Kaggle Kernels Compilation
Image Source: Wikimedia
Disclaimer: This compilation is still a work in progress.
- [
✔ ] Converting urls to text hyperlinks. - [
✔ ] Clean Kernel Titles. - [
✔ ] Write Introduction. - Better Topic Division.
Table of Contents
- Introduction
- General Machine Learning
- Data Visualization
- Forecasting
- Natural Language Processing
- Computer Vision
- Recommendation Systems
- Clustering
- Reinforcement Learning
- Competitions (Kernel Examples)
Introduction
In this compilation you will find curated Kaggle Kernels to aid on your Data Science Learning Journey. The list is weekly reviewed.
I wiil search on Kaggle for interesting and didactic Kernel, if is good will be added here.
If any author doesn't want his work on this compilation, open an issue and I will remove the requested Kernel.
Most Kernels are written in Python, but if any of them is written in R, the tag [R] will appear in the title end.
General Machine Learning
- Data Science Tutorial for Beginners
- Machine Learning Tutorial for Beginners
- Statistical Learning Tutorial for Beginners
- A Data Science Framework: To Achieve 99% Accuracy
- A Comprehensive ML Workflow with Python
- Guided path of Learning ML-DS
- Exploration of data step by step
- Python walkthrough for Titanic data analysis
- How to not overfit?
- OOP approach to FE and models
- Predictive Power Score vs Correlation
- Pseudo Labeling - QDA
- Bayesian Learning Basics | Tutorial
- Data Analysis using SQL:
- SQLalchemy and ML with sklearn demo
- Pyspark ML tutorial for beginners:
- Getting started with H2O (AutoML)
- XGBoost in H2O! (AutoML)
- CatBoost: A Deeper Dive
- GMEAN of low correlation
- Resampling strategies for imbalanced datasets
Deeep Learning
- Deep Learning Tutorial for Beginners
- In-Depth Guide to Convolutional Neural Networks
- Introduction to Pytorch (a very gentle start)
Data Visualization
- Creating a Good Analytics Report
- Visual data analysis in Python
- Patterns of Missing Data
- Basic EDA with Images
- Matplotlib Plotting Guide
- Plotting with Python: learn 80 plots STEP by STEP
- Seaborn and Plotly
- Seaborn Tutorial for Beginners
- Altair visualization: 2018 StackOverflow survey
- Plotly Tutorial for Beginners
- Visualization: Bokeh Tutorial Part 1
- Interactive Bokeh Tutorial Part 2
- Tutorial: Interactive data visualizations [R]
- Beginners guide to Highchart Visual in R
- Interactive Viz - UFC with Altair
- EDA using Pyviz
- Interactive Exploratory Data Analysis
- Advanced Pyspark for Exploratory Data Analysis
Forecasting
- Time Series Prediction Tutorial with EDA
- Back to (predict) the future - Interactive M5 EDA [R]
- EDA + Prophet + MLP Neural Network Forecasting
- Predicting stock movement
Natural Language Processing
- Data Science with DL & NLP: Advanced Techniques
- A mind map for of NLP
- NLP Cheatsheet - Master NLP
- Approaching (Almost) Any NLP Problem on Kaggle
- Regex Primer
- How to Preprocessing when using embeddings
- How To Preprocessing for GloVe Part 1: EDA
- How To Preprocessing for GloVe Part 2: Usage
- Gensim Word2Vec Tutorial
- Hitchhiker's Guide to NLP in spaCy
- Tutorial on topic modelling-LDA&NLP
- Treemap House of Horror: Spooky EDA/LDA/Features [R]
- Movie Review Sentiment Analysis EDA and models
- Loading BERT using pytorch (with tokenizer & apex)
- Text modelling in Pytorch v2
- CNN in keras on folds
- BERT for Humans: Tutorial+Baseline (Version 2)
- Clickbait News - BERT PyTorch
- Bert-base TF2.0 (now Huggingface transformer)
- DistilBert + Catalyst, amazon product reviews
- Vowpal Wabbit tutorial: blazingly fast learnin
Computer Vision
- Convolutional Neural Network (CNN) Tutorial
- Image classification with Convolutional Neural Networks (Fast.ai)
- A complete ML pipeline (Fast.ai)
- Introduction to CNN Keras
- Indian way to learn CNN
- Beginners guide to MNIST with fast.ai
- Practical Deep Learning Using PyTorch
- Classification in catalyst with utility scripts
- Pytorch utils for images
- RSNA Intracranial Hemorrhage Basic EDA + Data Visualization
- Severstal: Simple 2-step pipeline
- GAN Introduction
- Kuzushiji Recognition Complete Guide
- Train Simple XRay CNN
Recommendation Systems
- Recommendation Systems Tutorial
- How To Recommend Anything/Deep Recommender
- Tutorial: Collaborative filtering with PySpark
Clustering
- Unsupervised Learning: Clustering (Tutorial)
- In-depth EDA and K-Means Clustering
- Unsupervised learning: PCA and clustering
Reinforcement Learning
- Reinforcement Learning for Meal Planning in Python
- Learn by example Reinforcement Learning with Gym
- Crash Course in Reinforcement Learning
- Deep Reinforcement Learning on Stock Data
- RL from Scratch Part 1: Defining the Environment
- RL from Scratch Part 2: Understanding RL Parameters
Competitions (Kernel Examples)
- NFL Injury Analysis
- NFL Punt Analytics
- IEEE-CIS Fraud Detection
- Elo Merchant Category Recommendation
- Santander Customer Transaction Prediction
- Porto Seguro’s Safe Driver Prediction [R]
- Starter: ASHRAE Great Energy Predictor
- Vowpal Wabbit starter Microsoft Malware Prediction
- PUBG Data Exploration + Random Forest