real data bilan ishlayman deganlar uchun (api bilan ishlasa ham bo'ladi ).

Below are the useful links for Regression:

https://www.seldon.io/machine-learning-regression-explained

🫱Linear Regression

https://towardsdatascience.com/mathematics-hidden-behind-linear-regression-431fe4d11969

https://christophm.github.io/interpretable-ml-book/limo.html

https://www.youtube.com/watch?v=PaFPbb66DxQ

https://www.youtube.com/watch?v=3dhcmeOTZ_Q&t=35s

https://christophm.github.io/interpretable-ml-book/limo.html

🫱Gradient Descent:
https://youtu.be/qg4PchTECck?si=kau7fBI5Lgb0kGo1

https://youtu.be/sDv4f4s2SB8?si=WxA38LHuXQf13nF-

https://d2l.ai/chapter_optimization/gd.html

https://machinelearningmastery.com/gradient-descent-for-machine-learning/

https://www.ruder.io/optimizing-gradient-descent/

🫱Polynomial Regression
https://www.analyticsvidhya.com/blog/2021/07/all-you-need-to-know-about-polynomial-regression/
https://www.youtube.com/watch?v=QptI-vDle8Y

🫱KNN
https://youtu.be/sTJApaBjong?si=gycuLY3cCYXMDnH4

🫱About metrics:
https://machinelearningmastery.com/regression-metrics-for-machine-learning/

https://www.youtube.com/watch?v=2AQKmw14mHM&ab_channel=StatQuestwithJoshStarmer

https://thestatsgeek.com/2013/10/28/r-squared-and-adjusted-r-squared/

https://stattrek.com/regression/residual-analysis

https://medium.com/@HalderNilimesh/understanding-residual-plots-in-linear-regression-models-a-comprehensive-guide-with-examples-2fb5a60daf26

https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_percentage_error.html

https://en.wikipedia.org/wiki/Mean_absolute_percentage_error

🫱Loss Function
https://www.analyticsvidhya.com/blog/2019/08/detailed-guide-7-loss-functions-machine-learning-python-code/#Difference_Between_Loss_Function_and_Cost_Function

https://heartbeat.comet.ml/5-regression-loss-functions-all-machine-learners-should-know-4fb140e9d4b0

https://www.youtube.com/watch?v=QBbC3Cjsnjg

🫱Core ML Concepts:
https://www.educative.io/answers/definition-model-fitting

https://machinelearningmastery.com/train-test-split-for-evaluating-machine-learning-algorithms/

https://ruveydakardelcetin.medium.com/the-importance-of-splitting-datasets-into-training-validation-and-test-sets-417caaeae91d

https://www.simplilearn.com/tutorials/machine-learning-tutorial/cross-validation

https://statology.org/leave-one-out-cross-validation/

https://medium.com/@mouadenna/time-series-splitting-techniques-ensuring-accurate-model-validation-5a3146db3088

https://youtu.be/EuBBz3bI-aA?si=aRSpNvSTcHTMYWGB

https://ruveydakardelcetin.medium.com/the-importance-of-splitting-datasets-into-training-validation-and-test-sets-417caaeae91d

https://www.simplilearn.com/tutorials/machine-learning-tutorial/regularization-in-machine-learning

https://learn.epam.com/myLearning/eur01.safelinks.protection.outlook.com/GetUrlReputation

https://www.geeksforgeeks.org/hyperparameter-tuning/

https://jakevdp.github.io/PythonDataScienceHandbook/05.03-hyperparameters-and-model-validation.html

https://statisticsbyjim.com/regression/model-specification-variable-selection/

🫱More Advanced

GLMs

https://christophm.github.io/interpretable-ml-book/extend-lm.html

https://learn.epam.com/myLearning/eur01.safelinks.protection.outlook.com/GetUrlReputation

https://online.stat.psu.edu/stat504/lesson/6/6.1

https://www.youtube.com/watch?v=6Nfv0Xr44y8

https://www.youtube.com/watch?v=i62gffPrZYA

Robust regression
https://machinelearningmastery.com/robust-regression-for-machine-learning-in-python/

https://online.stat.psu.edu/stat501/lesson/t/t.1/t.1.1-robust-regression-methods

https://scikit-learn.org/stable/modules/linear_model.html#different-scenario-and-useful-concepts

https://www.youtube.com/watch?v=ddHpy0ptUvI

https://www.youtube.com/watch?v=Vf7oJ6z2LCc

Data analist uchun ML ga asoslanib data asosida strategik qarorlar qabul qilish suv va havodek zarur bo'ladi (Menimcha) .Kelajakda har bir kasb egasi Kasb + AI biladigan bo'ladi . Shunday ekan bosinglar

Below are the useful links for Classification:

🫱Classification

https://www.datacamp.com/blog/classification-machine-learning

https://www.geeksforgeeks.org/machine-learning/getting-started-with-classification/

https://www.youtube.com/watch?v=ju3J7iRy6xI&list=PLoROMvodv4rOzrYsAxzQyHb8n_RWNuS1e&index=16

🫱Binary Classification

https://spotintelligence.com/2023/10/09/binary-classification/

🖐Core Models

🫱Logistic Regression

https://www.v7labs.com/blog/logistic-regression

https://medium.com/@premvardhankumar/a-deep-understanding-of-logistic-regression-with-geometric-probabilistic-and-loss-minimization-2ced042bdcc7

https://www.youtube.com/watch?v=yIYKR4sgzI8

https://www.youtube.com/watch?v=kr_Be9NVXOM&list=PLoROMvodv4rOzrYsAxzQyHb8n_RWNuS1e&index=16

https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression

🫱Support Vector Machines

https://www.skillcamper.com/blog/support-vector-machines-svm-from-hyperplanes-to-kernel-tricks

https://www.youtube.com/watch?v=efR1C6CvhmE

https://www.youtube.com/watch?v=Toet3EiSFcM

https://www.youtube.com/watch?v=Qc5IyLW_hns

https://www.youtube.com/watch?v=Op0OyOuDjcQ&list=PLoROMvodv4rOzrYsAxzQyHb8n_RWNuS1e&index=62

https://www.youtube.com/watch?v=02icdqOJsH4&list=PLoROMvodv4rOzrYsAxzQyHb8n_RWNuS1e&index=64

https://scikit-learn.org/stable/modules/svm.html

🫱 k-Nearest Neighbors

https://www.ibm.com/think/topics/knn

https://www.youtube.com/watch?v=HVXime0nQeI

https://www.youtube.com/watch?v=0pP4EwWJgIU

https://scikit-learn.org/stable/modules/neighbors.html#classification

🫱 Naiive Bayes CLassifier

https://towardsdatascience.com/naive-bayes-classifier-81d512f50a7c

https://www.youtube.com/watch?v=CPqOCI0ahss

https://scikit-learn.org/stable/modules/naive_bayes.html

🫱 Loss Functions

https://365datascience.com/tutorials/machine-learning-tutorials/cross-entropy-loss/

https://towardsdatascience.com/understanding-binary-cross-entropy-log-loss-a-visual-explanation-a3ac6025181a

https://www.geeksforgeeks.org/machine-learning/ml-common-loss-functions/#classification-loss-functions-in-machine-learning

https://www.kaggle.com/code/nghihuynh/understanding-loss-functions-for-classification/notebook

🖐Evaluation Metrics

🫱Classification threshold

https://scikit-learn.org/stable/modules/classification_threshold.html

https://www.iguazio.com/glossary/classification-threshold/

🫱Confusion Matrix

https://scikit-learn.org/stable/modules/model_evaluation.html#confusion-matrix

https://www.evidentlyai.com/classification-metrics/confusion-matrix

🫱Accuracy Precision Recall F1 F(beta)

https://www.evidentlyai.com/classification-metrics/accuracy-precision-recall

https://towardsai.net/p/l/f1-to-f-beta

https://medium.com/@douglaspsteen/beyond-the-f-1-score-a-look-at-the-f-beta-score-3743ac2ef6e3#:~:text=The%20F%2Dbeta%20score%20calculation,to%20the%20F%2D1%20Score.

https://scikit-learn.org/stable/modules/model_evaluation.html#classification-metrics

🫱 ROC and AUC

https://www.evidentlyai.com/classification-metrics/explain-roc-curve

https://www.youtube.com/watch?v=4jRBRDbJemM

https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html

🫱PR-Curve (Precision-Recall Curve)

https://medium.com/@douglaspsteen/precision-recall-curves-d32e5b290248

https://scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html

🫱 Imbalanced Datasets

https://www.analyticsvidhya.com/blog/2021/06/5-techniques-to-handle-imbalanced-data-for-a-classification-problem/

https://www.analyticsvidhya.com/blog/2020/10/overcoming-class-imbalance-using-smote-techniques/

https://www.kdnuggets.com/2017/06/7-techniques-handle-imbalanced-data.html

🫱 Multiclass & Multilabel Classification

https://scikit-learn.org/stable/modules/multiclass.html

https://www.dataknowsall.com/multiclass.html

https://www.projectpro.io/article/multi-class-classification-python-example/547

https://www.geeksforgeeks.org/an-introduction-to-multilabel-classification/?ref=gcse

🫱 Micro vs. Macro Averaging

Source: How-To Geek
https://share.google/lMLzZECxCUBfuEsTg

Just do it

power bi ga datani olish usullari

«Иқтисодий хавфсизлик» блокини визуализация қилиш бўйича эксперт ечимини тайёрлаш.
Мазкур ечим қуйидагиларни ўз ичига олиши лозим:
асосий макроиқтисодий кўрсаткичлар ва молиявий барқарорлик индикаторларини визуал тақдим этиш концепцияси;
миллий индикаторлардан ҳудудий ва тармоқ даражаларигача интерактив деталлаштиришни таъминловчи бошқарув панеллари тузилмаси;
тезкор мониторинг тизими, жумладан «светофор» моделлари (KPI)ни визуализация қилиш ҳамда критик оғишлар бўйича хабардор қилиш механизмлари (алерт тизими);
фойдаланувчининг маълумотлар билан ўзаро ишлаш мантиғини акс эттирувчи демонстрацион макетлар (wireframe/UI-концепт).
Блок. Иқтисодий хавфсизлик
1. Макроиқтисодиёт:
ялпи ички маҳсулот — ЯИМ (динамикаси, таркиби);
инфляция (умумий, базавий, озиқ-овқат инфляцияси);
истеъмол нархлари индекси (CPI);
ўртача ва медиан иш ҳақи;
тўғридан-тўғри хорижий инвестициялар (FDI);
экспорт ва импорт (таркиби ва динамикаси).
2. Молиявий барқарорлик:
валюта захиралари ҳажми ва олтин-валюта фонди динамикаси;
миллий валютанинг асосий жаҳон валюталарига нисбатан курси динамикаси (USD, EUR, RUB, CNY);
давлат қарзи ва унинг таркиби (ташқи/ички);
бюджет баланси;
тўлов баланси дефицити/профицити;
банк тизими ликвидлиги;
муаммоли кредитлар улуши (NPL);
молиявий барқарорлик индекси. => #task