#data science Cheatsheets

A concise cheat sheet for the scikit-learn library, covering essential functionalities for machine learning in Python. This guide includes key concepts, model selection, preprocessing techniques, and evaluation metrics with practical examples.

A comprehensive cheat sheet covering essential concepts, tools, and techniques in Data Science. It provides a quick reference for machine learning algorithms, data manipulation, statistical methods, and more.

A comprehensive cheat sheet covering fundamental machine learning concepts, algorithms, and techniques. Useful for quick reference and understanding key aspects of machine learning workflows.

A comprehensive cheat sheet for the Julia programming language, covering syntax, data types, control flow, functions, and more. Designed for quick reference and efficient learning.

✅ 1. Supervised Learning • Regression o Linear Regression o Logistic Regression o Polynomial Regression o Ridge Regression o Lasso Regression o ElasticNet o Support Vector Machines (SVM) o Decision Trees o Random Forest • Classification o Logistic Regression o K-Nearest Neighbors (KNN) o Support Vector Machines (SVM) o Decision Trees o Random Forest o Naive Bayes o Confusion Matrix o Stochastic Gradient Descent o Gradient Boosting o AdaBoost o XGBoost o LightGBM o CatBoost ________________________________________ 🔍 2. Unsupervised Learning • Clustering 🔹 1. Centroid-Based Clustering • K-Means • K-Medoids • Mean-Shift ________________________________________ 🔹 2. Density-Based Clustering • DBSCAN • OPTICS • HDBSCAN ________________________________________ 🔹 3. Hierarchical Clustering • Agglomerative Clustering • BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) • Affinity Propagation ________________________________________ 🔹 4. Distribution-Based Clustering • Gaussian Mixture Models (GMM) • Dimensionality Reduction o PCA (Principal Component Analysis) o t-SNE o UMAP o ICA (Independent Component Analysis) o LDA (Linear Discriminant Analysis) ________________________________________ 🔁 3. Semi-Supervised Learning • Self-Training • Label Propagation • Label Spreading ________________________________________ 🔄 4. Reinforcement Learning • Q-Learning • Deep Q-Networks (DQN) • SARSA • Policy Gradient Methods • Actor-Critic • Proximal Policy Optimization (PPO) • Deep Deterministic Policy Gradient (DDPG) ________________________________________ 🧠 5. Deep Learning Algorithms 🔹 1. Feedforward Networks (FNN) • Multilayer Perceptron (MLP) • Deep Neural Networks (DNN) ________________________________________ 🔹 2. Convolutional Neural Networks (CNN) • LeNet • AlexNet • VGGNet • GoogLeNet (Inception) • ResNet • DenseNet • EfficientNet • MobileNet • SqueezeNet ________________________________________ 🔹 3. Recurrent Neural Networks (RNN) • Vanilla RNN • Long Short-Term Memory (LSTM) • Gated Recurrent Unit (GRU) • Bidirectional RNN • Deep RNNs • Echo State Networks (ESN) ________________________________________ 🔹 4. Attention-Based Models / Transformers • Transformer • BERT • GPT (GPT-1, GPT-2, GPT-3, GPT-4) • RoBERTa • ALBERT • XLNet • T5 • DistilBERT • Vision Transformer (ViT) • Swin Transformer • DeiT • Performer • Longformer ________________________________________ 🔹 5. Autoencoders • Vanilla Autoencoder • Sparse Autoencoder • Denoising Autoencoder • Contractive Autoencoder • Variational Autoencoder (VAE) ________________________________________ 🔹 6. Generative Adversarial Networks (GANs) • Vanilla GAN • Deep Convolutional GAN (DCGAN) • Conditional GAN (cGAN) • CycleGAN • StyleGAN • Pix2Pix • BigGAN • StarGAN • WGAN (Wasserstein GAN) • WGAN-GP ________________________________________ 🔹 7. Reinforcement Learning (Deep RL) • Deep Q-Network (DQN) • Double DQN • Dueling DQN • Policy Gradient • REINFORCE • Actor-Critic • A3C (Asynchronous Advantage Actor-Critic) • PPO (Proximal Policy Optimization) • DDPG (Deep Deterministic Policy Gradient) • TD3 (Twin Delayed DDPG) • SAC (Soft Actor-Critic)