Python for Data Science and Machine Learning Essential Training Part 1

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[ DevCourseWeb.com ] Python for Data Science and Machine Learning Essential Training Part 1
  • Get Bonus Downloads Here.url (0.2 KB)
  • ~Get Your Files Here ! 01 - Introduction
    • 01 - Data science life hacks.mp4 (2.4 MB)
    • 01 - Data science life hacks.srt (1.3 KB)
    • 02 - What you should know.mp4 (1.3 MB)
    • 02 - What you should know.srt (1.7 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)
    02 - 1. Introduction to the Data Professions
    • 01 - Introduction to the data professions.mp4 (21.8 MB)
    • 01 - Introduction to the data professions.srt (23.5 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)
    • 03 - Why to use Python for analytics.mp4 (10.2 MB)
    • 03 - Why to use Python for analytics.srt (9.7 KB)
    • 04 - High-level course road map.mp4 (2.6 MB)
    • 04 - High-level course road map.srt (2.7 KB)
    03 - 2. Data Preparation Basics
    • 01 - Intro to data preparation.mp4 (5.3 MB)
    • 01 - Intro to data preparation.srt (5.1 KB)
    • 02 - Numpy and pandas basics.mp4 (6.7 MB)
    • 02 - Numpy and pandas basics.srt (5.9 KB)
    • 03 - Filtering and selecting.mp4 (34.9 MB)
    • 03 - Filtering and selecting.srt (17.4 KB)
    • 04 - Treating missing values.mp4 (37.5 MB)
    • 04 - Treating missing values.srt (22.5 KB)
    • 05 - Removing duplicates.mp4 (17.7 MB)
    • 05 - Removing duplicates.srt (9.1 KB)
    • 06 - Concatenating and transforming.mp4 (27.9 MB)
    • 06 - Concatenating and transforming.srt (15.4 KB)
    • 07 - Grouping and aggregation.mp4 (13.1 MB)
    • 07 - Grouping and aggregation.srt (8.3 KB)
    04 - 3. Data Visualization 101
    • 01 - Importance of visualization in data science.mp4 (5.1 MB)
    • 01 - Importance of visualization in data science.srt (6.2 KB)
    • 02 - The three types of data visualization.mp4 (14.4 MB)
    • 02 - The three types of data visualization.srt (15.2 KB)
    • 03 - Selecting optimal data graphics.mp4 (16.4 MB)
    • 03 - Selecting optimal data graphics.srt (11.7 KB)
    • 04 - Communicating with color and context.mp4 (11.8 MB)
    • 04 - Communicating with color and context.srt (11.4 KB)
    05 - 4. Practical Data Visualization
    • 01 - Introduction to the matplotlib and Seaborn libraries.mp4 (38.7 MB)
    • 01 - Introduction to the matplotlib and Seaborn libraries.srt (21.3 KB)
    • 02 - Creating standard data graphics.mp4 (21.2 MB)
    • 02 - Creating standard data graphics.srt (13.5 KB)
    • 03 - Defining elements of a plot.mp4 (25.0 MB)
    • 03 - Defining elements of a plot.srt (15.8 KB)
    • 04 - Plot formatting.mp4 (33.2 MB)
    • 04 - Plot formatting.srt (17.5 KB)
    • 05 - Creating labels and annotations.mp4 (41.7 MB)
    • 05 - Creating labels and annotations.srt (23.8 KB)
    • 06 - Visualizing time series.mp4 (18.1 MB)
    • 06 - Visualizing time series.srt (11.1 KB)
    • 07 - Creating statistical data graphics in Seaborn.mp4 (31.8 MB)
    • 07 - Creating statistical data graphics in Seaborn.srt (17.3 KB)
    06 - 5. Exploratory Data Analysis
    • 01 - Simple arithmetic.mp4 (16.7 MB)
    • 01 - Simple arithmetic.srt (11.5 KB)
    • 02 - Generating summary statistics.mp4 (20.1 MB)
    • 02 - Generating summary statistics.srt (14.0 KB)
    • 03 - Summarizing categorical data.mp4 (21.4 MB)
    • 03 - Summarizing categorical data.srt (14.5 KB)
    • 04 - Pearson correlation analysis.mp4 (35.5 MB)
    • 04 - Pearson correlation analysis.srt (23.0 KB)
    • 05 - Spearman rank correlation and Chi-square.mp4 (31.7 MB)
    • 05 - Spearman rank correlation and Chi-square.srt (18.7 KB)
    • 06 - Extreme value analysis for outliers.mp4 (28.8 MB)
    • 06 - Extreme value analysis for outliers.srt (19.1 KB)
    • 07 - Multivariate analysis for outliers.mp4 (17.0 MB)
    • 07 - Multivariate analysis for outliers.srt (10.4 KB)
    07 - 6. Getting Started with Machine Learning
    • 01 - Cleaning and treating categorical variables.mp4 (25.2 MB)
    • 01 - Cleaning and treating categorical variables.srt (14.3 KB)
    • 02 - Transforming data set distributions.mp4 (14.4 MB)
    • 02 - Transforming data set distributions.srt (9.7 KB)
    • 03 - Applied machine learning Starter problem.mp4 (18.0 MB)
    • 03 - Applied machine learning Starter problem.srt (11.8 KB)
    08 - 7. Data Sourcing via Web Scraping
    • 01 - Introduction of web scraping.mp4 (3.7 MB)
    • 01 - Introduction of web scraping.srt (4.0 KB)
    • 02 - Python requests for automating data collection.mp4 (18.9 MB)
    • 02 - Python requests for automating data collection.srt (12.7 KB)
    • 03 - BeautifulSoup object.mp4 (46.6 MB)
    • 03 - BeautifulSoup object.srt (25.3 KB)
    • 04 - NavigableString objects.mp4 (29.1 MB)
    • 04 - NavigableString objects.srt (14.0 KB)
    • 05 - Data parsing.mp4 (37.9 MB)
    • 05 - Data parsing.srt (17.5 KB)
    • 06 - Web scraping in practice.mp4 (36.2 MB)
    • 06 - Web scraping in practice.srt (18.0 KB)
    • 07 - Asynchronous scraping.mp4 (46.7 MB)
    • 07 - Asynchronous scraping.srt (25.2 KB)
    09 - 8. Collaborative Analytics with Streamlit
    • 01 - Introduction to Streamlit.mp4 (7.1 MB)
    • 01 - Introduction to Streamlit.srt (8.4 KB)
    • 02 - Environment setup.mp4 (6.0 MB)
    • 02 - Environment setup.srt (4.1 KB)
    • 03 - Create basic charts.mp4 (21.0 MB)
    • 03 - Create basic charts.srt (10.4 KB)
    • 04 - Line charts in Streamlit.mp4 (18.3 MB)
    • 04 - Line charts in Streamlit.srt (9.8 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)
    • 06 - Create statistical charts.mp4 (20.2 MB)
    • 06 - Create statistical charts.srt (8.8 KB)
    10 - Conclusion

Description

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.



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1007 MB
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Python for Data Science and Machine Learning Essential Training Part 1


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