| 01 - Cleaning and treating categorical variables.mp4 | 25.2 MB | ||
| 01 - Cleaning and treating categorical variables.srt | 14.3 KB | ||
| 01 - Data science life hacks.mp4 | 2.4 MB | ||
| 01 - Data science life hacks.srt | 1.3 KB | ||
| 01 - Importance of visualization in data science.mp4 | 5.1 MB | ||
| 01 - Importance of visualization in data science.srt | 6.2 KB | ||
| 01 - Intro to data preparation.mp4 | 5.3 MB | ||
| 01 - Intro to data preparation.srt | 5.1 KB | ||
| 01 - Introduction of web scraping.mp4 | 3.7 MB | ||
| 01 - Introduction of web scraping.srt | 4 KB | ||
| 01 - Introduction to Streamlit.mp4 | 7.1 MB | ||
| 01 - Introduction to Streamlit.srt | 8.4 KB | ||
| 01 - Introduction to the data professions.mp4 | 21.8 MB | ||
| 01 - Introduction to the data professions.srt | 23.5 KB | ||
| 01 - Introduction to the matplotlib and Seaborn libraries.mp4 | 38.7 MB | ||
| 01 - Introduction to the matplotlib and Seaborn libraries.srt | 21.3 KB | ||
| 01 - Next steps.mp4 | 1.7 MB | ||
| 01 - Next steps.srt | 2 KB | ||
| 01 - Simple arithmetic.mp4 | 16.7 MB | ||
| 01 - Simple arithmetic.srt | 11.5 KB | ||
| 02 - Creating standard data graphics.mp4 | 21.2 MB | ||
| 02 - Creating standard data graphics.srt | 13.5 KB | ||
| 02 - Data science careers Identifying where and how you'll thrive.mp4 | 14.7 MB | ||
| 02 - Data science careers Identifying where and how you'll thrive.srt | 7.3 KB | ||
| 02 - Environment setup.mp4 | 6 MB | ||
| 02 - Environment setup.srt | 4.1 KB | ||
| 02 - Generating summary statistics.mp4 | 20.1 MB | ||
| 02 - Generating summary statistics.srt | 14 KB | ||
| 02 - Numpy and pandas basics.mp4 | 6.7 MB | ||
| 02 - Numpy and pandas basics.srt | 5.9 KB | ||
| 02 - Python requests for automating data collection.mp4 | 18.9 MB | ||
| 02 - Python requests for automating data collection.srt | 12.7 KB | ||
| 02 - The three types of data visualization.mp4 | 14.4 MB | ||
| 02 - The three types of data visualization.srt | 15.2 KB | ||
| 02 - Transforming data set distributions.mp4 | 14.4 MB | ||
| 02 - Transforming data set distributions.srt | 9.7 KB | ||
| 02 - What you should know.mp4 | 1.3 MB | ||
| 02 - What you should know.srt | 1.7 KB | ||
| 03 - Applied machine learning Starter problem.mp4 | 18 MB | ||
| 03 - Applied machine learning Starter problem.srt | 11.8 KB | ||
| 03 - BeautifulSoup object.mp4 | 46.6 MB | ||
| 03 - BeautifulSoup object.srt | 25.3 KB | ||
| 03 - Create basic charts.mp4 | 21 MB | ||
| 03 - Create basic charts.srt | 10.4 KB | ||
| 03 - Defining elements of a plot.mp4 | 25 MB | ||
| 03 - Defining elements of a plot.srt | 15.8 KB | ||
| 03 - Filtering and selecting.mp4 | 34.9 MB | ||
| 03 - Filtering and selecting.srt | 17.4 KB | ||
| 03 - How to use Codespaces with this course.mp4 | 7.3 MB | ||
| 03 - How to use Codespaces with this course.srt | 4.1 KB | ||
| 03 - Selecting optimal data graphics.mp4 | 16.4 MB | ||
| 03 - Selecting optimal data graphics.srt | 11.7 KB | ||
| 03 - Summarizing categorical data.mp4 | 21.4 MB | ||
| 03 - Summarizing categorical data.srt | 14.5 KB | ||
| 03 - Why to use Python for analytics.mp4 | 10.2 MB | ||
| 03 - Why to use Python for analytics.srt | 9.7 KB | ||
| 04 - Communicating with color and context.mp4 | 11.8 MB | ||
| 04 - Communicating with color and context.srt | 11.4 KB | ||
| 04 - High-level course road map.mp4 | 2.6 MB | ||
| 04 - High-level course road map.srt | 2.7 KB | ||
| 04 - Line charts in Streamlit.mp4 | 18.3 MB | ||
| 04 - Line charts in Streamlit.srt | 9.8 KB | ||
| 04 - NavigableString objects.mp4 | 29.1 MB | ||
| 04 - NavigableString objects.srt | 14 KB | ||
| 04 - Pearson correlation analysis.mp4 | 35.5 MB | ||
| 04 - Pearson correlation analysis.srt | 23 KB | ||
| 04 - Plot formatting.mp4 | 33.2 MB | ||
| 04 - Plot formatting.srt | 17.5 KB | ||
| 04 - Treating missing values.mp4 | 37.5 MB | ||
| 04 - Treating missing values.srt | 22.5 KB | ||
| 05 - Bar charts and pie charts in Streamlit.mp4 | 23.3 MB | ||
| 05 - Bar charts and pie charts in Streamlit.srt | 12.6 KB | ||
| 05 - Creating labels and annotations.mp4 | 41.7 MB | ||
| 05 - Creating labels and annotations.srt | 23.8 KB | ||
| 05 - Data parsing.mp4 | 37.9 MB | ||
| 05 - Data parsing.srt | 17.5 KB | ||
| 05 - Removing duplicates.mp4 | 17.7 MB | ||
| 05 - Removing duplicates.srt | 9.1 KB | ||
| 05 - Spearman rank correlation and Chi-square.mp4 | 31.7 MB | ||
| 05 - Spearman rank correlation and Chi-square.srt | 18.7 KB | ||
| 06 - Concatenating and transforming.mp4 | 27.9 MB | ||
| 06 - Concatenating and transforming.srt | 15.4 KB | ||
| 06 - Create statistical charts.mp4 | 20.2 MB | ||
| 06 - Create statistical charts.srt | 8.8 KB | ||
| 06 - Extreme value analysis for outliers.mp4 | 28.8 MB | ||
| 06 - Extreme value analysis for outliers.srt | 19.1 KB | ||
| 06 - Visualizing time series.mp4 | 18.1 MB | ||
| 06 - Visualizing time series.srt | 11.1 KB | ||
| 06 - Web scraping in practice.mp4 | 36.2 MB | ||
| 06 - Web scraping in practice.srt | 18 KB | ||
| 07 - Asynchronous scraping.mp4 | 46.7 MB | ||
| 07 - Asynchronous scraping.srt | 25.2 KB | ||
| 07 - Creating statistical data graphics in Seaborn.mp4 | 31.8 MB | ||
| 07 - Creating statistical data graphics in Seaborn.srt | 17.3 KB | ||
| 07 - Grouping and aggregation.mp4 | 13.1 MB | ||
| 07 - Grouping and aggregation.srt | 8.3 KB | ||
| 07 - Multivariate analysis for outliers.mp4 | 17 MB | ||
| 07 - Multivariate analysis for outliers.srt | 10.4 KB | ||
| Bonus Resources.txt | 409.6 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 100 total files | |||
Python for Data Science and Machine Learning Essential Training Part 1
https://DevCourseWeb.com
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 7h 44m | 1006 MB
Instructor: Lillian Pierson, P.E.
Python for Data Science and Machine Learning Essential Training is one of the most popular data science courses at LinkedIn Learning. It has now been updated and expanded to two parts-giving you even more hands-on, real-world Python experience.
In part one, instructor Lillian Pierson takes you step by step through a data science and machine learning project: a web scraper that downloads and analyzes data from the web. Along the way, she introduces techniques to clean, reformat, transform, and describe raw data; generate visualizations; remove outliers; perform simple data analysis; and generate web-based graphs using Streamlit. By the end of this course, you'll have acquired basic coding experience that you can take to your organization and quickly apply to your own custom data science and machine learning projects.
This course is integrated with GitHub Codespaces, an instant cloud developer environment that offers all the functionality of your favorite IDE without the need for any local machine setup. With GitHub Codespaces, you can get hands-on practice from any machine, at any time-all while using a tool that you'll likely encounter in the workplace. Check out the "Using GitHub Codespaces with this course" video to learn how to get started.
| torrent name | size | uploader | age | seed | leech |
|---|---|---|---|---|---|
| 2.9 GB | freecoursewb | 2 weeks | 15 | 5 | |
| 3.1 GB | freecoursewb | 2 weeks | 18 | 4 | |
| 1 GB | freecoursewb | 2 weeks | 0 | 0 | |
| 234.3 MB | SeedHash | 1 month | 23 | 7 | |
| 1.8 GB | freecoursewb | 2 months | 49 | 5 |
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