{"id":21959903,"url":"https://github.com/cricksmaidiene/mids_machine_learning","last_synced_at":"2025-10-10T09:32:19.808Z","repository":{"id":230852064,"uuid":"735903852","full_name":"cricksmaidiene/mids_machine_learning","owner":"cricksmaidiene","description":"🤖 A unified repository of coursework fragments from UC Berkeley MIDS ML courses","archived":false,"fork":false,"pushed_at":"2024-04-16T11:57:28.000Z","size":8187,"stargazers_count":5,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2024-11-29T09:42:25.384Z","etag":null,"topics":["coursework","data-science","generative-ai","jupyter-notebook","machine-learning","numpy","pandas","prompt-engineering","scikit-learn","spark","tensorflow","uc-berkeley"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/cricksmaidiene.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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":"2023-12-26T12:13:14.000Z","updated_at":"2024-08-23T18:52:16.000Z","dependencies_parsed_at":"2024-04-01T07:25:50.492Z","dependency_job_id":"25d86f5f-d892-4772-9ab2-edb75fd93de0","html_url":"https://github.com/cricksmaidiene/mids_machine_learning","commit_stats":null,"previous_names":["cricksmaidiene/mids_machine_learning"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cricksmaidiene%2Fmids_machine_learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cricksmaidiene%2Fmids_machine_learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cricksmaidiene%2Fmids_machine_learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cricksmaidiene%2Fmids_machine_learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/cricksmaidiene","download_url":"https://codeload.github.com/cricksmaidiene/mids_machine_learning/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":235941545,"owners_count":19069664,"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":["coursework","data-science","generative-ai","jupyter-notebook","machine-learning","numpy","pandas","prompt-engineering","scikit-learn","spark","tensorflow","uc-berkeley"],"created_at":"2024-11-29T09:34:56.019Z","updated_at":"2025-10-10T09:32:13.617Z","avatar_url":"https://github.com/cricksmaidiene.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Machine Learning @MIDS - UC Berkeley I School  🏫\n\n![](https://img.shields.io/badge/TensorFlow-FF6F00.svg?style=for-the-badge\u0026logo=TensorFlow\u0026logoColor=white)\n![](https://img.shields.io/badge/PyTorch-EE4C2C.svg?style=for-the-badge\u0026logo=PyTorch\u0026logoColor=white)\n![](https://img.shields.io/badge/scikitlearn-F7931E.svg?style=for-the-badge\u0026logo=scikit-learn\u0026logoColor=white)\n![](https://img.shields.io/badge/NumPy-013243.svg?style=for-the-badge\u0026logo=NumPy\u0026logoColor=white)\n\n- [Machine Learning @MIDS - UC Berkeley I School  🏫](#machine-learning-mids---uc-berkeley-i-school--)\n  - [📚 Coursework](#-coursework)\n  - [📙 Notebooks](#-notebooks)\n    - [🖊 Generative AI (`DATASCI 290`)](#-generative-ai-datasci-290)\n    - [🤖 Applied Machine Learning (`DATASCI 207`)](#-applied-machine-learning-datasci-207)\n    - [📕 Basic Notebooks](#-basic-notebooks)\n\n---\n\nA unified repository of coursework fragments from [UC Berkeley MIDS Program](https://www.ischool.berkeley.edu/programs/mids) 2022-2024. This is a collection of my adapted assignment submissions, classwork notebooks, and other relevant materials. It serves to showcase the scope of work, and also for personal reference.\n\n\u003e 🏗 Under Construction - Notebook Addition in Progress\n\n## 📚 Coursework\n\nI've taken the following core ML courses during my time at MIDS:\n\n- #### 🤖 [DATASCI 207: Applied Machine Learning - Fall 2022](https://ischoolonline.berkeley.edu/data-science/curriculum/applied-machine-learning/)\n\n  - Course Info: Barebones ML, Breadth of Models\n  - Course Project: 🍃 [Leafydex - Leaf Classification](https://github.com/cricksmaidiene/leafydex)\n\n- #### ⚡️ [DATASCI 261: Machine Learning at Scale - Spring 2023](https://ischoolonline.berkeley.edu/data-science/curriculum/machine-learning-at-scale/)\n\n  - Course Info: Data Engineering \u0026 Model Training with Apache Spark\n  - Course Project: 🛬 [US Flight Delay Prediction](https://github.com/cricksmaidiene/flight_delay_prediction)\n\n- #### 📰 [DATASCI 266: Natural Language Processing - Fall 2023](https://ischoolonline.berkeley.edu/data-science/curriculum/natural-language-processing/)\n\n  - Course Info: Neural Network Models with Transformers\n  - Course Project: 🏂 [Snowplough - News Topic Classification \u0026 Bias Analysis](https://github.com/cricksmaidiene/snowplough)\n\n- #### 🖊 [DATASCI 290: Generative AI - Spring 2024](https://www.ischool.berkeley.edu/courses/datasci/290/genai)\n\n  - Course Info: LLMs, Stable Difussion, RAGs, Prompt Engineering\n\n## 📙 Notebooks\n\n### 🖊 Generative AI (`DATASCI 290`)\n\n| Notebook | Description |\n| --- | --- |\n| [Stable Diffusion \u0026 Image Validation](./src/generative_ai/stable_diffusion_and_image_validation_mids_290_gen_ai.ipynb) | Multimodal image generation and captioning with `diffusers`, `CLIP`, `BLIP` and `Llava` |\n| [Prompt Engineering](./src/generative_ai/prompt_engineering_mids_290_gen_ai.ipynb) | Prompt Engineering Examples with `Mistral7B` |\n| [Retrieval Augmented Generation Proof-of-Concept](./src/generative_ai/retrieval_augmented_generation_poc.ipynb) | Google Colab notebook and report using `Mistal7B`, `Cohere` and `Qdrant` to develop a simple RAG system and iterate on performance |\n\n### 🤖 Applied Machine Learning (`DATASCI 207`)\n\n| Notebook | Description |\n| --- | --- |\n| [Introduction to Supervised Learning](./src/applied_ml/datasci_207_notebook_01.ipynb) | Road to Linear Regression with `Generalization` and `MSE` (Mean Squared Error) calculation |\n\n### 📕 Basic Notebooks\n\n| Notebook | Description |\n| --- | --- |\n| [PyTorch Introduction](./src/neural_networks/pytorch_introduction.ipynb) | A basic introduction to tensors, classes, and operations in PyTorch |\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcricksmaidiene%2Fmids_machine_learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcricksmaidiene%2Fmids_machine_learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcricksmaidiene%2Fmids_machine_learning/lists"}