Machine-Learning-with-Python

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Practice and tutorial-style notebooks covering wide variety of machine learning techniques

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- FUNDING.yml
- Readme.md
- Self_optimizing_ML_simple_example.ipynb
- DecisionTrees_RandomForest_Classification.ipynb
- KNN_Classification.ipynb
- Logistic_Reg_sklearn_statsmodels.ipynb
- Logistic_Regression_Classification.ipynb
- Naive_Bayes_Classification.ipynb
- Readme.md
- Skewed_Logistic_Regression.ipynb
- Stochastic_grad_descent.ipynb
- Support_Vector_Machine_Classification.ipynb
- Affinity_Propagation.ipynb
- Clustering_metrics.ipynb
- Clustering_with_dim_reduction.ipynb
- DBScan_Clustering.ipynb
- Hierarchical_Clustering.ipynb
- k-means_clustering_GMM.ipynb
- K_Means_Clustering_Practice.ipynb
- Mean_Shift_Clustering.ipynb
- Principal Component Analysis.ipynb
- Readme.md
- Complexity_Learning_Analysis_Lending_Data.ipynb
- Complexity_learning_curve_Hastie_dataset.ipynb
- Readme.md
- adult_income_data.csv
- Classified Data
- College_Data
- Concrete_Data.xls
- Height_Weight.xlsx
- hypothyroid.csv
- loan_data.csv
- Mall_Customers.csv
- Readme.md
- Sample - Superstore.xls
- slump_test.csv
- titanic_test.csv
- titanic_train.csv
- USA_Housing.csv
- wine.data.csv
- winequality-red.csv
- - .gitignore
  - Readme.md
  - request_pred.py
  - requirements.txt
  - server_lm.py
  - training_housing.py
- - form.py
  - keras_server.py
  - nothing_much.py
  - Readme.md
  - requirements.txt
  - utils.py
- Readme.md
- conf.py
- index.rst
- Function approximation by linear model and deep network LOOP test.ipynb
- Function approximation by linear model and deep network.ipynb
- Linear_Regression_Methods.ipynb
- Multi-variate LASSO regression with CV.ipynb
- Polynomial regression - linear and neural network.ipynb
- Readme.md
- Regularized polynomial regression with linear and random sampling - LOOP.ipynb
- Regularized polynomial regression with linear and random sampling.ipynb
- Complexity_curve_example.PNG
- Height_Weight_file_picture.PNG
- ML-DS-cycle-1.png
- NN-model_process.png
- NN-RSM-flow.PNG
- pandas-site.PNG
- Readme.md
- st-1.PNG
- SVM-1.PNG
- - linearmodel.py
  - mlp.py
  - Readme.md
  - scalene-1.PNG
- Readme.md
- Class_MyLinearRegression.ipynb
- Class_MyLinearRegression.py
- Readme.md
- Test_MLR.ipynb
- - Boston_housing.csv
  - CSV_EX_1.csv
  - CSV_EX_2.csv
  - CSV_EX_3.csv
  - CSV_EX_blankline.csv
  - CSV_EX_skipfooter.csv
  - CSV_EX_skiprows.csv
  - dir.PNG
  - Housing_data.xlsx
  - How to read various sources in a DataFrame.ipynb
  - Pandas CSV vs. PyArrow parquet reading speed.ipynb
  - PDF table reading and processing demo.ipynb
  - Readme.md
  - Table_EX_1.txt
  - Table_tab_separated.txt
  - WDI-2016.pdf
- Advanced Pandas Operations.ipynb
- fdata.txt
- fnumpy.npy
- How fast are NumPy ops.ipynb
- Matplotlib_Seaborn_basics.ipynb
- Numpy_operations.ipynb
- Numpy_Pandas_Quick.ipynb
- Numpy_Reading.ipynb
- Pandas_iteration.ipynb
- Pandas_Operations.ipynb
- pdpipe-example.ipynb
- Readme.md
- Sample - Superstore.xls
- Speed-up-Numpy-Pandas-with-Numexpr.ipynb
- linear_model.py
- Overall-scheme.png
- readme.md
- test_linear_model.py
- Random_function_generator.ipynb
- Readme.md
- Symbolic regression classification generator.ipynb
- Symbolic_regression_classification_generator.py
- - AddedFeatures_campaign_sale.csv
  - Cleaned_campaign_sale.csv
  - Data_wrangling.ipynb
  - Dataviz.ipynb
  - ML-1.ipynb
  - office_supply.csv
  - office_supply_campaign_results.xlsx
  - readme.md
- Forest_Fire_Prediction.ipynb
- Readme.md
- Linear_Regression_Methods.ipynb
- Linear_Regression_Practice.ipynb
- Linear_regression_statistical_estimation.ipynb
- Multi-variate LASSO regression with CV.ipynb
- Random_Forest_Regression.ipynb
- Readme.md
- Regression_Diagnostics.ipynb
- Regularized polynomial regression with linear and random sampling.ipynb
- Robust Linear Regression.ipynb
- Support Vector Regression.ipynb
- - hello-world.py
  - is_prime_app.py
  - Readme.md
  - Streamlit-demo-one.py
- Readme.md
- GMM_generator.ipynb
- Readme.md
- Scikit-learn-data-generation.ipynb
- Symbolic_regression_classification_generator.py
- Synth_Time_series.ipynb
- Synthetic-Data-Generation.ipynb
- 8.14 - profile-DS-workflow.png
- cProfile.ipynb
- Readme.md
- Readme.md
- Timing-decorator-ML-optimization.ipynb
- ML-Python-utils.py
- Readme.md
.gitignore
_config.yml
Interactive ML-1.ipynb
Jupyter_Markdown_Primer.ipynb
LICENSE
README.md

# Installation Guide

1. Get the code

git clone https://github.com/tirthajyoti/Machine-Learning-with-Python

Downloads the entire project code from GitHub to your computer.

cd Machine-Learning-with-Python

Moves into the project folder you just downloaded.

2. Python

Easy Recommended

Prerequisites

Git Needed to download the project code from GitHub.
Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.

⚠️ This is a large repository, so this method may point to an internal sub-package rather than the actual core product. Check the full README as well.

pip install -r Deployment/Linear_regression/requirements.txt

Installs the Python libraries listed in requirements.txt (or similar).

jupyter notebook

Launches Jupyter in your browser so you can open and run the notebook (.ipynb) files.

✅ If it runs without errors and prints output in the terminal, it worked.