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

1. Introduction
2. General Machine Learning
3. Data Visualization
4. Forecasting
5. Natural Language Processing
6. Computer Vision
7. Recommendation Systems
8. Clustering
9. Reinforcement Learning
10. 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