17 OCT 2024 - Welcome Back to TorrentFunk! Get your pirate hat back out. Streaming is dying and torrents are the new trend. Account Registration works again and so do Torrent Uploads. We invite you all to start uploading torrents again!
• Use autocorrelation to build time-series features.
• Detect and remove seasonal trends.
• Handle missing values.
• Download and ingest csv-formatted data.
• Handle dates in with a custom python converter.
• Evaluate a time-series model's performance.
Requirements
• It will help if you have used python before.
Description
Welcome!
In this course, we'll walk through every step of making your own weather predictor. We'll find weather data, explore it and get it in order. We'll use the modeling tools of deseasonalization and linear regression to predict temperatures at the beach. We'll use the statistical tools of autoregression and confidence intervals to guide our feature selection and apply our results. And we'll code the whole thing up from scratch in python and organize it to be easy to read and easy to extend.
When you're done, you'll have a standalone weather predictor that can estimate high temperatures three days from now. You'll also have hands-on experience solving a real word data science problem from end to end.
If you are a professor or a teacher at any level, you are welcome to evaluate the course for free, and I can set your students up with a deep educational discount.
Who is the target audience?
• Machine learning students and data scientists seeking project-based time series modeling and autocorrelation instruction.
For More Udemy Free Courses >>> http://www.freetutorials.eu
For more Lynda and other Courses >>> https://www.freecoursesonline.me/
Our Forum for discussion >>> https://discuss.freetutorials.eu/
VISITOR COMMENTS (0 )
FILE LIST
Filename
Size
1. Introduction/1. Introduction.mp4
85.8 MB
1. Introduction/1. Introduction.vtt
4.6 KB
1. Introduction/1.1 fort_lauderdale.csv a copy of the raw data.html
161 B
1. Introduction/1.2 buy_tickets.py the script for deciding whether to buy plane tickets(1).html
146 B
1. Introduction/1.2 buy_tickets.py the script for deciding whether to buy plane tickets.html
146 B
1. Introduction/1.3 predict_weather.py the weather prediction model(1).html
150 B
1. Introduction/1.3 predict_weather.py the weather prediction model.html
150 B
1. Introduction/1.4 tools.py a couple of tools that might be useful later(1).html
140 B
1. Introduction/1.4 tools.py a couple of tools that might be useful later.html
140 B
2. Get your data/1. Ask a sharp question.mp4
49.9 MB
2. Get your data/1. Ask a sharp question.vtt
4 KB
2. Get your data/1.1 Florida State University’s Florida Climate Center.html
113 B
2. Get your data/2. Get weather data.mp4
12.6 MB
2. Get your data/2. Get weather data.vtt
1.8 KB
2. Get your data/2.1 Florida State University’s Florida Climate Center(1).html
113 B
2. Get your data/2.1 Florida State University’s Florida Climate Center.html
0 B
2. Get your data/2.1 Florida State University’s Florida Climate Centerr.html
113 B
2. Get your data/3. Inspect the data.mp4
135.5 MB
2. Get your data/3. Inspect the data.vtt
11.3 KB
2. Get your data/4. Load the data.mp4
11.3 MB
2. Get your data/4. Load the data.vtt
2.4 KB
2. Get your data/5. Convert the data to lists.mp4
42.6 MB
2. Get your data/5. Convert the data to lists.vtt
6.6 KB
2. Get your data/6. Replace missing data with NaNs.mp4
39 MB
2. Get your data/6. Replace missing data with NaNs.vtt
5.1 KB
2. Get your data/7. Replace NaNs with estimates.mp4
46.8 MB
2. Get your data/7. Replace NaNs with estimates.vtt
7 KB
3. Find your features/1. How autocorrelation works.mp4
49.8 MB
3. Find your features/1. How autocorrelation works.vtt
12.8 KB
3. Find your features/2. Find the autocorrelation.mp4
46.5 MB
3. Find your features/2. Find the autocorrelation.vtt
6 KB
3. Find your features/3. Inspect the autocorrelation.mp4
22.4 MB
3. Find your features/3. Inspect the autocorrelation.vtt
4.5 KB
3. Find your features/4. Write a day-of-year calculator.mp4
95.8 MB
3. Find your features/4. Write a day-of-year calculator.vtt
11.1 KB
3. Find your features/5. Debug glitch in annual trend.mp4
66.8 MB
3. Find your features/5. Debug glitch in annual trend.vtt
8 KB
4. Build your model/1. Create seasonal model.mp4
66.1 MB
4. Build your model/1. Create seasonal model.vtt
5.9 KB
4. Build your model/2. Explore deseasonalized residuals.mp4
32.2 MB
4. Build your model/2. Explore deseasonalized residuals.vtt
3.4 KB
4. Build your model/3. Make three-day-out predictions.mp4
71.8 MB
4. Build your model/3. Make three-day-out predictions.vtt
7.3 KB
4. Build your model/4. Build the full model and refactor the code.mp4
77 MB
4. Build your model/4. Build the full model and refactor the code.vtt
7.2 KB
5. Deploy your model/1. Choose your decision criterion.mp4
90.7 MB
5. Deploy your model/1. Choose your decision criterion.vtt
7.5 KB
5. Deploy your model/2. Create a Predictor class with tests.mp4
63.3 MB
5. Deploy your model/2. Create a Predictor class with tests.vtt
4.7 KB
5. Deploy your model/3. Create a buy_tickets module to answer the question.mp4
51.4 MB
5. Deploy your model/3. Create a buy_tickets module to answer the question.vtt
![[FreeTutorials.Eu] [UDEMY] End-to-end Machine Learning Time-series analysis - [FTU]](https://f.t0r.site/cover/17963923.webp)
