[UDEMY] End-to-end Machine Learning Time-series analysis - [FTU]

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[FreeTutorials.Eu] [UDEMY] End-to-end Machine Learning Time-series analysis - [FTU] 1. Introduction
  • 1.1 fort_lauderdale.csv a copy of the raw data.html (0.2 KB)
  • 1.2 buy_tickets.py the script for deciding whether to buy plane tickets(1).html (0.1 KB)
  • 1.2 buy_tickets.py the script for deciding whether to buy plane tickets.html (0.1 KB)
  • 1.3 predict_weather.py the weather prediction model(1).html (0.1 KB)
  • 1.3 predict_weather.py the weather prediction model.html (0.1 KB)
  • 1.4 tools.py a couple of tools that might be useful later(1).html (0.1 KB)
  • 1.4 tools.py a couple of tools that might be useful later.html (0.1 KB)
  • 1. Introduction.mp4 (85.8 MB)
  • 1. Introduction.vtt (4.6 KB)
2. Get your data
  • 1.1 Florida State University’s Florida Climate Center.html (0.1 KB)
  • 1. Ask a sharp question.mp4 (49.9 MB)
  • 1. Ask a sharp question.vtt (4.0 KB)
  • 2.1 Florida State University’s Florida Climate Center(1).html (0.1 KB)
  • 2.1 Florida State University’s Florida Climate Center.html (0.0 KB)
  • 2.1 Florida State University’s Florida Climate Centerr.html (0.1 KB)
  • 2. Get weather data.mp4 (12.6 MB)
  • 2. Get weather data.vtt (1.8 KB)
  • 3. Inspect the data.mp4 (135.5 MB)
  • 3. Inspect the data.vtt (11.3 KB)
  • 4. Load the data.mp4 (11.3 MB)
  • 4. Load the data.vtt (2.4 KB)
  • 5. Convert the data to lists.mp4 (42.6 MB)
  • 5. Convert the data to lists.vtt (6.6 KB)
  • 6. Replace missing data with NaNs.mp4 (39.0 MB)
  • 6. Replace missing data with NaNs.vtt (5.1 KB)
  • 7. Replace NaNs with estimates.mp4 (46.8 MB)
  • 7. Replace NaNs with estimates.vtt (7.0 KB)
3. Find your features
  • 1. How autocorrelation works.mp4 (49.8 MB)
  • 1. How autocorrelation works.vtt (12.8 KB)
  • 2. Find the autocorrelation.mp4 (46.5 MB)
  • 2. Find the autocorrelation.vtt (6.0 KB)
  • 3. Inspect the autocorrelation.mp4 (22.4 MB)
  • 3. Inspect the autocorrelation.vtt (4.5 KB)
  • 4. Write a day-of-year calculator.mp4 (95.8 MB)
  • 4. Write a day-of-year calculator.vtt (11.1 KB)
  • 5. Debug glitch in annual trend.mp4 (66.8 MB)
  • 5. Debug glitch in annual trend.vtt (8.0 KB)
4. Build your model
  • 1. Create seasonal model.mp4 (66.1 MB)
  • 1. Create seasonal model.vtt (5.9 KB)
  • 2. Explore deseasonalized residuals.mp4 (32.2 MB)
  • 2. Explore deseasonalized residuals.vtt (3.4 KB)
  • 3. Make three-day-out predictions.mp4 (71.8 MB)
  • 3. Make three-day-out predictions.vtt (7.3 KB)
  • 4. Build the full model and refactor the code.mp4 (77.0 MB)
  • 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)
  • 1. Choose your decision criterion.vtt (7.5 KB)
  • 2. Create a Predictor class with tests.mp4 (63.3 MB)
  • 2. Create a Predictor class with tests.vtt (4.7 KB)
  • 3. Create a buy_tickets module to answer the question.mp4 (51.4 MB)
  • 3. Create a buy_tickets module to answer the question.vtt (5.1 KB)
6. Wrap up
  • 1. What_s next.mp4(1).mtd (114.0 MB)
  • 1. What_s next.mp4.mtd (114.0 MB)
  • Discuss.FreeTutorials.Us.html (165.7 KB)
  • FreeCoursesOnline.Me.html (108.3 KB)
  • FreeTutorials.Eu.html (102.2 KB)
  • Presented By SaM.txt (0.0 KB)
  • [TGx]Downloaded from torrentgalaxy.org.txt (0.5 KB)
  • Torrent Downloaded From GloDls.to.txt (0.1 KB)

Description



Build a weather predictor using python

Created by : Brandon Rohrer
Last updated : 4/2018
Language : English
Torrent Contains : 58 Files, 6 Folders
Course Source : https://www.udemy.com/build-your-own-weather-predictor-end-to-end-data-science/

What you'll learn

• Build a weather predictor using python.
• 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/





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[UDEMY] End-to-end Machine Learning Time-series analysis - [FTU]

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Download torrent
1.4 GB
seeders:19
leechers:7
[UDEMY] End-to-end Machine Learning Time-series analysis - [FTU]

Torrent hash: 0D1C4437F548A5D4CECF6B7DB8874895B95E4E69