Build a Demand Forecasting ML App: Python Data Science
ML QUEST #1: Demand Forecasting - Full course on Patreon
► / ml-quest-1-demand-forecasting-full-344393
ML QUEST #1: Demand Forecasting - GitHub source code
► https://github.com/zazencodes/ml-ques...
Second Brain - Newsletter sign up for free access
► https://zazencodes.substack.com/
0:00 - Introduction to ML Quest #1 - Demand Forecasting
10:18 - Understanding the Dataset & the Problem to Model
13:54 - Data Exploration - Loading the Dataset
20:56 - Data Exploration - Handling Missing Data
27:21 - Data Exploration - Anomaly and Outlier Detection
38:30 - Data Exploration - Temporal Coverage and Distribution Analysis
54:23 - Feature Engineering - Our Goal & General Considerations
59:36 - Feature Engineering - One-Hot Encoding
1:05:38 - Feature Engineering - Custom Ordered Categorical Encoder
1:14:48 - Feature Engineering - Numeric & Date Features
1:19:17 - Feature Engineering - Moving Average & Lag Features
1:39:29 - Bonus - Data Imputation for Temporal Gaps
1:52:25 - Modeling - Introduction
1:53:15 - Modeling - Create a Model Using Simple Heuristics
1:56:26 - Modeling - General Considerations
1:59:10 - Modeling - Grid Search for Hyperparameter Optimization
2:02:30 - Modeling - Gradient Boosted Regression
2:04:10 - Modeling - Train-Test Split for Timeseries Data
2:05:49 - Modeling - Grid Search Continued
2:08:10 - Modeling - Visualizing & Interpreting Grid Search Results
2:15:00 - Modeling - Training the "Final Model"
2:19:46 - Modeling - Training a Stack of Models
2:25:03 - Modeling - Generating the Forecast
2:34:03 - Modeling - Visualizing the Forecast
2:41:06 - Modeling - Uploading Forecast to Postgres
2:50:38 - Hands-On App Implementation - Introduction
2:51:45 - Hands-On App Implementation - ML App Overview
2:55:55 - Hands-On App Implementation - Docker Compose App Overview
3:00:24 - Hands-On App Implementation - Data Loading & Cleanup Module
3:09:50 - Hands-On App Implementation - Feature Encoding Module
3:20:57 - Hands-On App Implementation - Numeric Features Module
3:27:04 - Hands-On App Implementation - Model Training Module
3:35:45 - Hands-On App Implementation - Model Forecast Module
3:49:49 - Hands-On App Implementation - Forecast Database Integration
3:56:45 - ML App Demo / Walk-Through - Model Library Docker App
4:02:42 - ML App Demo / Walk-Through - Model Train Module
4:10:03 - ML App Demo / Walk-Through - Running the Model Train Module
4:17:13 - ML App Demo / Walk-Through - Model Forecast Module
4:27:49 - ML App Demo / Walk-Through - Running the Model Forecast Module
4:30:08 - User App Demo / Walk-Through - Model API with FastAPI on Docker
4:43:40 - User App Demo / Walk-Through - Model Dashboard with Streamlit on Docker
4:48:26 - User App Demo / Walk-Through - Model Dashboard Insights
4:54:20 - User App Demo / Walk-Through - Streamlit Dashboard Code
5:14:34 - User App Demo / Walk-Through - Course Summary & Conclusion
Смотрите видео Build a Demand Forecasting ML App: Python Data Science онлайн без регистрации, длительностью часов минут секунд в хорошем качестве. Это видео добавил пользователь ZazenCodes 07 Сентябрь 2024, не забудьте поделиться им ссылкой с друзьями и знакомыми, на нашем сайте его посмотрели 385 раз и оно понравилось 29 людям.