| A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | Level 1: Data Science | Level 2: Machine Learning | Level 3: General Deep Learning (Part 1) | Level 4: Specific Deep Learning (Part 2) | Level 5: Working in Production | |||||||||||||||||||||
2 | Skill | Video | Article | Code | Skill | Video | Article | Code | Skill | Video | Article | Code | Skill | Video | Article | Code | Skill | Video | Article | Code | ||||||
3 | Data Gathering | Machine Learning Supervised | Key Points | Computer Vision (CV) | Deployment | |||||||||||||||||||||
4 | Web Scrapping | https://www.youtube.com/watch?v=zXif_9RVadI&list=PL5-da3qGB5IDbOi0g5WFh1YPDNzXw4LNL&index=2 | Your first steps | https://www.youtube.com/watch?v=cKxRvEZd3Mw&list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal | https://colab.research.google.com/drive/1rc6E2tSfOIh3OHn_syN8tyZwbFYjreXd | ANNs | https://www.youtube.com/watch?v=_HB0RPuTIgI&t=3478s | https://machinelearningmastery.com/multi-class-classification-tutorial-keras-deep-learning-library/ | image classification | https://www.youtube.com/watch?v=u2TjZzNuly8&list=PLQY2H8rRoyvwLbzbnKJ59NkZvQAW9wLbx&index=8 | Flask Rest API | https://blog.keras.io/building-a-simple-keras-deep-learning-rest-api.html | ||||||||||||||
5 | Kaggle Import | https://www.youtube.com/watch?v=eEgZtNOCJhk | https://gist.github.com/githubssd/ac0b477df0284f157aa44b9b8e16dcaf | Train, Test, Valid | https://www.youtube.com/watch?v=Zi-0rlM4RDs&t=235s | Backpropagation | https://www.youtube.com/watch?v=IHZwWFHWa-w | Bias & Interpretability | https://www.youtube.com/watch?v=5zpNeGQBv2o | Redis + Flask Scalable API | https://www.pyimagesearch.com/2018/01/29/scalable-keras-deep-learning-rest-api/ | |||||||||||||||
6 | Upload to Colab | https://www.youtube.com/watch?v=SHYAQHDQoU41 | Linear reg. | https://www.youtube.com/watch?v=i_tZ7CQx7FA&list=PLOyG6yVJdOXmZA0vkimYdaAENja1UGKB3&index=5 | Activation Functions | https://www.youtube.com/watch?v=9vB5nzrL4hY | https://towardsdatascience.com/deep-learning-which-loss-and-activation-functions-should-i-use-ac02f1c56aa8 | Object Detection (YOLO) | https://www.youtube.com/watch?v=NM6lrxy0bxs | https://www.kdnuggets.com/2018/05/implement-yolo-v3-object-detector-pytorch-part-1.html | https://pjreddie.com/darknet/yolo/ | Android ML with TFLite | https://www.youtube.com/watch?v=JnhW5tQ_7Vo&list=PLQY2H8rRoyvwLbzbnKJ59NkZvQAW9wLbx&index=37 | |||||||||||||
7 | from url / API | use wget | Log reg. | https://www.youtube.com/watch?v=SBBjPUuFvJU&list=PLOyG6yVJdOXmZA0vkimYdaAENja1UGKB3&index=6 | Learning rate | https://www.youtube.com/watch?v=jWT-AX9677k | human pose estimation | https://www.learnopencv.com/deep-learning-based-human-pose-estimation-using-opencv-cpp-python/ | Swift ML with TF | https://www.youtube.com/watch?v=s65BigoMV_I | https://github.com/tensorflow/swift-models | |||||||||||||||
8 | extract from zip | use unzip | Classification algos | https://www.youtube.com/watch?v=CtKeHnfK5uA&list=PL_Nji0JOuXg2udXfS6nhK3CkIYLDtHNLp&index=2 | Make your Model Better | Mask RCNN | https://www.analyticsvidhya.com/blog/2018/07/building-mask-r-cnn-model-detecting-damage-cars-python/ | https://github.com/matterport/Mask_RCNN | TFX | https://www.youtube.com/watch?v=Mxk4qmO_1B4&list=PLQY2H8rRoyvwLbzbnKJ59NkZvQAW9wLbx&index=15 | ||||||||||||||||
9 | From Drive | https://www.youtube.com/watch?v=Gvwuyx_F-28 | Ensembling | https://www.youtube.com/watch?v=m-S9Hojj1as | Under & Over Fitting | https://www.youtube.com/watch?v=GMrTBtzJkCg&index=2&t=0s&list=PLQY2H8rRoyvwLbzbnKJ59NkZvQAW9wLbx | image segmentation | https://hackernoon.com/how-to-use-detectron-facebooks-free-platform-for-object-detection-9d41e170bbcb | federated learning | https://www.youtube.com/watch?v=1YbPmkChcbo | ||||||||||||||||
10 | From csv file | https://www.youtube.com/watch?v=5_QXMwezPJE&list=PL5-da3qGB5ICCsgW1MxlZ0Hq8LL5U3u9y&index=2 | Summary of ML | https://www.youtube.com/watch?v=LOD4PvvQ5js&t=2147s | https://medium.com/edureka/artificial-intelligence-algorithms-fad283a0d8e2 | Lear. rate Finder | https://www.youtube.com/watch?v=U1aPRX_SIZM | https://www.pyimagesearch.com/2019/08/05/keras-learning-rate-finder/ | Natural Language Processing (NLP) | CV raspberry | https://www.youtube.com/watch?v=aimSGOAUI8Y | |||||||||||||||
11 | open BIG file (gigs) | https://www.youtube.com/watch?v=dqXXG2Wcio0 | Machine Learning Unsupervised | Lear. r Decay & Schedule | https://www.pyimagesearch.com/2019/07/22/keras-learning-rate-schedules-and-decay/ | tokenization | https://www.youtube.com/watch?v=BO4g2DRvL6U&list=PLQY2H8rRoyvwLbzbnKJ59NkZvQAW9wLbx&index=31 | Web App tensorflow.js | https://www.youtube.com/watch?v=YB-kfeNIPCE | |||||||||||||||||
12 | Remove duplicates | https://www.pyimagesearch.com/2020/04/20/detect-and-remove-duplicate-images-from-a-dataset-for-deep-learning/ | KMeans | https://www.youtube.com/watch?v=4b5d3muPQmA | https://medium.com/datadriveninvestor/k-means-clustering-b89d349e98e6 | Dropout | https://www.youtube.com/watch?v=NhZVe50QwPM | Text Classification | https://www.youtube.com/watch?v=fNxaJsNG3-s | Model Optimization | ||||||||||||||||
13 | Data Preprocessing | DBScan | https://www.youtube.com/watch?v=sJQHz97sCZ0 | batch normalization | https://www.youtube.com/watch?v=DtEq44FTPM4 | https://towardsdatascience.com/batch-normalization-and-dropout-in-neural-networks-explained-with-pytorch-47d7a8459bcd | Word Embeddings | https://www.youtube.com/watch?v=64qSgA66P-8 | https://machinelearningmastery.com/develop-word-embeddings-python-gensim/ | Size for Mobile & Edge | https://www.youtube.com/watch?v=3JWRVx1OKQQ | https://blog.tensorflow.org/2020/03/higher-accuracy-on-vision-models-with-efficientnet-lite.html?linkId=84432329 | ||||||||||||||
14 | Normalization | https://www.youtube.com/watch?v=Fw5iiyIHzew | https://towardsdatascience.com/understand-data-normalization-in-machine-learning-8ff3062101f0 | mean shift | https://www.youtube.com/playlist?list=PLd56wE27eBdgXToYHDRTRfA0xEs7qLYI- | Fine Tuning | https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html | cosine similarity | https://www.machinelearningplus.com/nlp/cosine-similarity/ | Model Compression | https://blog.rasa.com/compressing-bert-for-faster-prediction-2/ | |||||||||||||||
15 | Missing Data | https://www.youtube.com/watch?v=fCMrO_VzeL8&list=PL5-da3qGB5ICCsgW1MxlZ0Hq8LL5U3u9y&index=16 | https://towardsdatascience.com/how-to-handle-missing-data-8646b18db0d4 | Other Clustering Algos | https://www.youtube.com/watch?v=EUQY3hL38cw&list=PL_Nji0JOuXg2udXfS6nhK3CkIYLDtHNLp&index=9 | tensorboard | https://www.youtube.com/watch?v=xM8sO33x_OU | RNNs | https://www.youtube.com/watch?v=UNmqTiOnRfg | Monitoring | ||||||||||||||||
16 | Outliers | https://medium.com/@mehulved1503/effective-outlier-detection-techniques-in-machine-learning-ef609b6ade72 | https://colab.research.google.com/drive/1KdENO4sxkHyK5Mm-T2WUiR-O_G8hys8T | Apriori | https://www.youtube.com/watch?v=WGlMlS_Yydk&list=PL_Nji0JOuXg2udXfS6nhK3CkIYLDtHNLp&index=10 | https://medium.com/edureka/apriori-algorithm-d7cc648d4f1e | Label Smoothing | https://www.pyimagesearch.com/2019/12/30/label-smoothing-with-keras-tensorflow-and-deep-learning/ | Sequence2Sequence RNN | https://blog.keras.io/a-ten-minute-introduction-to-sequence-to-sequence-learning-in-keras.html | Optimization | |||||||||||||||
17 | Image Anomaly Detec. | https://www.pyimagesearch.com/2020/01/20/intro-to-anomaly-detection-with-opencv-computer-vision-and-scikit-learn/ | Key Points | Predictive Analysis | State Of The Art NLP | https://www.youtube.com/watch?v=BGKumht1qLA | https://github.com/huggingface/transformers | Input Pipeline | https://www.youtube.com/watch?v=SxOsJPaxHME | |||||||||||||||||
18 | Label Encoding | https://www.youtube.com/watch?v=J2gz0mbvg78&list=PLL2hlSFBmWwwGvEYNw-F5KkFZsrLJEIUd&index=14 | Gradient Descent | https://www.youtube.com/watch?v=sDv4f4s2SB8 | Regression | https://www.youtube.com/watch?v=-vHQub0NXI4&list=PLQY2H8rRoyvwLbzbnKJ59NkZvQAW9wLbx&index=16 | Transformers | https://www.youtube.com/watch?v=TQQlZhbC5ps | https://medium.com/inside-machine-learning/what-is-a-transformer-d07dd1fbec04 | https://huggingface.co/blog/how-to-train | Parallel Processing | https://www.youtube.com/watch?v=Ny3O4VpACkc | ||||||||||||||
19 | One Hot Encoding | https://www.youtube.com/watch?v=0s_1IsROgDc&t=696s | Error Metrics | https://www.youtube.com/watch?v=aDW44NPhNw0&list=PLs8w1Cdi-zvY9ICoYqu1XV0YoTQgShXw2 | Computer Vision (CV) | machine translation | https://towardsdatascience.com/neural-machine-translation-15ecf6b0b | https://github.com/qlanners/nmt_tutorial/blob/master/quinn_thesis_final.pdf | Model & Data Caching | https://www.youtube.com/watch?v=H_FDL0oRAWE | https://www.kaggle.com/abhishek/3-different-ways-to-cache-a-function-in-python | |||||||||||||||
20 | DataFrame Opt. | https://www.youtube.com/watch?v=wDYDYGyN_cw&list=PL5-da3qGB5ICCsgW1MxlZ0Hq8LL5U3u9y&index=21 | Confusion Matrix | https://www.youtube.com/watch?v=Kdsp6soqA7o | Intuition | https://www.youtube.com/watch?v=ACU-T9L4_lI | https://medium.com/@dataturks/deep-learning-and-computer-vision-from-basic-implementation-to-efficient-methods-3ca994d50e90 | BERT | https://www.youtube.com/watch?v=q5OmCB7CN-U | Other | ||||||||||||||||
21 | Feature Selection | https://machinelearningmastery.com/feature-selection-in-python-with-scikit-learn/ | Loss Functions | https://www.youtube.com/watch?v=QBbC3Cjsnjg&t=52s | CNNs | https://www.youtube.com/watch?v=H-HVZJ7kGI0&t=1962s | https://medium.com/@RaghavPrabhu/understanding-of-convolutional-neural-network-cnn-deep-learning-99760835f148 | https://github.com/nehal96/Deep-Learning-ND-Exercises/blob/master/Convolutional%20Neural%20Networks/convolutional-neural-networks-notes.md | distill bert | https://medium.com/huggingface/distilbert-8cf3380435b5 | migrate from TF1 to TF2 | https://www.youtube.com/watch?v=JmSNUeBG-PQ&list=PLQY2H8rRoyvwLbzbnKJ59NkZvQAW9wLbx&index=17 | ||||||||||||||
22 | Data Analysis | Fine Tuning (make your model better) | Natural Language Processing (NLP) | gpt-2 | https://amaarora.github.io/2020/02/18/annotatedGPT2.html | |||||||||||||||||||||
23 | Pandas Data Investigation | https://www.youtube.com/watch?v=dcqPhpY7tWk | ROC & AUC | https://www.youtube.com/watch?v=fSytzGwwBVw | Introduction to NLP | https://www.youtube.com/watch?v=_hAVVULrZ0Q&list=PLhQjrBD2T382Nz7z1AEXmioc27axa19Kv&index=6 | XLNET | https://mlexplained.com/2019/06/30/paper-dissected-xlnet-generalized-autoregressive-pretraining-for-language-understanding-explained/ | ||||||||||||||||||
24 | Analysing TED talks | https://www.youtube.com/watch?v=nxxWi4JZiLQ&list=PLL2hlSFBmWwyAjqlOrdBYMQCN8d7hHUL1&index=7 | Cross Validation | https://www.youtube.com/watch?v=4jRBRDbJemM | Overview | https://www.youtube.com/watch?v=xvqsFTUsOmc | https://github.com/adashofdata/nlp-in-python-tutorial | Generative Adversaria networks (GAN) | ||||||||||||||||||
25 | Grouping | https://www.youtube.com/watch?v=qy0fDqoMJx8 | Improve Log. Reg | https://www.youtube.com/watch?v=vN5cNN2-HWE&list=PLblh5JKOoLUICTaGLRoHQDuF_7q2GfuJF&index=12 | Reinforcement Learning (RL) | pix2pix | https://github.com/affinelayer/pix2pix-tensorflow | |||||||||||||||||||
26 | Sales Analysis Project | https://www.youtube.com/watch?v=eMOA1pPVUc4&t=135s | Choose K for K-Means | https://www.youtube.com/watch?v=QXOkPvFM6NU&list=PLs8w1Cdi-zvZGyT2Rt0ieA0G6xGUqn3Xw | Intro to RL | https://www.youtube.com/watch?v=qv6UVOQ0F44 | cycle gan | youtube.com/watch?v=AxrKVfjSBiA | ||||||||||||||||||
27 | Data Visualization | Grid Search | https://www.youtube.com/watch?v=Gol_qOgRqfA | https://towardsdatascience.com/grid-search-for-model-tuning-3319b259367e | RL & Genetic Algorithm | https://www.youtube.com/watch?v=aeWmdojEJf0 | StyleGAN | youtube.com/watch?v=SWoravHhsUU | https://towardsdatascience.com/how-to-train-stylegan-to-generate-realistic-faces-d4afca48e705 | https://github.com/NVlabs/stylegan | ||||||||||||||||
28 | Matplotlib | https://www.youtube.com/watch?v=6rKe2IEIu8c | https://colab.research.google.com/drive/1KdENO4sxkHyK5Mm-T2WUiR-O_G8hys8T | Recommendation Systems | HAHA RL BAD DRIVER | https://www.youtube.com/watch?v=VMp6pq6_QjI | Reinforcement Learning (RL) | |||||||||||||||||||
29 | Seaborn | https://www.youtube.com/watch?v=z7ZINBk8EUk&list=PL998lXKj66MpNd0_XkEXwzTGPxY2jYM2d&index=1 | Summary | https://www.youtube.com/watch?v=z0dx-YckFko | Other | RL in Tensorflow | https://github.com/wau/keras-rl2 | |||||||||||||||||||
30 | word embeddings tsne | https://medium.com/@aneesha/using-tsne-to-plot-a-subset-of-similar-words-from-word2vec-bb8eeaea6229 | Content Based | https://heartbeat.fritz.ai/recommender-systems-with-python-part-i-content-based-filtering-5df4940bd831 | AutoEncoders | https://blog.paperspace.com/autoencoder-image-compression-keras/ | Robotics | |||||||||||||||||||
31 | Collaborative | https://realpython.com/build-recommendation-engine-collaborative-filtering/ | Robotics Transfer Learning | https://www.youtube.com/watch?v=nQ1Ev9Inqco | ||||||||||||||||||||||
32 | Matrix Factorization | https://www.youtube.com/watch?v=ZspR5PZemcs&t=4s | ||||||||||||||||||||||||
33 | Hybrid | https://www.youtube.com/watch?v=_hf_y-_sj5Y&list=PLZoTAELRMXVN7QGpcuN-Vg35Hgjp3htvi | ||||||||||||||||||||||||
34 | Interpretability | |||||||||||||||||||||||||
35 | Intuition | https://www.youtube.com/watch?v=C80SQe16Rao | https://github.com/klemag/PyconUS_2019-model-interpretability-tutorial | |||||||||||||||||||||||
36 | ELI5 | |||||||||||||||||||||||||
37 | LIME | |||||||||||||||||||||||||
38 | SHAP | |||||||||||||||||||||||||
39 | ||||||||||||||||||||||||||
40 | ||||||||||||||||||||||||||
41 | ||||||||||||||||||||||||||
42 | ||||||||||||||||||||||||||
43 | ||||||||||||||||||||||||||
44 | ||||||||||||||||||||||||||
45 | ||||||||||||||||||||||||||
46 | ||||||||||||||||||||||||||
47 | ||||||||||||||||||||||||||
48 | ||||||||||||||||||||||||||
49 | ||||||||||||||||||||||||||
50 | ||||||||||||||||||||||||||
51 | ||||||||||||||||||||||||||
52 | ||||||||||||||||||||||||||
53 | ||||||||||||||||||||||||||
54 | ||||||||||||||||||||||||||
55 | ||||||||||||||||||||||||||
56 | ||||||||||||||||||||||||||
57 | ||||||||||||||||||||||||||
58 | ||||||||||||||||||||||||||
59 | ||||||||||||||||||||||||||
60 | ||||||||||||||||||||||||||
61 | ||||||||||||||||||||||||||
62 | ||||||||||||||||||||||||||
63 | ||||||||||||||||||||||||||
64 | ||||||||||||||||||||||||||
65 | ||||||||||||||||||||||||||
66 | ||||||||||||||||||||||||||
67 | ||||||||||||||||||||||||||
68 | ||||||||||||||||||||||||||
69 | ||||||||||||||||||||||||||
70 | ||||||||||||||||||||||||||
71 | ||||||||||||||||||||||||||
72 | ||||||||||||||||||||||||||
73 | ||||||||||||||||||||||||||
74 | ||||||||||||||||||||||||||
75 | ||||||||||||||||||||||||||
76 | ||||||||||||||||||||||||||
77 | ||||||||||||||||||||||||||
78 | ||||||||||||||||||||||||||
79 | ||||||||||||||||||||||||||
80 | ||||||||||||||||||||||||||
81 | ||||||||||||||||||||||||||
82 | ||||||||||||||||||||||||||
83 | ||||||||||||||||||||||||||
84 | ||||||||||||||||||||||||||
85 | ||||||||||||||||||||||||||
86 | ||||||||||||||||||||||||||
87 | ||||||||||||||||||||||||||
88 | ||||||||||||||||||||||||||
89 | ||||||||||||||||||||||||||
90 | ||||||||||||||||||||||||||
91 | ||||||||||||||||||||||||||
92 | ||||||||||||||||||||||||||
93 | ||||||||||||||||||||||||||
94 | ||||||||||||||||||||||||||
95 | ||||||||||||||||||||||||||
96 | ||||||||||||||||||||||||||
97 | ||||||||||||||||||||||||||
98 | ||||||||||||||||||||||||||
99 | ||||||||||||||||||||||||||
100 | ||||||||||||||||||||||||||