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Resources for Self Studying ML

ML-Self-Starter-Pack

Basic Stuff

  • Intro to Python

    • Article Series: https://www.programiz.com/python-programming
    • Course: https://www.udacity.com/course/introduction-to-python--ud1110
    • Intro Course: https://cognitiveclass.ai/courses/python-for-data-science

  • Numpy

    • Numpy in 10 Minutes: https://www.youtube.com/watch?v=xECXZ3tyONo&ab_channel=PythonProgrammer
    • All in One Article(No Self Promo): https://copyassignment.com/numpy-for-machine-learning-a-complete-guide/
    • Docs Padho

  • Pandas

    • All in One Article(No Self Promo): https://copyassignment.com/python-pandas-tutorial-complete-introduction/
    • Pandas in 10 Minutes: https://www.youtube.com/watch?v=iGFdh6_FePU&ab_channel=PythonProgrammerPythonProgrammer
    • Docs Padho

  • Scipy

    • Git Repo: https://github.com/AllenDowney/CompStats
    • Binder: https://mybinder.org/v2/gh/allendowney/compstats/master
    • Site Tutorial: https://allendowney.github.io/CompStats/tutorial
    • OP Talk: https://www.youtube.com/watch?v=He9MCbs1wgE&ab_channel=Enthought

  • Matplotlib

    • All in One Article(No Self Promo): https://copyassignment.com/matplotlib-python-a-beginners-walkthrough/
    • Quick Intro to Matplotlib: https://www.youtube.com/watch?v=nzKy9GY12yo&ab_channel=blondiebytes
    • Docs Padho

  • Seaborn

    • All in One Article(No Self Promo): https://copyassignment.com/seaborn-create-elegant-plots/
    • Article at TDS: https://towardsdatascience.com/data-visualization-using-seaborn-fc24db95a850
    • Docs Padho

  • Miscellaneous

    • SQL Intro Course: https://www.khanacademy.org/computing/computer-programming/sql
    • SQL Micro Course: https://www.kaggle.com/learn/advanced-sql
    • Intro to ML: https://copyassignment.com/machine-learning-a-gentle-introduction/

Applied ML - I (Algo Zoo)

  • Linear Regression

    • Working and Intuition
      • OLS Article(No Self Promo): https://copyassignment.com/linear-regression-machine-learning/
      • LR Video(Statquest): https://www.youtube.com/watch?v=nk2CQITm_eo
      • Multiple Regression: https://www.youtube.com/watch?v=zITIFTsivN8
    • Variants
      • Polynomial Regression: https://www.youtube.com/watch?v=QptI-vDle8Y
      • L1 Regularization(Lasso): https://www.youtube.com/watch?v=NGf0voTMlcs
      • L2 Regularization(Ridge): https://www.youtube.com/watch?v=Q81RR3yKn30
      • L1+L2 Regularization(ElasticNet): https://www.youtube.com/watch?v=1dKRdX9bfIo

  • Gradient Descent

    • Article(No Self Promo): https://copyassignment.com/gradient-descent-linear-regression/
    • Video: https://www.youtube.com/watch?v=sDv4f4s2SB8
    • SGD Clearly Explained: https://www.youtube.com/watch?v=vMh0zPT0tLI
    • Video: https://www.youtube.com/watch?v=1j4bERmqmOU
    • GD with Momentum: https://www.youtube.com/watch?v=G9dUDHktfXI
    • GD Variants: https://www.analyticsvidhya.com/blog/2021/03/variants-of-gradient-descent-algorithm/

  • Logistic Regression

    • Article(No Self Promo): https://copyassignment.com/logistic-regression-machine-learning/
    • Statquest: https://www.youtube.com/watch?v=yIYKR4sgzI8
    • Andrew Ng Part 1: https://www.youtube.com/watch?v=-la3q9d7AKQ
    • Andrew Ng Part 2: https://www.youtube.com/watch?v=t1IT5hZfS48

  • Decision Trees

    • Article(No Self Promo): https://copyassignment.com/decision-tree-machine-learning/
    • DT from Scratch: https://www.youtube.com/watch?v=LDRbO9a6XPU
    • Statquest: https://www.youtube.com/watch?v=_L39rN6gz7Y

  • Random Forest

    • Article(No Self Promo): https://copyassignment.com/random-forest-machine-learning/
    • Bagging: https://www.youtube.com/watch?v=2Mg8QD0F1dQ
    • RF Article: https://towardsdatascience.com/understanding-random-forest-58381e0602d2

  • kNN

    • Article(No Self Promo): https://copyassignment.com/k-nearest-neighbors-machine-learning/
    • Video: https://www.youtube.com/watch?v=UqYde-LULfs

  • SVM

    • SVM Video: https://www.youtube.com/watch?v=Lpr__X8zuE8
    • Custom Kernel Training(No Self Promo): https://krypticmouse.hashnode.dev/training-svm-over-custom-kernels

  • Naive Bayes

    • Video: https://www.youtube.com/watch?v=Q8l0Vip5YUw
    • Implementation: https://towardsdatascience.com/learning-by-implementing-gaussian-naive-bayes-3f0e3d2c01b2

  • Ensemble Learning

    • Stacking: https://www.youtube.com/watch?v=sBrQnqwMpvA
    • Blending and Stacking Implementation: https://www.youtube.com/watch?v=TuIgtitqJho
    • Boosting
      • AdaBoost: https://www.youtube.com/watch?v=9CPsYsB4OLI&t=117s
      • Gradient Boosting: https://www.youtube.com/watch?v=3CC4N4z3GJc&t=132s
      • XGBoost: https://www.youtube.com/watch?v=OtD8wVaFm6E

Applied ML - II (Model Optimization and Evaluation)

  • Evaluation Metrics

    • Regression Metrics: https://towardsdatascience.com/what-are-the-best-metrics-to-evaluate-your-regression-model-418ca481755b
    • Classification Metrics:
      • Metrics: https://towardsdatascience.com/14-popular-evaluation-metrics-in-machine-learning-33d9826434e4
      • ROC Curve: https://www.youtube.com/watch?v=z5qA9qZMyw0

  • Cross Validation

    • Statquest: https://www.youtube.com/watch?v=TIgfjmp-4BA
    • Tutorial: https://www.youtube.com/watch?v=L_dQrZZjGDg

  • Hyperparameter Tuning

    • Tutorial: https://www.youtube.com/watch?v=jY2v4q3TPbs
    • Article: https://towardsdatascience.com/hyperparameters-optimization-526348bb8e2d
    • HyperOpt Tutorial: https://towardsdatascience.com/automate-hyperparameter-tuning-for-your-models-71b18f819604
    • Optuna Tutorial: https://analyticsindiamag.com/hands-on-python-guide-to-optuna-a-new-hyperparameter-optimization-tool/
    • Ray-Tune Tutorial(Come Here after doing DL): https://medium.com/riselab/cutting-edge-hyperparameter-tuning-with-ray-tune-be6c0447afdf

  • Miscellaneous

    • Feature Engineering Micro Course: https://www.kaggle.com/learn/feature-engineering
    • Feature Selection: https://www.analyticsvidhya.com/blog/2020/10/feature-selection-techniques-in-machine-learning/
    • Handling Imbalanced Data: https://www.analyticsvidhya.com/blog/2017/03/imbalanced-data-classification/
    • Class Weights for Imbalance: https://www.youtube.com/watch?v=Kp31wfHpG2c&ab_channel=BhaveshBhatt
    • Imbalance Class Metrics: https://machinelearningmastery.com/tour-of-evaluation-metrics-for-imbalanced-classification/

Applied ML - III (Deep Learning and Time Series)

  • Deep Learning

    • Basic Intro Playlist: https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi
    • PyTorch
      • PyTorch 101: https://www.youtube.com/watch?v=_R-mvKBD5U8&list=PL98nY_tJQXZln8spB5uTZdKN08mYGkOf2
      • Udacity(Course PyTorch): https://www.udacity.com/course/deep-learning-pytorch--ud188
      • PyTorch Tutorials: https://pytorch.org/tutorials/
    • TensorFlow
      • Coursera(Audit all of them): https://www.coursera.org/specializations/deep-learning
      • Tensorflow Tutorials: https://www.tensorflow.org/tutorials
    • TensorBoard(PyTorch): https://pytorch.org/tutorials/intermediate/tensorboard_tutorial.html
    • PyTorch Lightning: https://towardsdatascience.com/from-pytorch-to-pytorch-lightning-a-gentle-introduction-b371b7caaf09

  • Time Series Forecasting

    • Handling Missing Data in Time Series: https://www.kaggle.com/juejuewang/handle-missing-values-in-time-series-for-beginners
    • Data Splitting in Time Series: https://medium.com/keita-starts-data-science/time-series-split-with-scikit-learn-74f5be38489e
    • Article: https://machinelearningmastery.com/simple-time-series-forecasting-models/
    • TSF Algos: https://machinelearningmastery.com/time-series-forecasting-methods-in-python-cheat-sheet/
    • FbProphet: https://blog.exploratory.io/an-introduction-to-time-series-forecasting-with-prophet-package-in-exploratory-129ed0c12112
    • NeuralProphet: https://towardsdatascience.com/facebooks-prophet-deep-learning-neuralprophet-76796aed1d86
    • LSTM for TSF: https://stackabuse.com/time-series-prediction-using-lstm-with-pytorch-in-python/

  • Hyperparameter Tuning for NN

    • Ray-Tune Tutorial(PyTorch): https://medium.com/riselab/cutting-edge-hyperparameter-tuning-with-ray-tune-be6c0447afdf
    • KerasTuner Tutorial(Tensorflow): https://www.tensorflow.org/tutorials/keras/keras_tuner

Natural Language Processing

  • NLP Basics

    • Text Preprocessing for NLP: https://towardsdatascience.com/text-preprocessing-in-natural-language-processing-using-python-6113ff5decd8
    • Intro to Word Embeddings: https://towardsdatascience.com/introduction-to-word-embeddings-4cf857b12edc
    • CountVectorizer: https://towardsdatascience.com/natural-language-processing-count-vectorization-with-scikit-learn-e7804269bb5e#:~:text=%23%20about%20count%20vectorization,call%20fit%20on%20the%20text
    • TFIDF Vectorizer: https://medium.com/@cmukesh8688/tf-idf-vectorizer-scikit-learn-dbc0244a911a
    • Subword Tokenization;Byte Pair Encoding: https://www.youtube.com/watch?v=zjaRNfvNMTs
    • SentencePiece Tokenizer: https://towardsdatascience.com/sentencepiece-tokenizer-demystified-d0a3aac19b15

  • Text Classification

    • Basic Text Classification using TFIDF: https://medium.com/swlh/text-classification-using-tf-idf-7404e75565b8
    • Text Classification using LSTM: https://towardsdatascience.com/multiclass-text-classification-using-lstm-in-pytorch-eac56baed8df
    • Transformers
      • Attention(Video): https://www.youtube.com/watch?v=W2rWgXJBZhU
      • Transformer NN(Video): https://www.youtube.com/watch?v=TQQlZhbC5ps
      • BERT(Video): https://www.youtube.com/watch?v=xI0HHN5XKDo
      • Text Classification using BERT: https://towardsdatascience.com/bert-text-classification-using-pytorch-723dfb8b6b5b

  • Beyond Text Classification

    • Topic Modelling: https://monkeylearn.com/blog/introduction-to-topic-modeling/#:~:text=Topic%20modeling%20is%20an%20unsupervised,characterize%20a%20set%20of%20documents.
    • POS Tagging: https://towardsdatascience.com/part-of-speech-tagging-for-beginners-3a0754b2ebba
    • Named Entity Recognition: https://medium.com/cogitotech/how-does-named-entity-recognition-work-ner-methods-f23201a69648#:~:text=Depending%20on%20the%20process%20has,identifying%20and%20locating%20the%20entities.
    • Entity Extraction using Transformers: https://chriskhanhtran.github.io/posts/named-entity-recognition-with-transformers/#:~:text=According%20to%20its%20definition%20on,categories%20such%20as%20person%20names%2C

  • Seq2Seq

    • Intro to Seq2Seq: https://www.youtube.com/watch?v=MqugtGD605k
    • Text Summarization: https://www.youtube.com/watch?v=dHHvdubDnYM
    • Machine Translation: https://pytorch.org/tutorials/intermediate/seq2seq_translation_tutorial.html

Computer Vision

  • CV Basics

    • How computer sees images: https://towardsdatascience.com/how-does-computer-understand-images-c1566d4537bf#:~:text=A%20computer%20sees%20an%20image%20as%200s%20and%201s.,smallest%20unit%20in%20an%20image.&text=When%20we%20take%20a%20digital,a%20different%20number%20of%20channels.
    • Intro to OpenCV: https://stackabuse.com/introduction-to-opencv-with-python/
    • Video Handling using OpenCV: https://learnopencv.com/read-write-and-display-a-video-using-opencv-cpp-python/
    • Image Classification using CNN: https://www.analyticsvidhya.com/blog/2019/10/building-image-classification-models-cnn-pytorch/
    • Image Classification using Tranfer Learning: https://learnopencv.com/image-classification-using-transfer-learning-in-pytorch/#:~:text=We%20use%20transfer%20learning%20to,ImageNet%20with%20millions%20of%20images.
    • Image Augmentation using Albumentations: https://heartbeat.fritz.ai/image-augmentations-with-albumentations-c1ca8fc78db7
    • Image Augmentation using TorchVision: https://www.youtube.com/watch?v=Zvd276j9sZ8

  • Object Detection

    • RCNN, FasterRCNN: https://towardsdatascience.com/understanding-fast-r-cnn-and-faster-r-cnn-for-object-detection-adbb55653d97
    • YOLOv5: https://towardsai.net/p/computer-vision/yolo-v5%E2%80%8A-%E2%80%8Aexplained-and-demystified
    • SSD: https://developers.arcgis.com/python/guide/how-ssd-works/#:~:text=Instead%20of%20using%20sliding%20window,an%20object%20within%20that%20region.
    • YOLOv5 on Custom Dataset: https://towardsdatascience.com/how-to-train-a-custom-object-detection-model-with-yolo-v5-917e9ce13208

  • Image Segmentation

    • Intro: https://www.analyticsvidhya.com/blog/2019/04/introduction-image-segmentation-techniques-python/
    • IS using Detectron2: https://www.kaggle.com/lewisgmorris/image-segmentation-using-detectron2

  • AutoEncoders

    • Intro: https://medium.com/pytorch/implementing-an-autoencoder-in-pytorch-19baa22647d1
    • Convolutional Autoencoders: https://analyticsindiamag.com/how-to-implement-convolutional-autoencoder-in-pytorch-with-cuda/
    • Variational Autoencoders: https://debuggercafe.com/getting-started-with-variational-autoencoder-using-pytorch/

  • GANS

    • Intro to GANS: https://www.youtube.com/watch?v=8L11aMN5KY8
    • DCGANS: https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html
    • CycleGANS: https://analyticsindiamag.com/hand-on-implementation-of-cyclegan-image-to-image-translation-using-pytorch/

  • Miscellaneous

    • Capsule Networks: https://medium.com/@RiterApp/capsule-networks-as-a-new-approach-to-image-recognition-345d4db0831
    • Transformer for Image Data: https://towardsdatascience.com/implementing-visualttransformer-in-pytorch-184f9f16f632

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Resources for Self Studying ML

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