My list of Kaggle Best Notebooks — Topic wise ( Data Science and Machine Learning) — Part 2
Part 2— Notebooks from which you will learn the most…
Welcome back peeps! Today with this I’m gonna open Kaggles’ pandora’s box — MY list of Kaggle Best Notebooks — each topic wise for Data Science and Machine Leaning — Part 2.
System Design Case Studies — In Depth
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Mega Compilation : Solved System Design Case studies
Part 1 of this series can be found here —
I have been participating in the Kaggle competitions for past 4.5 years during my free time and it’s been an incredible learning curve. As much as I loved writing my own solution to the problems on the platform, I thoroughly went through some of the top notebooks only to find the gems hidden beneath. Thank you to the amazing community of Kaggle ( especially the star notebooks) — I have learned so much and implemented those learnings at my job.
Some of the other best Series —
How to solve any System Design Question ( approach that you can take)?
30 days of Data Structures and Algorithms and System Design Simplified
100 days : Your Data Science and Machine Learning Degree Series with projects
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Projects Videos —
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In this post, I’ll share with you the best notebooks on Kaggle( according to me) from which you can learn the most and exponentially speed up your learning curve in data science and ML field.
Highly Recommended Data Science and Machine Learning Courses that you MUST take ( with certificate) —
Find best data science and data engineering courses here
Find best Machine Learning and Deep Learning courses here
Disclaimer : This is my list that I’m just sharing so that people who are getting started in the field of Data Science and ML don’t fall in the rabbit hole with overwhelming information out there. Remember learning is a three step process — one what do you want to learn and second from where you want to learn and third implement what you learned.
Lets’s dive in!
Random Forests
- Star Notebook : https://www.kaggle.com/code/prashant111/random-forest-classifier-tutorial
- Star Notebook : https://www.kaggle.com/code/arthurtok/feature-ranking-rfe-random-forest-linear-models
- Star Notebook : https://www.kaggle.com/code/dansbecker/random-forests
- https://www.kaggle.com/code/benhamner/random-forest-benchmark-r
- https://www.kaggle.com/code/zlatankr/titanic-random-forest-82-78
60 days Project based Data Science and ML ( with implemented projects): Mega Compilation —
ROC Curve
- Star Notebook: https://www.kaggle.com/code/kanncaa1/roc-curve-with-k-fold-cv
- https://www.kaggle.com/code/nirajvermafcb/comparing-various-ml-models-roc-curve-comparison
- https://www.kaggle.com/code/philschmidt/quora-eda-model-selection-roc-pr-plots
Part 1 and 2 ( Day 1- 71 ) of Data Science and ML series can be found here —
Boosting
- https://www.kaggle.com/code/faressayah/ensemble-ml-algorithms-bagging-boosting-voting
- https://www.kaggle.com/code/fahadmehfoooz/boosting-without-sklearn
Optimization Techniques
- Star Notebook : https://www.kaggle.com/code/clair14/tutorial-bayesian-optimization
- https://www.kaggle.com/code/tilii7/bayesian-optimization-of-xgboost-parameters
- https://www.kaggle.com/code/sionek/bayesian-optimization
- https://www.kaggle.com/code/hengzheng/bayesian-optimization-seed-blending
- https://www.kaggle.com/code/corochann/optuna-tutorial-for-hyperparameter-optimization
- https://www.kaggle.com/code/rhtsingh/speeding-up-transformer-w-optimization-strategies
Data Pre-processing and Data Visualization : Mega Compilation
Principal Component Analysis
- Star Notebook : https://www.kaggle.com/code/nirajvermafcb/principal-component-analysis-explained
- https://www.kaggle.com/code/kanncaa1/tutorial-pca-intuition-and-image-completion
- https://www.kaggle.com/code/arthurtok/principal-component-analysis-with-kmeans-visuals
- https://www.kaggle.com/code/nirajvermafcb/principal-component-analysis-with-scikit-learn
- https://www.kaggle.com/code/arthurtok/a-cluster-of-colors-principal-component-analysis
Encoding Techniques
- Star Notebook : https://www.kaggle.com/code/dansbecker/using-categorical-data-with-one-hot-encoding
- https://www.kaggle.com/code/shahules/an-overview-of-encoding-techniques
- https://www.kaggle.com/code/ogrellier/python-target-encoding-for-categorical-features
- https://www.kaggle.com/code/rtatman/data-cleaning-challenge-character-encodings
- https://www.kaggle.com/code/avanwyk/encoding-cyclical-features-for-deep-learning
- https://www.kaggle.com/code/kabure/eda-feat-engineering-encode-conquer
Complete Pandas and techniques : Mega Compilation
BERT
- Start Notebook : https://www.kaggle.com/code/gunesevitan/nlp-with-disaster-tweets-eda-cleaning-and-bert
- https://www.kaggle.com/code/xhlulu/disaster-nlp-keras-bert-using-tfhub
- https://www.kaggle.com/code/parulpandey/eda-and-preprocessing-for-bert
- https://www.kaggle.com/code/abhinand05/bert-for-humans-tutorial-baseline
Recurrent Neural Network
- https://www.kaggle.com/code/kanncaa1/recurrent-neural-network-with-pytorch
- https://www.kaggle.com/code/thebrownviking20/intro-to-recurrent-neural-networks-lstm-gru
- https://www.kaggle.com/code/mayer79/rnn-starter-for-huge-time-series
LSTM
- https://www.kaggle.com/code/faressayah/stock-market-analysis-prediction-using-lstm
- https://www.kaggle.com/code/shivamb/beginners-guide-to-text-generation-using-lstms
- https://www.kaggle.com/code/amirrezaeian/time-series-data-analysis-using-lstm-tutorial
- https://www.kaggle.com/code/ngyptr/lstm-sentiment-analysis-keras
- https://www.kaggle.com/code/theoviel/deep-learning-starter-simple-lstm
- https://www.kaggle.com/code/johanvandenheuvel/lstm-model-of-stockdata
- https://www.kaggle.com/code/raoulma/ny-stock-price-prediction-rnn-lstm-gru
- https://www.kaggle.com/code/kunwar31/simple-lstm-with-identity-parameters-fastai
- https://www.kaggle.com/code/eashish/bidirectional-gru-with-convolution
- https://www.kaggle.com/code/bminixhofer/simple-lstm-pytorch-version
Collaborative Filtering
- https://www.kaggle.com/code/jhoward/collaborative-filtering-deep-dive
- https://www.kaggle.com/code/kanncaa1/recommendation-systems-tutorial
- https://www.kaggle.com/code/philippsp/book-recommender-collaborative-filtering-shiny
- https://www.kaggle.com/code/gspmoreira/recommender-systems-in-python-101
- https://www.kaggle.com/code/ajmichelutti/collaborative-filtering-on-anime-data
Complete Python with Projects : Mega Compilation
Convolution Neural Network
- https://www.kaggle.com/code/kanncaa1/convolutional-neural-network-cnn-tutorial
- https://www.kaggle.com/code/rajmehra03/flower-recognition-cnn-keras
- https://www.kaggle.com/code/shahules/getting-started-with-cnn-and-vgg16
- https://www.kaggle.com/code/uysimty/keras-cnn-dog-or-cat-classification
LGBM
- https://www.kaggle.com/code/hjd810/keras-lgbm-aug-feature-eng-sampling-prediction
- https://www.kaggle.com/code/kellibelcher/tps-may-2022-eda-lgbm-neural-networks
- https://www.kaggle.com/code/its7171/lgbm-with-loop-feature-engineering
- https://www.kaggle.com/code/ryches/simple-lgbm-solution
- https://www.kaggle.com/code/kyakovlev/ieee-simple-lgbm
- https://www.kaggle.com/code/hijest/lgbm-nn-ensemble-2-0
Pytorch
- https://www.kaggle.com/code/kanncaa1/pytorch-tutorial-for-deep-learning-lovers
- https://www.kaggle.com/code/pestipeti/pytorch-starter-fasterrcnn-train
- https://www.kaggle.com/code/hung96ad/pytorch-starter
- https://www.kaggle.com/code/abhishek/pytorch-bert-inference
- https://www.kaggle.com/code/pestipeti/pytorch-starter-fasterrcnn-inference
- https://www.kaggle.com/code/khyeh0719/pytorch-efficientnet-baseline-train-amp-aug
- https://www.kaggle.com/code/carloalbertobarbano/vgg16-transfer-learning-pytorch
- https://www.kaggle.com/code/artgor/pytorch-approach
Object Detection
- Star Notebook : https://www.kaggle.com/code/tanulsingh077/end-to-end-object-detection-with-transformers-detr
- Star Notebook : https://www.kaggle.com/code/aryaprince/getting-started-with-object-detection-with-pytorch
- https://www.kaggle.com/code/aruchomu/yolo-v3-object-detection-in-tensorflow
- https://www.kaggle.com/code/xhlulu/intro-to-tf-hub-for-object-detection
- https://www.kaggle.com/code/artgor/object-detection-with-pytorch-lightning
- https://www.kaggle.com/code/artkulak/2class-object-detection-inference-with-filtering
Forecasting and Time Series
- Star Notebook : https://www.kaggle.com/code/kashnitsky/topic-9-part-1-time-series-analysis-in-python/notebook
- https://www.kaggle.com/code/kashnitsky/topic-9-part-2-time-series-with-facebook-prophet/notebook
- https://www.kaggle.com/code/kanncaa1/time-series-prediction-tutorial-with-eda
- https://www.kaggle.com/code/headsortails/wiki-traffic-forecast-exploration-wtf-eda
- https://www.kaggle.com/code/zoupet/predictive-analysis-with-different-approaches
- https://www.kaggle.com/code/parulpandey/getting-started-with-time-series-using-pandas
Tech Interview — Mega Compilation
Dimensionality Reduction
- https://www.kaggle.com/code/arthurtok/interactive-intro-to-dimensionality-reduction/notebook
- https://www.kaggle.com/code/tilii7/dimensionality-reduction-pca-tsne
- https://www.kaggle.com/code/arthurtok/interactive-intro-to-dimensionality-reduction
- https://www.kaggle.com/code/wassimchouchen/clustering-using-dimensionality-reduction
- https://www.kaggle.com/code/frankmollard/dimensionality-reduction-3d-interactive
- https://www.kaggle.com/code/eliotbarr/dimensionality-reduction-explained
- https://www.kaggle.com/code/remidi/dimensionality-reduction-techniques
- https://www.kaggle.com/code/residentmario/dimensionality-reduction-and-pca-for-fashion-mnist
- https://www.kaggle.com/code/paulrohan2020/tutorial-dimensionality-reduction-pca-maths
Natural Language Processing
- Star Notebook : https://www.kaggle.com/code/pavansanagapati/knowledge-graph-nlp-tutorial-bert-spacy-nltk
- Star Notebook : https://www.kaggle.com/code/tanulsingh077/deep-learning-for-nlp-zero-to-transformers-bert
- https://www.kaggle.com/code/jhoward/getting-started-with-nlp-for-absolute-beginners
- https://www.kaggle.com/code/abhishek/approaching-almost-any-nlp-problem-on-kaggle/notebook
- https://www.kaggle.com/code/zikazika/natural-language-processing-theory-and-practice
- https://www.kaggle.com/code/shivan118/natural-language-processing-master
- https://www.kaggle.com/code/ravichaubey1506/natural-language-processing-with-python
- https://www.kaggle.com/code/miguelfzzz/natural-language-processing-rnn-lstm-s
Text Mining
- Star Notebook : https://www.kaggle.com/code/shivamb/data-science-glossary-on-kaggle
- https://www.kaggle.com/code/kanncaa1/applying-text-mining
- https://www.kaggle.com/code/artgor/text-modelling-in-pytorch
- https://www.kaggle.com/code/ambarish/fun-in-text-mining-with-simpsons
- https://www.kaggle.com/code/eliotbarr/text-mining-with-sklearn-keras-mlp-lstm-cnn
- https://www.kaggle.com/code/ekrembayar/f-r-i-e-n-d-s-text-mining-data-visualization
- https://www.kaggle.com/code/michau96/harry-potter-and-the-text-mining
- https://www.kaggle.com/code/imanjowkar/spam-detection-with-text-mining-and-ml-methods
- https://www.kaggle.com/code/xvivancos/market-basket-analysis
- https://www.kaggle.com/code/xvivancos/analyzing-star-wars-movie-scripts
Convolution Filters
Most Popular System Design Questions — Mega Compilation
Transformers
- https://www.kaggle.com/code/dschettler8845/transformers-course-chapter-2-tf-torch/notebook
- https://www.kaggle.com/code/maroberti/fastai-with-transformers-bert-roberta
- https://www.kaggle.com/code/adityaecdrid/pytorch-demystifying-transformers
- https://www.kaggle.com/code/manabendrarout/transformers-classifier-method-starter-train
- https://www.kaggle.com/code/claverru/demystifying-transformers-let-s-make-it-public
- https://www.kaggle.com/code/datafan07/disaster-tweets-nlp-eda-bert-with-transformers
- https://www.kaggle.com/code/vad13irt/optimization-approaches-for-transformers
- https://www.kaggle.com/code/odins0n/jax-flax-tf-data-vision-transformers-tutorial
- https://www.kaggle.com/code/yutanakamura/dear-pytorch-lovers-bert-transformers-lightning
- https://www.kaggle.com/code/officialshivanandroy/transformers-generating-titles-from-abstracts
- https://www.kaggle.com/code/ligtfeather/bottleneck-transformers-for-visual-recognition
- https://www.kaggle.com/code/faressayah/sentiment-model-with-tensorflow-transformers
- https://www.kaggle.com/code/abhilash1910/nlp-workshop-playing-with-transformers
Deep Learning
- Star Notebook : https://www.kaggle.com/code/kanncaa1/deep-learning-tutorial-for-beginners
- https://www.kaggle.com/code/rohanrao/a-deep-learning-of-deep-learning/notebook
- https://www.kaggle.com/code/dansbecker/deep-learning-from-scratch
- https://www.kaggle.com/code/theoviel/deep-learning-starter-simple-lstm
- https://www.kaggle.com/code/tanulsingh077/deep-learning-for-nlp-zero-to-transformers-bert
- https://www.kaggle.com/code/dimitreoliveira/deep-learning-for-time-series-forecasting
- https://www.kaggle.com/code/ranjeetjain3/visualization-machine-learning-deep-learning
- https://www.kaggle.com/code/bulentsiyah/deep-learning-based-semantic-segmentation-keras
- https://www.kaggle.com/code/omershect/learning-pytorch-lstm-deep-learning-with-m5-data
Implemented Projects : Mega Compilation
CycleGANs
- https://www.kaggle.com/code/jesperdramsch/understanding-and-improving-cyclegans-tutorial/notebook
- https://www.kaggle.com/code/dimitreoliveira/improving-cyclegan-monet-paintings
- https://www.kaggle.com/code/yushg123/introduction-to-gans-with-keras
- https://www.kaggle.com/code/jesperdramsch/getting-started-with-standard-gans-tutorial
- https://www.kaggle.com/code/upamanyumukherjee/cyclegan
Part 3 : Coming Soon!
30 days of Data Analytics Series —
Day 1 : Data Analytics basics and kickstart of Data analytics with projects series
Day 3 : Data Analytics Ecosystem — Data Life Cycle, Data Analysis complete process ( most important things)
Day 5 : Statistics
Day 6 : Basic and Advanced SQL
Day 8 : Pandas and Numpy
Day 9 : Data Manipulation
Day 10 : Data Visualization — Part 1
Day 11 : Project 1 : Data Visualization — Part 2
Day 12 : Data Visualization — Part 3
Day 13: Tableau — Part 1
Day 14: Tableau — Part 2
Day 15: Tableau — Part 3
Day 16 : Data Analysis Project 2
Day 17 : Data Analysis Project 3
Day 18: Data Analysis Project 4
Day 20 : Data Analysis Project 6
Day 21 : Data Analysis Project 7
Take Complete Hands On Tableau Course : Link
Quick Recap — Most Important Projects, Data Science, Machine Learning, Programming Tricks and Techniques
Writing Efficient and Optimized Python Code
Writing Efficient Python Code — Part 2
Use these hacks and techniques…
medium.datadriveninvestor.com
Big Query SQL and Linux
Some of the links are affiliates.
Happy learning and Kaggling :)
Follow for more updates, stay tuned and of-course let me end this post with a quote by Steve Jobs ;)
“Stay hungry. Stay foolish.”
