regression_in_ML.ipynb		Softmax_Regression_in_ML.ipynb
Stacking_and_Blending_in_ML.ipynb		Stacking_and_Blending_in_ML.ipynb
Unsupervised_Learning_and_Recommendations_with_Scikit_Learn_learning.ipynb		Unsupervised_Learning_and_Recommendations_with_Scikit_Learn_learning.ipynb
Unsupervised_Techniques.ipynb		Unsupervised_Techniques.ipynb
Voting_Ensemble(Classification&Regressor).ipynb		Voting_Ensemble(Classification&Regressor).ipynb
Xgboost_Classification_in_ML.ipynb		Xgboost_Classification_in_ML.ipynb
Xgboost_Regression_in_ML.ipynb		Xgboost_Regression_in_ML.ipynb
adaboost_demo.ipynb		adaboost_demo.ipynb
batch_gradient_descent.ipynb		batch_gradient_descent.ipynb
dtreeviz_demo.ipynb		dtreeviz_demo.ipynb
evaluation_metrics.ipynb		evaluation_metrics.ipynb
gradient_boosting_classification.ipynb		gradient_boosting_classification.ipynb
gradient_boosting_classification{Datasets}.ipynb		gradient_boosting_classification{Datasets}.ipynb

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