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

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

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1.4 GB
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57
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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/








File list
  • [FreeTutorials.Eu] [UDEMY] End-to-end Machine Learning Time-series analysis - [FTU]
  • 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 5.1 KB
  • 6. Wrap up/1. What_s next.mp4(1).mtd 114 MB
  • 6. Wrap up/1. What_s next.mp4.mtd 114 MB
  • Discuss.FreeTutorials.Us.html 165.7 KB
  • FreeCoursesOnline.Me.html 108.3 KB
  • FreeTutorials.Eu.html 102.2 KB
  • Presented By SaM.txt 33 B

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