ML Cheatsheet

Cheat sheet for ML models based on scikit-learn, TensorFlow, PyTorch, matplotlib, NumPy, and pandas.

Introduction

This repository contains a collection of example machine learning source codes for various ML frameworks and libraries such as scikit-learn, TensorFlow, PyTorch, matplotlib, NumPy, and pandas. The purpose of this cheatsheet is to provide a quick reference for students and developers to understand and implement various machine learning models and techniques.

Installation

To use the examples in this repository, you need to have Python installed on your machine. You can install the required libraries using pip:

pip install scikit-learn tensorflow torch matplotlib numpy pandas

To use the examples using torchvision and torchaudio, you can install them using pip:

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

Table of Contents

• ML Cheatsheet
  • Introduction
  • Installation
  • Table of Contents
  • scikit-learn
    • Classification
    • Regression
    • Clustering
    • Dimensionality Reduction
    • Model Evaluation and Selection
  • TensorFlow
    • Basic Operations
    • Neural Networks
    • Model Training
    • Model Evaluation
    • Model Saving and Loading
  • PyTorch
    • Basic Operations
    • Neural Networks
    • Model Training
    • Model Evaluation
    • Model Saving and Loading
  • matplotlib
    • Basic Plots
    • Advanced Plots
    • Customization
  • NumPy
    • Array Operations
    • Mathematical Operations
    • Linear Algebra
    • Random Sampling
  • pandas
    • DataFrame Operations
    • Data Cleaning
    • Data Aggregation
    • Time Series Analysis

scikit-learn

Classification

• classification/logistic_regression.py
• classification/decision_tree.py
• classification/random_forest.py
• classification/support_vector_machine.py

Regression

• regression/linear_regression.py
• regression/ridge_regression.py
• regression/decision_tree_regression.py
• regression/random_forest_regression.py

Clustering

• clustering/k_means.py
• clustering/hierarchical_clustering.py
• clustering/dbscan.py

Dimensionality Reduction

• dimensionality_reduction/pca.py
• dimensionality_reduction/lda.py

Model Evaluation and Selection

• model_evaluation/cross_validation.py
• model_evaluation/grid_search.py
• model_evaluation/random_search.py

TensorFlow

Basic Operations

• basic_operations/tensor_operations.py
• basic_operations/variables_and_constants.py

Neural Networks

• neural_networks/feedforward_nn.py
• neural_networks/convolutional_nn.py
• neural_networks/recurrent_nn.py

Model Training

• model_training/compile_and_train.py
• model_training/callbacks.py

Model Evaluation

• model_evaluation/evaluate_model.py
• model_evaluation/confusion_matrix.py

Model Saving and Loading

• model_saving_loading/save_and_load_model.py
• model_saving_loading/checkpoints.py

PyTorch

Basic Operations

• basic_operations/tensor_operations.py
• basic_operations/autograd.py

Neural Networks

• neural_networks/feedforward_nn.py
• neural_networks/convolutional_nn.py
• neural_networks/recurrent_nn.py

Model Training

• model_training/training_loop.py
• model_training/optimizers.py

Model Evaluation

• model_evaluation/evaluate_model.py
• model_evaluation/confusion_matrix.py

Model Saving and Loading

• model_saving_loading/save_and_load_model.py
• model_saving_loading/checkpoints.py

matplotlib

Basic Plots

• basic_plots/line_plot.py
• basic_plots/bar_chart.py
• basic_plots/histogram.py

Advanced Plots

• advanced_plots/subplots.py
• advanced_plots/3d_plot.py
• advanced_plots/heatmap.py

Customization

• customization/plot_styles.py
• customization/annotations.py
• customization/legends.py

NumPy

Array Operations

• array_operations/array_creation.py
• array_operations/array_manipulation.py

Mathematical Operations

• math_operations/basic_math.py
• math_operations/statistics.py

Linear Algebra

• linear_algebra/matrix_multiplication.py
• linear_algebra/eigenvalues.py

Random Sampling

• random_sampling/random_numbers.py
• random_sampling/distributions.py

pandas

DataFrame Operations

• dataframe_operations/creation.py
• dataframe_operations/selection_and_filtering.py

Data Cleaning

• data_cleaning/handling_missing_data.py
• data_cleaning/data_transformation.py

Data Aggregation

• data_aggregation/group_by.py
• data_aggregation/pivot_table.py

Time Series Analysis

• time_series/creating_time_series.py
• time_series/rolling_statistics.py