{"id":21352153,"url":"https://github.com/kennethleungty/kennethleungty","last_synced_at":"2025-07-12T20:32:09.426Z","repository":{"id":56066056,"uuid":"391407404","full_name":"kennethleungty/kennethleungty","owner":"kennethleungty","description":"Data Science Portfolio","archived":false,"fork":false,"pushed_at":"2024-09-14T17:19:43.000Z","size":477,"stargazers_count":25,"open_issues_count":0,"forks_count":5,"subscribers_count":2,"default_branch":"main","last_synced_at":"2024-09-15T03:02:13.241Z","etag":null,"topics":["ai","analytics","artificial-intelligence","data","data-analytics","data-science","data-science-portfolio","data-scientist","deep-learning","ds","kenneth-leung","kennethleungty","machine-learning","ml","portfolio","python"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/kennethleungty.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2021-07-31T16:24:37.000Z","updated_at":"2024-09-14T17:19:46.000Z","dependencies_parsed_at":"2023-11-07T18:31:20.656Z","dependency_job_id":"655a1907-d511-44dd-860f-536b5e0a5a87","html_url":"https://github.com/kennethleungty/kennethleungty","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kennethleungty%2Fkennethleungty","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kennethleungty%2Fkennethleungty/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kennethleungty%2Fkennethleungty/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kennethleungty%2Fkennethleungty/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/kennethleungty","download_url":"https://codeload.github.com/kennethleungty/kennethleungty/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":225834368,"owners_count":17531471,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["ai","analytics","artificial-intelligence","data","data-analytics","data-science","data-science-portfolio","data-scientist","deep-learning","ds","kenneth-leung","kennethleungty","machine-learning","ml","portfolio","python"],"created_at":"2024-11-22T03:12:49.451Z","updated_at":"2025-07-12T20:32:09.408Z","avatar_url":"https://github.com/kennethleungty.png","language":null,"funding_links":["https://www.buymeacoffee.com/kennethleung"],"categories":[],"sub_categories":[],"readme":"## 👋 Hello, I'm Kenneth Leung\n\n- Thanks for popping by! As an avid learner, bold builder, curious explorer, and driven doer with a bias towards action, I enjoy seeking and solving meaningful problems with data and tech while having fun at the same time.\n- I welcome you to join me on a journey of data science discovery! Follow me on [GitHub](https://github.com/kennethleungty), [Medium](https://kennethleungty.medium.com), and [LinkedIn](https://linkedin.com/in/kennethleungty) to stay updated with more engaging and practical content.\n- You can find my data science portfolio here, where every project and article was born out of inspiration, curiosity, and motivation. Feel free to connect for a chat (coffee or virtual) to discuss shared interests and topics!\n\n![Project Count](https://komarev.com/ghpvc/?username=kennethleungty\u0026color=green) ![](https://img.shields.io/static/v1?label=Project+count\u0026message=83\u0026color=2ea44f) ![GitHub stars](https://img.shields.io/github/stars/kennethleungty?affiliations=OWNER\u0026style=social)\n\n\u003ch3\u003e How to reach me \u003c/h3\u003e\n\u003cdiv\u003e\n    \u003ca href=\"https://www.linkedin.com/in/kennethleungty\" target=\"_blank\"\u003e\n        \u003cimg src=\"https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge\u0026logo=linkedin\u0026logoColor=white\" height=\"30\" width=\"125px\"\u003e\n    \u003c/a\u003e\u0026nbsp\n    \u003ca href=\"https://calendly.com/kennethleungty/chat\" target=\"_blank\"\u003e\n        \u003cimg src=\"https://img.shields.io/badge/Calendly-Let's%20chat!-brightgreen?style=for-the-badge\u0026logo=googlechat\" height=\"30\"\u003e\n    \u003c/a\u003e\u0026nbsp\n    \u003ca href=\"https://www.buymeacoffee.com/kennethleung\" target=\"_blank\"\u003e    \n        \u003cimg src=\"https://cdn.buymeacoffee.com/buttons/default-orange.png\" alt=\"Buy Me A Coffee\" height=\"30\" width=\"125\"\n    \u003c/a\u003e\u0026nbsp\n    \n\u003c/div\u003e\n\u003chr\u003e\n\n\n## Portfolio Contents\n1. [Computer Vision](#computer-vision)\n2. [Database Management](#database)\n3. [Data Extraction and Web Scraping](#data-extraction-and-web-scraping)\n4. [Data Science Certification Guides](#data-science-certification-guides)\n5. [Data Science Toolkit](#data-science-tools)\n6. [Data Science in the Real World](#real-world-data-science)\n7. [Generative AI](#generative-ai)\n8. [Insights from Data Science Talks](#talks)\n9. [Machine Learning](#machine-learning)\n10. [MLOps](#mlops)\n11. [Natural Language Processing](#natural-language-processing)\n12. [Networks and Graphs](#networks-and-graphs)\n13. [Responsible AI](#responsible-ai)\n14. [Sports Analytics](#sports-analytics)\n15. [Visualization](#visualization)\n16. [Web Development](#web-development)\n17. [Web3 and Metaverse](#web3)\n18. [Writing for DataCamp](#writing-for-datacamp)\n19. [Writing Tips](#writing-tips)\n\n**Projects with :star: are my personal favourites, so do check them out!**\n\n___\n\u003ca name=\"computer-vision\"\u003e\u003c/a\u003e\n## Computer Vision :eye:\n| Title | Article | Repo |\n| --- | --- | --- |\n| Classifying Images of Alcoholic Beverages with fast.ai v2 | [🔗](https://towardsdatascience.com/classifying-images-of-alcoholic-beverages-with-fast-ai-34c4560b5543?sk=d0efa0e6b6d214c52b337a0381a4fd3d) | [🔗](https://github.com/kennethleungty/Alcohol-Image-Classifier-fastai) |\n| Russian Car Plate Detection with OpenCV and TesseractOCR | [🔗](https://towardsdatascience.com/russian-car-plate-detection-with-opencv-and-tesseractocr-dce3d3f9ff5c?sk=263f4351c3c2c5cd9c60a4469b9dab08) | [🔗](https://github.com/kennethleungty/Car-Plate-Detection-OpenCV-TesseractOCR) |\n| Evaluate OCR Output Quality with Character Error Rate (CER) and Word Error Rate (WER) | [🔗](https://towardsdatascience.com/evaluating-ocr-output-quality-with-character-error-rate-cer-and-word-error-rate-wer-853175297510?sk=9b76f0a994edc90c105f71ac2f7d725f) | [🔗](https://github.com/kennethleungty/OCR-Metrics-CER-WER) |\n| Top Python libraries for Image Augmentation in Computer Vision | [🔗](https://towardsdatascience.com/top-python-libraries-for-image-augmentation-in-computer-vision-2566bed0533e?sk=a45cb5c2070f8434f876d48597addd33) | [🔗](https://github.com/kennethleungty/Image-Augmentation-Libraries) |\n| :star: PyTorch Ignite Tutorial - Classifying Tiny ImageNet with EfficientNet | [🔗](https://towardsdatascience.com/pytorch-ignite-classifying-tiny-imagenet-with-efficientnet-e5b1768e5e8f?sk=505c5489f67772912e40ce0ed67bba20) | [🔗](https://github.com/kennethleungty/PyTorch-Tiny-ImageNet-Classification) |\n| Practical Guide to Transfer Learning in TensorFlow for Multiclass Image Classification | [🔗](https://towardsdatascience.com/practical-guide-to-transfer-learning-in-tensorflow-for-multiclass-image-classification-d35fab7b28c0?sk=712d34ba690c8255588a624419a3ad5a) | [🔗](https://github.com/kennethleungty/TensorFlow-Transfer-Learning-Image-Classification) |\n\n___\n\u003ca name=\"database\"\u003e\u003c/a\u003e\n## Database Management :file_cabinet:   \n| Title | Article | Repo |\n| --- | --- | --- |    \n| :star: Definitive Guide to Creating a SQL Database on Cloud with AWS and Python | [🔗](https://towardsdatascience.com/definitive-guide-to-create-an-sql-database-on-cloud-with-aws-and-python-c818c7270af2?sk=be354d077fffa25b2eb1e59dd1dd1d94) | [🔗](https://github.com/kennethleungty/AWS-RDS-MySQL-Python) | \n| PyMySQL - Connecting Python and SQL for Data Science | [🔗](https://towardsdatascience.com/pymysql-connecting-python-and-sql-for-data-science-91e7582d21d7?sk=d450443e2e74f1c8b5d7d3820d1e9202) | [🔗](https://github.com/kennethleungty/PyMySQL-Demo) | \n\n    \n___\n\u003ca name=\"data-extraction-and-web-scraping\"\u003e\u003c/a\u003e\n## Data Extraction and Web Scraping :toolbox:\n| Title | Article | Repo |\n| --- | --- | --- |\n| Using OneMap API to extract Singapore postal codes, coordinates and travel distance | - | [🔗](https://github.com/kennethleungty/OneMap-API) |\n| A Detailed Web Scraping Walkthrough Using Python and Selenium | [🔗](https://medium.com/swlh/web-scrapping-healthcare-professionals-information-1372385d639d?sk=a1acd77dc36b84a1446fa4106831d15d) | [🔗](https://github.com/kennethleungty/Web-Scraping-Walkthrough-HCP-Info) |\n| :star: How to Web Scrape Wikipedia using LangChain Agents and Tools with OpenAI's LLMs and Function Calling | [🔗](https://medium.datadriveninvestor.com/how-to-web-scrape-wikipedia-using-llm-agents-f0dba8400692?sk=890b6e9281596573274cfe407ffe5f87)| [🔗](https://github.com/kennethleungty/Wikipedia-Scraping-with-LLM-Agents) |\n  \n___\n\u003ca name=\"data-science-certification-guides\"\u003e\u003c/a\u003e\n## Data Science Certification Guides :man_student:\n| Title | Article | Repo |\n| --- | --- | --- |\n| 3 Steps to Get AWS Cloud Practitioner Certified in 2 Weeks | [🔗](https://towardsdatascience.com/3-steps-to-get-aws-cloud-practitioner-certified-in-2-weeks-or-less-772178f48249?sk=b2865229b15eb0342bb0e2030faf8c4a) | [🔗](https://github.com/kennethleungty/AWS-Certified-Cloud-Practitioner-Notes) |\n| 3 Steps to Get Tableau Desktop Certified in 2 Weeks | [🔗](https://towardsdatascience.com/3-steps-to-get-tableau-desktop-specialist-certified-in-2-weeks-abbef25778de?sk=5bbc1f99cc8543bb71683239237d91c9) | - |\n| :star: No-Frills Guide to Passing the AWS Certified Machine Learning Specialty Exam | [🔗](https://towardsdatascience.com/no-frills-guide-to-passing-the-aws-certified-machine-learning-specialty-exam-55624579353f?sk=c21257b289f7d5ae9e1a661957c3a484) | - |\n| Microsoft Certified: Azure AI Engineer Associate - Study Notes | - | [🔗](https://github.com/kennethleungty/Azure-AI-Engineer-Associate-Notes)\n\n___\n\u003ca name=\"data-science-tools\"\u003e\u003c/a\u003e\n## Data Science Toolkit :hammer_and_wrench:\n| Title | Article | Repo |\n| --- | --- | --- |\n| Common Python codes for Data Wrangling | - | [🔗](https://github.com/kennethleungty/Common-Python-Codes) |\n| Enhance your Python code’s readability with pycodestyle | [🔗](https://towardsdatascience.com/enhance-your-python-codes-readability-with-pycodestyle-9838976077cb?sk=d609f9fcac20e3ad0feb086fc1ea160c) | - |\n| Free Resources for Generating Realistic Fake Data | [🔗](https://towardsdatascience.com/free-resources-for-generating-realistic-fake-data-da63836be1a8?sk=475419fafb691b93c236b99afe674439) | - |\n| Most Starred and Forked GitHub Repos for Data Science and Python | [🔗](https://towardsdatascience.com/the-most-starred-forked-github-repos-for-python-and-data-science-f8bb3de47e96?sk=fba11166d349efae710a45d67fffede9) | - |\n| Most Starred and Forked GitHub Repos for Data Science and R | [🔗](https://medium.datadriveninvestor.com/most-starred-and-forked-github-repos-for-r-in-data-science-fb87a54d2a6a?sk=3a74fb3e788faa2307b98dfdabf51384) | - |\n| Automatically Generate Machine Learning Code with Just a Few Clicks | [🔗](https://towardsdatascience.com/automatically-generate-machine-learning-code-with-just-a-few-clicks-7901b2334f97?sk=995415e038af91d9a18f9f8e21b04efb) | - |\n| Read and Modify Image Metadata with Python | [🔗](https://towardsdatascience.com/read-and-edit-image-metadata-with-python-f635398cd991?sk=ff7814558f3642d356d0988dc188008a) | [🔗](https://github.com/kennethleungty/Image-Metadata-Exif) |\n| Top Tips to Google Search Like a Seasoned Data Scientist | [🔗](https://towardsdatascience.com/top-tips-to-google-search-like-a-data-science-pro-897599f4d9ae?sk=710f416b4c7c329ee084c54ec67abf0a) | - |\n| How to Swap Day and Month of Incorrectly Formatted Excel Dates | [🔗](https://medium.datadriveninvestor.com/how-to-swap-day-and-month-of-incorrectly-formatted-excel-dates-503dfc7ad7da?sk=1b7154a961c4dd8a1ab0ef791a3922fc) | - |\n\n___\n\u003ca name=\"real-world-data-science\"\u003e\u003c/a\u003e\n## Data Science in the Real World :earth_asia:\n| Title | Article | Repo |\n| --- | --- | --- |\n| Exploring Illegal Drugs in Singapore — A Data Perspective | [🔗](https://towardsdatascience.com/exploring-illegal-drugs-in-singapore-a-data-perspective-3716a75ee557) | [🔗](https://github.com/kennethleungty/Exploring-Illegal-Drugs) |\n| Pharmacokinetic Modeling of Drug Concentration Trajectories using Ordinary Differential Equations (ODE) and Global Optimization with Differential Evolution | - | [🔗](https://github.com/kennethleungty/ODE-Modelling-with-Differential-Evolution) |\n| Healthcare’s AI Future — In Conversation with Andrew Ng and Fei-Fei Li | [🔗](https://towardsdatascience.com/healthcares-ai-future-conversation-with-andrew-ng-and-fei-fei-li-a6eacb6aaaf1?sk=be96a46cc7df4fe5abe4bdf810b9518d) | - |    \n| Real-World Data Science Use Cases in the Insurance Industry | [🔗](https://medium.datadriveninvestor.com/real-world-data-science-use-cases-in-the-insurance-industry-58f280983ee2?sk=5c4a080f00cf953fc91f4f21bf4a0e74) | - |    \n| :star: Failed-ML: Compilation of high-profile real-world examples of failed machine learning projects | [🔗](https://towardsdatascience.com/when-ai-goes-astray-high-profile-machine-learning-mishaps-in-the-real-world-26bd58692195?sk=7b1b9831399403b123523172e74c8ddd) | [🔗](https://github.com/kennethleungty/Failed-ML) |\n\n___\n\u003ca name=\"generative-ai\"\u003e\u003c/a\u003e\n## Generative AI :robot:    \n| Title | Article | Repo |\n| --- | --- | --- |\n| Generative AI Pharmacist - Macy | [🔗](https://www.linkedin.com/feed/update/urn:li:activity:7031533843429949440) | [🔗](https://github.com/kennethleungty/Generative-AI-Pharmacist) |\n| :star: ChatPod - Q\u0026A over your Podcasts with Whisper, FAISS, and LangChain | [🔗](https://www.linkedin.com/feed/update/urn:li:activity:7051350652484026368/) | [🔗](https://github.com/kennethleungty/ChatPod/) |\n| :star: Running Llama 2 and other Open-Source LLMs on CPU Inference Locally for Document Q\u0026A | [🔗](https://kennethleungty.medium.com/running-llama-2-on-cpu-inference-for-document-q-a-3d636037a3d8?sk=d9e9f1ad0ff72e66adf11e0226ad4489) | [🔗](https://github.com/kennethleungty/Llama-2-Open-Source-LLM-CPU-Inference) |\n| :star: Finance-LLMs - Comprehensive Compilation of Real-World LLM Implementation in Financial Services | [🔗](https://medium.com/datadriveninvestor/how-llms-are-reshaping-financial-services-real-world-examples-d07bc74ad4bd?sk=eda65064719c240396e65cbb55bb2e30) | [🔗](https://github.com/kennethleungty/finance-llms) |\n| :star: Text-to-Audio Generation with Bark, Clearly Explained | [🔗](https://betterprogramming.pub/text-to-audio-generation-with-bark-clearly-explained-4ee300a3713a?sk=e2b2f75f5fc93c656bef031c60bf99bf) | [🔗](https://github.com/kennethleungty/Text-to-Audio-with-Bark) |\n| Guide to ChatGPT's Advanced Settings — Top P, Frequency Penalties, Temperature, and More | [🔗](https://towardsdatascience.com/guide-to-chatgpts-advanced-settings-top-p-frequency-penalties-temperature-and-more-b70bae848069?sk=c976bb82783c56b37b2549cc79628f12) | - |\n| Inside the Leaked System Prompts of GPT-4, Gemini 1.5, Claude 3, and More | [🔗](https://medium.com/gitconnected/inside-the-leaked-system-prompts-of-gpt-4-gemini-1-5-claude-3-and-more-4ecb3d22b447?sk=7e053318c47b260ee482a5c8b319dd83) | - |\n| :star: Exposing Jailbreak Vulnerabilities in LLM Applications with ARTKIT | [🔗](https://medium.com/towards-data-science/exposing-jailbreak-vulnerabilities-in-llm-applications-with-artkit-d2df5f56ece8?sk=99f8d313c09ab04b15b8627c997b3705) | [🔗](https://github.com/kennethleungty/ARTKIT-Gandalf-Challenge) |\n| :star: How to Benchmark DeepSeek-R1 Distilled Models on GPQA Using Ollama and OpenAI's simple-evals | [🔗](https://towardsdatascience.com/how-to-benchmark-deepseek-r1-distilled-models-on-gpqa-using-ollama-and-openais-simple-evals/) | [🔗](https://github.com/kennethleungty/DeepSeek-R1-Ollama-Simple-Evals)\n___\n\n\u003ca name=\"talks\"\u003e\u003c/a\u003e\n## Insights from Data Science Talks :man_teacher:\n| Title | Article | Repo |\n| --- | --- | --- |\n| Bridging AI’s Proof-of-Concept to Production Gap — Insights from Andrew Ng | [🔗](https://towardsdatascience.com/bridging-ais-proof-of-concept-to-production-gap-insights-from-andrew-ng-f2ac119ee737?sk=3a5e77fd5e3d7f4ca4f6118a6c98c7af) | - |\n    \n\n___\n\u003ca name=\"machine-learning\"\u003e\u003c/a\u003e\n## Machine Learning :slot_machine:\n| Title | Article | Repo |\n| --- | --- | --- |\n| Exploring Condominium Rental Prices with Web Scraping and Exploratory Data Analysis | [🔗](https://medium.com/swlh/web-scrapping-and-data-analysis-of-condominium-rental-market-in-singapore-da5265c71d19) | [🔗](https://github.com/kennethleungty/Singapore-Condo-Rental-Market-Analysis) |\n| Using Ensemble Regressors to Predict Condominium Rental Prices | [🔗](https://medium.com/geekculture/using-ensemble-regressors-to-predict-condo-rental-prices-47eb7c3d5cd9) | [🔗](https://github.com/kennethleungty/Singapore-Condo-Rental-Market-Analysis) |\n| The Dying ReLU Problem, Clearly Explained | [🔗](https://towardsdatascience.com/the-dying-relu-problem-clearly-explained-42d0c54e0d24?sk=44114ea1d3f153c69a6c846806299447) | - |\n| Why Bootstrapping Actually Works | [🔗](https://towardsdatascience.com/why-bootstrapping-actually-works-1e75640cf172?sk=fb4234ee543cec688b9fcc56560dcd27) | - |\n| :star: Assumptions of Logistic Regression, Clearly Explained | [🔗](https://towardsdatascience.com/assumptions-of-logistic-regression-clearly-explained-44d85a22b290?sk=02e66433ea855897d8164e5027866137) | [🔗](https://github.com/kennethleungty/Logistic-Regression-Assumptions) |\n| Data-Centric AI Competition - Tips and Tricks of a Top 5% Finish | [🔗](https://towardsdatascience.com/data-centric-ai-competition-tips-and-tricks-of-a-top-5-finish-9cacc254626e?sk=540ba74bd77f36016beb73f59fb58584) | [🔗](https://github.com/kennethleungty/Data-Centric-AI-Competition) |\n| Credit Card Fraud Detection with AutoXGB | [🔗](https://towardsdatascience.com/autoxgb-for-financial-fraud-detection-f88f30d4734a?sk=13bbbe9761698db8d4c0ffef661db916)| [🔗](https://github.com/kennethleungty/Credit-Card-Fraud-Detection-AutoXGB) |\n| :star: Micro, Macro \u0026 Weighted Averages of F1 Score, Clearly Explained | [🔗](https://towardsdatascience.com/micro-macro-weighted-averages-of-f1-score-clearly-explained-b603420b292f?sk=e319c91bebb43b90a1e9cd2bcfce311d) | - |\n| Principal Component Regression - Clearly Explained and Implemented | [🔗](https://towardsdatascience.com/principal-component-regression-clearly-explained-and-implemented-608471530a2f?sk=60a7d1ed57b94e744dff1bb356a9e986) | [🔗](https://github.com/kennethleungty/Principal-Component-Regression) |\n| :star: Feature Selection with Simulated Annealing in Python, Clearly Explained | [🔗](https://towardsdatascience.com/feature-selection-with-simulated-annealing-in-python-clearly-explained-1808db14f8fa?sk=4b74f6b1e9143f6b3066dd1b5980c2d4) | [🔗](https://github.com/kennethleungty/Simulated-Annealing-Feature-Selection) |\n| Quick Primer on Types of Missing Data and Imputation Techniques | [🔗](https://medium.com/geekculture/quick-primer-on-types-of-missing-data-and-imputation-techniques-cf253ce88755?sk=efe876f9c0d658a326ad4520bc5076e7) | - |\n| Imputation of Missing Data in Tables with DataWig | [🔗](https://towardsdatascience.com/imputation-of-missing-data-in-tables-with-datawig-2d7ab327ece2?sk=0462569e9e01e3c9681c985631ca3b51) | [🔗](https://github.com/kennethleungty/DataWig-Missing-Data-Imputation) |\n\n    \n___\n\u003ca name=\"mlops\"\u003e\u003c/a\u003e\n## MLOps - Machine Learning Operations :man_mechanic:\n| Title | Article | Repo |\n| --- | --- | --- |\n| Key Learning Points from MLOps Specialization — Course 1/4 | [🔗](https://towardsdatascience.com/key-learning-points-from-mlops-specialization-course-deeplearning-ai-andrew-ng-5d0746605752?sk=3d1215fd0a562af6f74033a13fa05107) | [🔗](https://github.com/kennethleungty/MLOps-Specialization-Notes/tree/main/1.%20Introduction%20to%20Machine%20Learning%20in%20Production) |\n| Key Learning Points from MLOps Specialization — Course 2/4 | [🔗](https://towardsdatascience.com/key-learning-points-from-mlops-specialization-course-2-13af51e22d90?sk=29b8ffeae2530cf244b5fcf1c09ba54d) | [🔗](https://github.com/kennethleungty/MLOps-Specialization-Notes/tree/main/2.%20Machine%20Learning%20Data%20Lifecycle%20in%20Production) |\n| Key Learning Points from MLOps Specialization — Course 3/4 | [🔗](https://towardsdatascience.com/key-learning-points-from-mlops-specialization-course-3-9e67558212ee?sk=1b5120c42d3326a11889dc13a354c484) | [🔗](https://github.com/kennethleungty/MLOps-Specialization-Notes/tree/main/3.%20Machine%20Learning%20Modeling%20Pipelines%20in%20Production) |\n| Key Learning Points from MLOps Specialization — Course 4/4 | [🔗](https://towardsdatascience.com/key-learning-points-from-mlops-specialization-course-4-ee39bbd2864b?sk=ef14da43869074668002cfb89fb87735) | [🔗](https://github.com/kennethleungty/MLOps-Specialization-Notes/tree/main/4.%20Deploying%20Machine%20Learning%20Models%20in%20Production) |       \n| :star: End-to-End AutoML Pipeline with H2O AutoML, MLflow, FastAPI, and Streamlit for Insurance Cross-Sell | [🔗](https://towardsdatascience.com/end-to-end-automl-train-and-serve-with-h2o-mlflow-fastapi-and-streamlit-5d36eedfe606?sk=9dcb775703e54445bfb35e1a86ad5381) | [🔗](https://github.com/kennethleungty/End-to-End-AutoML-Insurance) |\n| :star: How to Dockerize Machine Learning Applications Built with H2O, MLflow, FastAPI, and Streamlit | [🔗](https://towardsdatascience.com/how-to-dockerize-machine-learning-applications-built-with-h2o-mlflow-fastapi-and-streamlit-a56221035eb5?sk=3ac7a613f10b42e9fbc0c395d7d529af) | [🔗](https://github.com/kennethleungty/End-to-End-AutoML-Insurance) |\n| :star: Building and Managing an Isolation Forest Anomaly Detection Pipeline with Kedro | [🔗](https://neptune.ai/blog/data-science-pipelines-with-kedro) | [🔗](https://github.com/kennethleungty/Anomaly-Detection-Pipeline-Kedro) |\n\n    \n___\n\u003ca name=\"natural-language-processing\"\u003e\u003c/a\u003e\n## Natural Language Processing :bookmark_tabs:\n| Title | Article | Repo |\n| --- | --- | --- |\n| COVID-19 Vaccine — What’s the Public Sentiment? | [🔗](https://towardsdatascience.com/covid-19-vaccine-whats-the-public-sentiment-7149c9b42b99?sk=614cb3f0f8b00d03240f0e439260e479) | [🔗](https://github.com/kennethleungty/COVID19-Vaccine-Sentiment-Analysis) |\n| Keyword Extraction and Analysis Pipeline with KeyBERT and Taipy | [🔗](https://towardsdatascience.com/arxiv-keyword-extraction-and-analysis-pipeline-with-keybert-and-taipy-2972e81d9fa4?sk=b45c99c3d1104e3eeea80d9c3fb1a993) | [🔗](https://github.com/kennethleungty/Keyword-Analysis-with-KeyBERT-and-Taipy) |\n\n___\n\u003ca name=\"networks-and-graphs\"\u003e\u003c/a\u003e\n## Networks and Graphs :globe_with_meridians:\n| Title | Article | Repo |\n| --- | --- | --- |\n| :star: Network Analysis and Visualization of Drug-Drug Interactions | [🔗](https://towardsdatascience.com/network-analysis-and-visualization-of-drug-drug-interactions-1e0b41d0d3df?sk=4ef977b9ba9f726bf3d30549296ae1ec) | [🔗](https://github.com/kennethleungty/Drug-Interactions-Network-Analysis-and-Visualization) |\n| How to Deploy Interactive Pyvis Network Graphs on Streamlit | [🔗](https://towardsdatascience.com/how-to-deploy-interactive-pyvis-network-graphs-on-streamlit-6c401d4c99db?sk=a5ee66d9e548f2b69e309ac10a531eba) | [🔗](https://github.com/kennethleungty/Pyvis-Network-Graph-Streamlit) |\n| A No-Code Approach to Building Knowledge Graphs | [🔗](https://towardsdatascience.com/a-no-code-approach-to-building-knowledge-graphs-ce5d6b244b2b?sk=36c9171d5774e277a164618901ab5b94) | [🔗](https://blog.kgbase.com/a-no-code-approach-to-building-knowledge-graphs/) |\n| :star: Text-to-SQL with GraphRAG on Neo4j Knowledge Graph Semantic Representation of SQL Databases | *Coming Soon* | [🔗](https://github.com/kennethleungty/Text-to-SQL-with-KG-Neo4j-GraphRAG)\n\n___\n\u003ca name=\"responsible-ai\"\u003e\u003c/a\u003e\n## Responsible AI :police_officer:\n| Title | Article | Repo |\n| --- | --- | --- |\n| Responsible AI Masterclass (for Institute of Banking and Finance Singapore) | [🔗](https://www.linkedin.com/feed/update/urn:li:activity:7206097813410107392/) | [🔗](https://github.com/kennethleungty/Responsible-AI-Masterclass) |\n\n___\n\u003ca name=\"sports-analytics\"\u003e\u003c/a\u003e\n## Sports Analytics :soccer:\n| Title | Article | Repo |\n| --- | --- | --- |\n| :star: Analyzing English Premier League VAR Football Decisions | [🔗](https://towardsdatascience.com/analyzing-english-premier-league-var-football-decisions-c6d280061ebf?sk=4d968e99ff9ec1e1322bb69fc0af280c) | [🔗](https://github.com/kennethleungty/English-Premier-League-VAR-Analysis) |\n| Combining Python and R for FIFA Football World Ranking Analysis | [🔗](https://towardsdatascience.com/combining-python-and-r-for-fifa-football-world-ranking-analysis-d71bb6ceacdb) | [🔗](https://github.com/kennethleungty/FIFA-Football-World-Rankings) |\n\n___\n\u003ca name=\"visualization\"\u003e\u003c/a\u003e\n## Visualization :chart_with_upwards_trend:\n| Title | Article | Repo |\n| --- | --- | --- |\n| Uniform Singapore Energy Price and Demand Forecast Dashboard (with Plotly Dash) | - | [🔗](https://github.com/kennethleungty/Plotly-Dash-USEP-Dashboard) |\n| Visualizing Fortune 500 Companies in a Bar Chart Race | [🔗](https://towardsdatascience.com/the-fortune-500-bar-chart-race-9612dc9d0e63?sk=6ac0cffbe15e0d152bed9b6440c78a24) | [🔗](https://github.com/kennethleungty/Fortune-Global-500-Bar-Chart-Race) |\n| How to Easily Draw Neural Network Architecture Diagrams | [🔗](https://towardsdatascience.com/how-to-easily-draw-neural-network-architecture-diagrams-a6b6138ed875?sk=89e9e58c0b0c2e47525051f5f88d4048) | [🔗](https://github.com/kennethleungty/Neural-Network-Architecture-Diagrams) |\n\n___\n\u003ca name=\"web-development\"\u003e\u003c/a\u003e\n## Web Development :desktop_computer:\n| Title | Article | Repo |\n| --- | --- | --- |\n| :star: Post COVID-19 Vaccination Wait-Time Tracker (with Python Flask) | [🔗](https://www.linkedin.com/feed/update/urn:li:activity:6822717617636282368/) | [🔗](https://github.com/kennethleungty/Post-Vaccine-Timer) |\n| From HTTP to HTTPS — Easily Secure Flask Web Apps With Talisman | [🔗](https://betterprogramming.pub/from-http-to-https-easily-secure-flask-web-apps-with-talisman-3359692d3eac?sk=bdee663dbbb69bee8b40dd952a693e3e) | - |\n| :star: Food King Directory (in collaboration with Night Owl Cinematics) | [🔗](https://www.linkedin.com/feed/update/urn:li:activity:6889772186220019712/) | [🔗](https://directory.foodking.sg) |\n\n___\n\u003ca name=\"web3\"\u003e\u003c/a\u003e\n## Web3 and Metaverse :man_technologist:\n| Title | Article | Repo |\n| --- | --- | --- |\n| The Web3 / Metaverse Glossary — A Keyword Guide to the Tech Future | [🔗](https://medium.datadriveninvestor.com/the-web3-metaverse-glossary-a-keyword-guide-to-the-tech-future-42afedd9c3c0?sk=2515b48545a28d1d185608e405442aa5) | - |\n\n___\n\u003ca name=\"writing-for-datacamp\"\u003e\u003c/a\u003e\n## Writing for DataCamp :writing_hand:\n| Title | Article | Repo |\n| --- | --- | --- |\n| :star: What Mature Data Infrastructure Looks Like | [🔗](https://www.datacamp.com/community/blog/data-infrastructure-tools) | - |\n| Democratizing Data in Government Agencies | [🔗](https://www.datacamp.com/community/blog/democratizing-data-in-government-agencies) | - |\n| A Survey Into Data Governance Tools | [🔗](https://www.datacamp.com/community/blog/a-survey-into-data-governance-tools) | - |\n| Scaling Data Science With Data Governance | [🔗](https://www.datacamp.com/community/blog/scaling-data-science-with-data-governance) | - |\n| 3 Reasons Why All Teams Should Learn SQL | [🔗](https://www.datacamp.com/community/blog/why-your-organization-should-upskill-on-sql) | - |\n| 3 Reasons Why All Teams Should Learn R | [🔗](https://www.datacamp.com/community/blog/three-reasons-why-all-teams-should-learn-r) | - |\n| How Tableau Helps Your Organization Achieve Greater Data Insights | [🔗](https://www.datacamp.com/community/blog/why-your-organization-should-upskill-on-tableau) | - |\n| How PowerBI Helps Your Organization Achieve Greater Data Insights | [🔗](https://www.datacamp.com/community/blog/why-your-organization-should-upskill-on-powerbi) | - |\n\n___\n\u003ca name=\"writing-tips\"\u003e\u003c/a\u003e\n## Writing Tips :scroll:\n| Title | Article | Repo |\n| --- | --- | --- |\n| Create a Clickable Table of Contents for Your Medium Posts | [🔗](https://medium.com/geekculture/how-to-create-clickable-table-of-contents-for-your-medium-posts-e81e22f83142?sk=a3993bdd63a3faeb1f324e05007faad9)  | - |\n\n___\n    \n\u003c!--\n| Placeholder | [🔗](article_link) | [🔗](github_link) |\n\u003ca name=\" \"\u003e\u003c/a\u003e\n--\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkennethleungty%2Fkennethleungty","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkennethleungty%2Fkennethleungty","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkennethleungty%2Fkennethleungty/lists"}