{"id":20066899,"url":"https://github.com/anilkumarteegala/wqu-ds-unit-2","last_synced_at":"2026-03-02T20:31:17.407Z","repository":{"id":104670985,"uuid":"293681407","full_name":"AnilKumarTeegala/WQU-DS-Unit-2","owner":"AnilKumarTeegala","description":"This repo contains all the files material releated to WorldQuant University's Data Science Summer 2020 Session Unit 2: Machine Learning and Statistical Analysis","archived":false,"fork":false,"pushed_at":"2020-09-08T03:38:06.000Z","size":6,"stargazers_count":31,"open_issues_count":0,"forks_count":33,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-01-12T23:25:59.180Z","etag":null,"topics":["data-science","machine-learning","statistical-analysis","wqu"],"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/AnilKumarTeegala.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":"2020-09-08T02:34:45.000Z","updated_at":"2024-12-19T11:13:25.000Z","dependencies_parsed_at":null,"dependency_job_id":"eab7f84d-4618-4d78-84e9-f759507473d2","html_url":"https://github.com/AnilKumarTeegala/WQU-DS-Unit-2","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/AnilKumarTeegala%2FWQU-DS-Unit-2","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AnilKumarTeegala%2FWQU-DS-Unit-2/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AnilKumarTeegala%2FWQU-DS-Unit-2/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AnilKumarTeegala%2FWQU-DS-Unit-2/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/AnilKumarTeegala","download_url":"https://codeload.github.com/AnilKumarTeegala/WQU-DS-Unit-2/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241494180,"owners_count":19971871,"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":["data-science","machine-learning","statistical-analysis","wqu"],"created_at":"2024-11-13T14:00:20.986Z","updated_at":"2026-03-02T20:31:12.240Z","avatar_url":"https://github.com/AnilKumarTeegala.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# WQU-DS-Unit-2\nThis repo contains all the files material releated to WorldQuant University's Data Science Summer 2020 Session Unit 2: Machine Learning and Statistical Analysis\n\n## Syllabus\n\n1. Introduction to machine learning and Scikit-learn API\n2. Regression, classification, \u0026 model selection (miniproject: ml)\n3. Feature engineering\n4. NLP and dimension reduction (miniproject: nlp)\n5. KNeighbors, clustering and ensemble models\n6. Support vector machines\n7. Time series analysis and anomaly detection\n8. Clustering\n\n\n## Chapter Content Videos\n### Ch1. Introduction to machine learning and Scikit-learn API\n\n- [Introduction to Machine Learning](https://youtu.be/9J6FNvil6Gw)\n- [1.2.1 Intro to Scikit-learn](https://youtu.be/ecryKgFv5MA)\n- [1.2.2 Predictors](https://youtu.be/AHALKrsFdVw)\n- [1.2.3 Transformers and Pipeline](https://youtu.be/zVU4131QPsw)\n- [1.2.4 Feature Unions](https://youtu.be/_gOyCmdumps)\n- [1.2.5 Custom Transformers](https://youtu.be/I0YbI9x51kU)\n- [1.2.6 Custom Predictors](https://youtu.be/bxf2JHcH4UI)\n- [1.2.7 Exercise Distance Transformer](https://youtu.be/0zYnOu1axGQ)\n- [1.2.8 Exercise Majority Classifier](https://youtu.be/SGLjDeJqDb0)\n- [1.3.1 Persisting Your Model](https://youtu.be/V9Pm_EZvABA)\n- [1.3.2 Common Mistakes](https://youtu.be/YZQNIUIAi3A)\n### Ch2. Regression, classification, \u0026 model selection\n\n- [2.1.1 Regression Metrics](https://youtu.be/wlMrMvphMuw)\n- [2.2.1 Linear Regression Intro](https://youtu.be/L1h6cIG8XcA)\n- [2.2.2 Gradient Descent and Huber Loss](https://youtu.be/j0d4RogPiXM)\n- [2.2.3 Multivariate Regression](https://youtu.be/9YZbXipwAQg)\n- [2.2.4 Feature Importance](https://youtu.be/x5XB1ynjCGI)\n- [2.3.1 Classification Metrics](https://youtu.be/SMmbzWn8yGI)\n- [2.3.2 Probabilistic Models and Metrics](https://youtu.be/4MgWh8oD-hQ)\n- [2.3.3 Logistic Regression](https://youtu.be/oqj-0_4WKq4)\n- [2.3.4 Multiclassification](https://youtu.be/gOVP8c1Cmmg)\n- [2.4.1 Model Selection](https://youtu.be/lEBStVJXXpM)\n- [2.4.2 Intro to Decision Trees](https://youtu.be/D6tNPIXCI1o)\n- [2.4.3 Underfitting and Overfitting](https://youtu.be/ZhhvoUAhA80)\n- [2.4.4 GridSearchCV](https://youtu.be/cWqE82yQi1Y)\n- [2.4.5 Comparing Two Models](https://youtu.be/rZ8XGPm-i1o)\n- [2.5.1 Imputation](https://youtu.be/vJ3WBCW2sas)\n- [2.5.2 Categorical Data](https://youtu.be/qOAoV8HK8e0)\n- [2.6.1 GridsearchCV and Pipelines 1](https://youtu.be/c8ZJMXM6vvo)\n- [2.6.2 GridsearchCV and Pipelines 2](https://youtu.be/mqnG1yANXvo)\n- [2.6.3 RandomizedSearchCV](https://youtu.be/BXzf0gJuV4w)\n\n### Ch3 Feature Enginerring \u0026 KNN\n\n- [3.1.1 Feature Engineering and Extraction](https://youtu.be/kHDRaKe2B5A)\n- [3.1.2 Feature Transformation](https://youtu.be/YAJsJNS3DAA)\n- [3.1.3 Curse of Dimensionality](https://youtu.be/HvG45qVJM84)\n- [3.1.4 Regularization](https://youtu.be/ArJqhJ415d4)\n- [3.1.5 Multicollinearity and PCA](https://youtu.be/8eBKta-D334)\n- [3.1.6 Ensemble Models](https://youtu.be/oUOi5T1b_iU)\n- [3.2.1 Bias and Variance](https://youtu.be/-ZLg6Zp9HHg)\n- [3.2.2 Learning Curves](https://youtu.be/KB4fPj68Rbo)\n- [3.3.1 Intro KNN](https://youtu.be/EV6xlHdTaEY)\n- [3.3.2 KNN Bias and Variance](https://youtu.be/AXbhiyWJZdw)\n- [3.3.3 KNN Time Complexity](https://youtu.be/XleyueB7jXU)\n- [3.3.4 KD Trees and Weights](https://youtu.be/UV1WeqUFPE8)\n- [3.4.1 Intro to NLP](https://youtu.be/w3HtykbMcqk)\n- [3.4.2 Spacy](https://youtu.be/7nojkNN0EME)\n- [3.4.3 Obtaining a Corpus](https://youtu.be/4mYBqqbW408)\n- [3.4.4 Bag of Words Model](https://youtu.be/7bKNknkmIJI)\n- [3.4.5 Hashing Vectorizer](https://youtu.be/_wPHgQhiMVY)\n- [3.4.6 TF-IDF](https://youtu.be/kv9cE-uOnis)\n- [3.4.7 Improving Signal](https://youtu.be/UR4JcVOBiAI)\n- [3.4.8 N-grams and Similarity](https://youtu.be/tuwCNPEFRDs)\n- [3.4.9 Word Usage Classifier](https://youtu.be/FNU4dNeKrHo)\n- [3.4.10 Exercise I](https://youtu.be/kAZuP3efPbg)\n- [3.4.11 Exercise II](https://youtu.be/MkpFJkSrC2o)\n- [3.4.12 Exercise III and IV](https://youtu.be/Uw1_iZgihC0)\n\n### Ch4 Decision Trees \u0026 Gradient Boosting\n- [4.1.1 Intro to Decision Trees](https://youtu.be/4cIr8W9tXD8)\n- [4.1.2 Tree Error Metrics](https://youtu.be/Pv8bnN3E4xA)\n- [4.1.3 Trees for Regression](https://youtu.be/LvUazyMSRFM)\n- [4.1.4 Training Trees and Hyperparameters](https://youtu.be/nC-pHG_hgdY)\n- [4.1.5 Geometric Interpretation and Time Complexity](https://youtu.be/l23BnV-4Xwc)\n- [4.1.6 Time Complexity Continued](https://youtu.be/oD_INvReM_I)\n- [4.1.7 Random Forests](https://youtu.be/JUzss0-pvz8)\n- [4.1.8 Extreme Random Forests](https://youtu.be/GLjopN8Lw94)\n- [4.1.9 Gradient Boosting Trees I](https://youtu.be/KJtV7fTrFH4)\n- [4.1.10 Gradient Boosting Trees II](https://youtu.be/E2R4D2Gc4x4)\n- [4.1.11 Feature Importance](https://youtu.be/WDLvgw-Znmg)\n- [4.1.12 Exercises](https://youtu.be/7q3pk2VcDFQ)\n\n### Ch5 SVM \u0026 Clustering\n- [5.1.1 Intro to SVM](https://youtu.be/GxSSq1B-BHg)\n- [5.1.2 Largest Margin Classifier](https://youtu.be/EK-69gK8y9E)\n- [5.1.3 Soft Margin Classifier](https://youtu.be/QT-Flust1Hc)\n- [5.1.4 SVM Kernels](https://youtu.be/VQWkkPV1Q_Q)\n- [5.1.5 SVM vs Logistic Regression](https://youtu.be/4G7uEZCAr2U)\n- [5.1.6 SVM Regression](https://youtu.be/xp8TJgzgWp0)\n- [5.1.7 SVM Lagrangian Dual](https://youtu.be/defq3yJ8cyw)\n- [5.1.8 Kernel Trick](https://youtu.be/YcRcxm4LSqg)\n- [5.1.9 SVM Time Complexity and Multiclass](https://youtu.be/FR5D4A3Nn0c)\n- [5.1.10 SVM Tuning Kernels Exercise Part I](https://youtu.be/MggSYvtMcLQ)\n- [5.1.11 SVM Tuning Kernels Exercise Part II](https://youtu.be/tvWb6XObsFg)\n- [5.1.12 SVM Kernel Approximations](https://youtu.be/A3HPwA0IWIM)\n- [5.1.13 SVM Online Learning](https://youtu.be/bgzt8UL8x4Y)\n- [5.1.14 SVM Online Learning Pipeline](https://youtu.be/E1RoFHjJRDc)\n- [5.2.1 Intro to Clustering](https://youtu.be/HktZtB7Te0c)\n- [5.2.2 Metrics for Clustering](https://youtu.be/kWsylUt8LxA)\n- [5.2.3 KMeans Clustering](https://youtu.be/_OKUAiC9FLY)\n- [5.2.4 Elbow Plots](https://youtu.be/J_jH7cXGUSQ)\n- [5.2.5 Gaussian Mixture Models](https://youtu.be/WwDiKfHW52U)\n- [5.2.6 Choosing Cluster Based on Silhouette](https://youtu.be/Gj4HHh4dDEk)\n- [5.2.7 GMM Choosing Number of Components](https://youtu.be/QO3F4lBs4m4)\n### Ch6 Time Series Analysis \u0026 Dimensionality Reduction\n- [\t6.1.1 Intro to Time Series](https://youtu.be/BvDcWoLnWFk)\n- [\t6.1.2 Crossvalidation in Time Series](https://youtu.be/ZOJAof-3YYA)\n- [\t6.1.3 Stationary Signal](https://youtu.be/WytJdsQBeos)\n- [\t6.1.4 Modeling Drift](https://youtu.be/53FLb9usJBk)\n- [\t6.1.5 Fourier Transforms Part I](https://youtu.be/k-cF7LB7p4w)\n- [\t6.1.6 Fourier Transforms Part II](https://youtu.be/7KSdhV2elq8)\n- [\t6.1.7 Fourier Components in our Model](https://youtu.be/ek3vt4xLL-k)\n- [\t6.1.8 Modeling Noise](https://youtu.be/IBBcCxCPv1w)\n- [\t6.1.9 Moving Statistics](https://youtu.be/8LUt3PBcCFk)\n- [\t6.1.10 Full Model](https://youtu.be/oIIE1Fh8eNc)\n- [\t6.1.11 ARMA and ARIMA](https://youtu.be/UPox0nn4IvE)\n- [\t6.1.12 AR Example](https://youtu.be/GaQ70ZHo69s)\n- [\t6.2.1 Intro to Dimension Reduction](https://youtu.be/b5BihaS90q0)\n- [6.2.2 Math of Projections](https://youtu.be/CZjSrCpkxxk)\n- [6.2.3 PCA](https://youtu.be/dCYH6MVyfSA)\n- [6.2.4 PCA in Scikit Learn](https://youtu.be/lSxcJbaRvkA)\n- [6.2.5 PCA Implementation Details](https://youtu.be/QQl4h1gyCwc)\n- [6.2.6 Choosing the Number of Components](https://youtu.be/4azFI2QzyXU)\n- [6.2.7 Truncated SVD](https://youtu.be/nfwInvCoym0)\n- [6.2.8 NMF](https://youtu.be/ELLDyWiiSXU)\n- [6.2.9 Using PCA with Supervised ML](https://youtu.be/u8tUlG28CH0)\n- [6.2.10 PCA for Visualization](https://youtu.be/MRwKUI5ulio)\n- [6.2.11 NMF Exercise Part I](https://youtu.be/oo1sDDHE-DM)\n- [6.2.12 NMF Exercise Part II](https://youtu.be/PQt9XPS_bT0)\n- [6.2.13 Variants of PCA](https://youtu.be/0KdHFPXsonE)\n\n### Ch7 Anomaly Detection\n- [7.1.1 Intro to Anomaly Detection](https://youtu.be/KuFUQ7wWhsY)\n- [7.1.2 One class SVM](https://youtu.be/2l0TD7gCzvQ)\n- [7.1.3 Isolation Forest](https://youtu.be/xIAwOj_xh9s)\n- [7.1.4 Comparison Between One-class SVM and Isolation Forest](https://youtu.be/VIyK4gLB2hg)\n- [7.1.5 Intro to Case Study](https://youtu.be/TQk9uSFMo6w)\n- [7.1.6 Initial Baseline Model Part I](https://youtu.be/qrJTbKyv1eA)\n- [7.1.7 Initial Baseline model Part II](https://youtu.be/HMRoWtI6eMo)\n- [7.1.8 Full Baseline Model](https://youtu.be/gTyO2ldQfWc)\n- [7.1.9 Z-score Detection](https://youtu.be/d2YIby-isCE)\n- [7.1.10 Rolling Z-score Detection](https://youtu.be/cQ2YIZmeQKg)\n- [7.1.11 Using External Features Initial Model](https://youtu.be/_SvveHx_Nfk)\n- [7.1.12 Using External Features Tuning the Model](https://youtu.be/2Z5SQsx4LFE)\n- [7.1.13 Packaging the Time Series Anomaly Detector](https://youtu.be/hTWHZTTbmJk)\n### Ch8 Model Deployment\n- [8.1.1 Model Considerations](https://youtu.be/SFFFnqugdOQ)\n- [8.1.2 Model Development](https://youtu.be/DrClE-USVDg)\n- [8.1.3 Flask App Local Development](https://youtu.be/chRA6Lngw-A)\n- [8.1.4 GET requests](https://youtu.be/v1yYLbB_uUE)\n- [8.1.5 Making GET Requests with Model](https://youtu.be/q9DgF7mJbrc)\n- [8.1.6 Using our Model with Twitter Web API](https://youtu.be/AFwGAeBDeEk)\n- [8.1.7 POST Requests and Flask Templates](https://youtu.be/qbi3aT0KEsA)\n- [8.1.8 Preparing for Deployment to the Web](https://youtu.be/zqbIVwnA0go)\n- [8.1.9 Deploying our App to the Web with Heroku](https://youtu.be/ALlhG0pjx1w)\n- [8.2.1 Rethinking Model Tuning](https://youtu.be/TPcytvHTQoA)\n- [8.2.2 Intro to Bayes Theorem](https://youtu.be/bzsLD59kF2g)\n- [8.2.3 Bayesian Inference](https://youtu.be/i6EyxNl_mdk)\n- [8.2.4 Bayesian Optimization](https://youtu.be/GIQL38tkyRs)\n- [8.3.1 End of Course Material](https://youtu.be/htdGrsIMZkg)\n\n### Office Hours\n- [LIVE Streaming Office Hours](https://www.youtube.com/channel/UCW5qH1I2RMA0CnJ0uo7OTvQ/live)\n- [LIVE Streaming Office Hours](https://www.youtube.com/channel/UCW5qH1I2RMA0CnJ0uo7OTvQ/live)\n- [LIVE Streaming Office Hours](https://www.youtube.com/channel/UCW5qH1I2RMA0CnJ0uo7OTvQ/live)\n- [LIVE Streaming Office Hours](https://www.youtube.com/channel/UCW5qH1I2RMA0CnJ0uo7OTvQ/live)\n- [OFFICE HOURS PLAYLIST](https://www.youtube.com/playlist?list=PLeDYvCW3J3jmrHB7ESo8hZ5XqlYdB3SnV)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanilkumarteegala%2Fwqu-ds-unit-2","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fanilkumarteegala%2Fwqu-ds-unit-2","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanilkumarteegala%2Fwqu-ds-unit-2/lists"}