50-Days-of-Machine-Learning

(★ 9)

50 Days of Machine Learning is an immersive project designed to guide learners through essential machine learning concepts and techniques, featuring daily hands-on exercises and real-world datasets.

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- Day 00 API To DataFrame.ipynb
- movies.csv
- Answer.xlsx
- aug_train.csv
- generative_ai_learning_resources.csv
- IPL Matches 2008-2020.csv
- working-with-csv.ipynb
- zomato.csv
- Day 2.ipynb
- train.json
- Day 03.ipynb
- Day 04.ipynb
- train.csv
- train.csv
- Univariate Analysis.ipynb
- Bivariate Analysis.ipynb
- train.csv
- output.html
- Pandas Profiling.ipynb
- train.csv
- Social_Network_Ads.csv
- Standardization.ipynb
- Normalization.ipynb
- wine_data.csv
- customer.csv
- Day 10 Ordinal Encoding.ipynb
- cars.csv
- Day 11 One-Hot Encoding.ipynb
- Column Transformer.ipynb
- covid_toy.csv
- - clf.pkl
  - ohe_embarked.pkl
  - ohe_sex.pkl
- pipe.pkl
- predict-using-pipeline.ipynb
- predict-without-pipeline.ipynb
- titanic-using-pipeline.ipynb
- titanic-without-using-pipeline.ipynb
- train.csv
- Function Transformer.ipynb
- train.csv
- concrete_data.csv
- Power Transformer.ipynb
- binarization.ipynb
- Day 16.ipynb
- train.csv
- Handling Mixed Variables.ipynb
- titanic.csv
- messages.csv
- orders.csv
- working-with-dates-and-time.ipynb
- Complete Case Analysis.ipynb
- data_science_job.csv
- arbitrary-value-imputation.ipynb
- mean-median-imputation.ipynb
- titanic_toy.csv
- frequent-value-imputation.ipynb
- missing-category-imputation.ipynb
- train.csv
- automatically-select-imputer-parameters.ipynb
- house-train.csv
- missing-indicator.ipynb
- random-sample-imputation.ipynb
- train.csv
- KNN Imputer.ipynb
- train.csv
- 50_Startups.csv
- step-by-step.ipynb
- Outlier Removal using Z-Score.ipynb
- placement.csv
- Outlier Removal using IQR Method.ipynb
- placement.csv
- Outlier Detection using Percentiles.ipynb
- weight-height.csv
- Feature Construction and Feature Splitting.ipynb
- train.csv
- pca_step_by_step (1).ipynb
- README.md
- placement.csv
- Simple Linear Regression.ipynb
- placement.csv
- Regression Metrics.ipynb
- code-from-scratch.ipynb
- multiple_linear_regression.ipynb
- animation.gif
- animation1.gif
- animation2.gif
- animation3.gif
- animation4.gif
- animation5.gif
- animation6.gif
- animation7.gif
- animation8.gif
- animation9.gif
- cost_function.html
- cost_function2.html
- gradient-descent-3d.ipynb
- gradient-descent-animation(both-m-and-b).ipynb
- gradient-descent-animation(onlyb).ipynb
- gradient-descent-code-from-scratch.ipynb
- gradient_descent_step_by_step.ipynb
- batch-gradient-descent.ipynb
- mini-batch-gradient-descent-from-scratch.ipynb
- mini_batch_contour_plot.gif
- stochastic-gradient-descent-animation.ipynb
- stochastic-gradient-descent-from-scratch.ipynb
- stochastic_animation_contour_plot.gif
- stochastic_animation_cost_plot.gif
- stochastic_animation_line_plot.gif
- polynomial-regression.ipynb
- Ridge Regularization.ipynb
- ridge-regression-from-scratch-m-and-b.ipynb
- ridge-regression-from-scratch.ipynb
- ridge-regression-gradient-descent.ipynb
- ridge-regression-key-understandings.ipynb
- lasso-regression-demo.ipynb
- lasso-regression-key-points.ipynb
- elastic-net-regression.ipynb
- gradient-descent.ipynb
- perceptron-trick-sigmoid.ipynb
- perceptron-trick.ipynb
- classification-metrics-binary.ipynb
- classification-metrics-multi-iris1.ipynb
- classification-metrics-multi-mnist1.ipynb
- polynomial-logistic-regression.ipynb
- softmax-demo.ipynb
- streamlit-viz-tool.py
- bagging_vs_random_forest.ipynb
- code-example-random-forest.ipynb
- feature-importance-in-sklearn.ipynb
- heart.csv
- how-feature-importance-is-calculated.ipynb
- oob-score-demo.ipynb
- random_forest_demo.ipynb
- rf_learning_tool.ipynb
- adaboost-hyperparameter.ipynb
- adaboost_demo.ipynb
- heart.csv
- Stacking.ipynb
- gradient_boost_step_by_step.ipynb
- app.py
- kmeans-clustering-demo.ipynb
- kmeans.py
- student_clustering.csv
- - dbscan_demo.ipynb
- - agglomerative-clustering.ipynb
  - hierarchical-clustering-with-python-and-scikit-learn-shopping-data.csv
README.md

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