{"id":13599295,"url":"https://github.com/edyoda/data-science-complete-tutorial","last_synced_at":"2025-05-15T14:04:39.946Z","repository":{"id":38151489,"uuid":"148876755","full_name":"edyoda/data-science-complete-tutorial","owner":"edyoda","description":"For extensive instructor led learning","archived":false,"fork":false,"pushed_at":"2022-10-31T09:44:29.000Z","size":57561,"stargazers_count":1804,"open_issues_count":12,"forks_count":762,"subscribers_count":65,"default_branch":"master","last_synced_at":"2025-04-15T03:53:55.555Z","etag":null,"topics":["decision-trees","feature-selection","linear-regression","machine-learning","nearest-neighbors","numpy","pandas","pipeline","scikit-learn"],"latest_commit_sha":null,"homepage":"https://www.edyoda.com/program/data-scientist-program","language":"Jupyter Notebook","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/edyoda.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}},"created_at":"2018-09-15T06:37:32.000Z","updated_at":"2025-03-23T22:46:41.000Z","dependencies_parsed_at":"2023-01-19T15:15:50.207Z","dependency_job_id":null,"html_url":"https://github.com/edyoda/data-science-complete-tutorial","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/edyoda%2Fdata-science-complete-tutorial","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/edyoda%2Fdata-science-complete-tutorial/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/edyoda%2Fdata-science-complete-tutorial/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/edyoda%2Fdata-science-complete-tutorial/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/edyoda","download_url":"https://codeload.github.com/edyoda/data-science-complete-tutorial/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":254355334,"owners_count":22057354,"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":["decision-trees","feature-selection","linear-regression","machine-learning","nearest-neighbors","numpy","pandas","pipeline","scikit-learn"],"created_at":"2024-08-01T17:01:01.888Z","updated_at":"2025-05-15T14:04:34.936Z","avatar_url":"https://github.com/edyoda.png","language":"Jupyter Notebook","funding_links":[],"categories":["Jupyter Notebook"],"sub_categories":[],"readme":"In person training - https://www.edyoda.com/program/data-scientist-program\n\n# Machine Learning Git Codebook\n\n**Lesson 1 :** [Introduction to Numpy](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/1.%20NumPy.ipynb) [(Video)](https://www.edyoda.com/resources/videolisting/1263/)  \n**Lesson 2 :** [Data Wrangling using Pandas](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/2.%20Pandas%20for%20Machine%20Learning.ipynb)  \n**Lesson 3 :** [Plotting in Python](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/3.%20Plotting.ipynb)  \n**Lesson 4 :** [Linear Models for Regression \u0026 Classification](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/4.%20Linear%20Models%20for%20Classification%20%26%20Regression.ipynb)  \n**Lesson 5 :** [Preprocessing Data](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/5.%20PreProcessing.ipynb)  \n**Lesson 6 :** [Decision Trees](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/6.%20Decision%20Tree.ipynb)  \n**Lesson 7 :** [Naive Bayes](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/7.%20Naive%20Bayes.ipynb)  \n**Lesson 8 :** [Composite Estimators](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/8.%20Composite%20Estimators%20using%20Pipelines%20%26%20FeatureUnions.ipynb)  \n**Lesson 9 :** [Model Selection and Evaluation](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/9.%20Model%20Selection%20%26%20Evaluation.ipynb)  \n**Lesson 10 :** [Feature Selection Techniques](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/10.%20Feature%20Selection%20Techniques.ipynb)  \n**Lesson 11 :** [Nearest Neighbors](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/11.%20Nearest%20Neighbors.ipynb)  \n**Lesson 12 :** [Clustering Techniques](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/12.%20Clustering%20Techniques.ipynb)  \n**Lesson 13 :** [Anomaly Detection](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/13.%20Anomaly%20Detection.ipynb)  \n**Lesson 14 :** [Support Vector Machines](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/14.%20Support%20Vector%20Machines.ipynb)  \n**Lesson 15 :** [Dealing with Imbalanced Classes](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/15.%20Dealing%20with%20Imbalanced%20Classes.ipynb)  \n**Lesson 16 :** [Ensemble Methods](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/16.%20Ensemble%20Methods.ipynb)  \n\n\n## Case Study of Classic ML Problems\n**Case 1 :** [Linear Regression](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/LR%20Example.ipynb)  \n**Case 2 :** [Cancer Prediction](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/Cancer%20Prediction.ipynb)  \n**Case 3 :** [Online Learning](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/Online%20Learning.ipynb)  \n**Case 4 :** [Customer Churn Prediction](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/Project%20-%20Customer%20Churn%20Prediction.ipynb)  \n**Case 5 :** [Income Prediction](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/Project%20-%20Income%20Prediction.ipynb)  \n**Case 6 :** [Predicting Employee Exit](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/Project%20-%20Predicting%20Employee%20Exit.ipynb)  \n**Case 7 :** [Face Generation](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/Project%20-%20Face%20Generation.ipynb)  \n**Case 8 :** [Finding Similar Houses](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/Project%20-%20Finding%20Similar%20Houses.ipynb)  \n\n## The Free courses available on EdYoda\n\n**Python** - https://www.edyoda.com/course/98 \n\n**Angular** - https://www.edyoda.com/course/1227\n\n**Machine Learning** - https://www.edyoda.com/course/1416 \n\n**Dog Breed Prediction Project** - https://www.edyoda.com/course/1336  \n\n**AI Project - Web application for Object Identification** - https://www.edyoda.com/course/1185  \n\n**Numpy** - https://www.edyoda.com/course/1263 \n\n**Tensorflow** - https://www.edyoda.com/course/99  \n\n**Amazon Web Services** - https://www.edyoda.com/course/1410  \n\n**DevOps** - https://www.edyoda.com/course/100 \n\n**Android** -   \nhttps://www.edyoda.com/course/101  \nhttps://www.edyoda.com/course/1173   \n\n**Deep Reinforcement Learning** - https://www.edyoda.com/course/1421  \n\n**Knowledge Graphs, Deep Learning, Reasoning** - https://www.edyoda.com/course/1420 \n\n**Natural Language Processing** - https://www.edyoda.com/course/1419 \n\n**GAN Miniseries** - https://www.edyoda.com/course/1418\n\n**Implementing Java Api's work** - https://www.edyoda.com/channel/2398/ \n\n**Introduction to Neural Nets** - https://www.edyoda.com/channel/2500/\n\n**Videos from deep cognition studio** - https://www.edyoda.com/channel/2380/  \n\n## About Us\nWe want to democratize education and create free quality course content.\n\t\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fedyoda%2Fdata-science-complete-tutorial","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fedyoda%2Fdata-science-complete-tutorial","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fedyoda%2Fdata-science-complete-tutorial/lists"}