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[FreeCoursesOnline.Me] [Packt] Python and Data Science A Practical Guide [FCO]

[FreeCoursesOnline.Me] [Packt] Python and Data Science A Practical Guide [FCO]

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Description




By: Eduonix, Michael Mustaine

Released: Friday, March 29, 2019 [New Release!]

Torrent Contains: 80 Files, 15 Folders

Course Source: https://www.packtpub.com/application-development/python-and-data-science-practical-guide-video



Learn Data Science and Python to do Web Scraping, Data Analysis, Data Visualization, Machine Learning, Deep Learning...



Video Details



ISBN 9781838553012

Course Length 13 hours 9 minutes



Table of Contents



• INTRODUCTION

• ENVIRONMENT

• INTEGERS AND STRINGS

• IF STATEMENTS AND BASIC PROGRAMMING LOGIC

• LISTS, TUPLES, DICTIONARIES AND FOR/WHILE LOOPS

• FUNCTIONS AND PACKAGES

• PANDAS AND DATA FRAMES

• VISUALIZATION - SCATTER PLOTS, BAR PLOTS

• SCRAPING THE WEB WITH PYTHON

• BASICS OF NATURAL LANGUAGE PROCESSING (NLP)

• INTRODUCTION TO MACHINE LEARNING

• OPTIONAL CLASSES

• PROJECT #1 - ANALYZE AND VISUALIZE DATA ON KAGGLE

• PROJECT #2 - NATURAL LANGUAGE PROCESSING

• PROJECT #3 - CREATE A SIMPLE SUPPORT VECTOR MACHINE



Video Description



This course is designed to teach you the basics of Python and Data Science in a practical way, so that you can acquire, test, and master your Python skills gradually.

You’ll see that you'll learn all these things with Python:



• Using Variables & Strings

• Using Booleans & Logical Operators

• Using Functions & Packages

• Using Lists, Tuples and Dictionaries

• Using For & While Loops

• Using Panda & Data Frames

• Doing Data Visualization

• Scraping Web Data

• Doing some basic Natural Language Processing (NLP)

• Basics of Machine Learning & Deep Learning



And much more to come.



Style and Approach



This course is designed in a practical way to teach you the basics of Python and Data Science. A complete course packed with step-by-step instructions, working examples, and helpful advice. This course is clearly divided into small parts that will help you understand each part individually and help you learn at your own pace.



What You Will Learn



• How to set up your Python environment

• How to manipulate String & Variables with Python

• How to use Booleans & Logical Operators with Python

• How to use Functions & Packages with Python

• How to use Pandas & Data Frames with Python

• How to perform Data Visualization with Python

• How to do Web Scraping with Python

• The Basics of Natural Language Processing (NLP)

• The Basics of Deep Learning & Reinforcement Learning



Authors



Eduonix



Eduonix Learning Solutions creates and distributes high-quality technology training content. Their team of industry professionals have been training manpower for more than a decade. They aim to teach technology the way it is used in industry and the professional world. They have a professional team of trainers for technologies ranging from mobility, web to enterprise and database and server administration



Michael Mustaine



Michael Mustaine is a polymath with academic and professional experience in psychology and data science. He has created innovative human-centric analysis for user segmentation, feature selection, machine learning, and key performance metric generation specialized to individual client needs. Recent clients include: cryptocurrency hedgefunds, not-for-profit data journalists, and universities. He provide interpretable evaluations of complex behaviour with a degree of innovation that only comes from being a trailblazer in the field.



For More Udemy Free Courses >>> https://ftuforum.com/

For more Lynda and other Courses >>> https://www.freecoursesonline.me/

Our Forum for discussion >>> https://discuss.ftuforum.com/








File list
  • [FreeCoursesOnline.Me] [Packt] Python and Data Science A Practical Guide [FCO]
  • 01.Introduction/0101.Introduction.mp4 16.8 MB
  • 02.Environment/0201.Introduction to data science.mp4 235 MB
  • 02.Environment/0202.Installing dependencies.mp4 39.3 MB
  • 02.Environment/0203.Real world examples.mp4 30.1 MB
  • 02.Environment/0204.Using Anaconda and Jupyter notebooks.mp4 55.2 MB
  • 03.Integers and Strings/0301.Introduction to variables.mp4 58.6 MB
  • 03.Integers and Strings/0302.Integers, floats, and math operators.mp4 53.8 MB
  • 03.Integers and Strings/0303.Strings and indexing part 1.mp4 38 MB
  • 03.Integers and Strings/0304.Strings and indexing part 2.mp4 32.9 MB
  • 03.Integers and Strings/0305.Using Modulo with Strings.mp4 32 MB
  • 03.Integers and Strings/0306.Problem.mp4 10.8 MB
  • 03.Integers and Strings/0307.Answer.mp4 26 MB
  • 04.If Statements and Basic Programming Logic/0401.Booleans and Comparison Operators.mp4 48.1 MB
  • 04.If Statements and Basic Programming Logic/0402.If Else Statements.mp4 38.7 MB
  • 04.If Statements and Basic Programming Logic/0403.Elif and Logic Operators.mp4 41.7 MB
  • 04.If Statements and Basic Programming Logic/0404.Try Except Statements.mp4 33.1 MB
  • 04.If Statements and Basic Programming Logic/0405.Finally Statements and Advance TryExcept Logic.mp4 32.4 MB
  • 04.If Statements and Basic Programming Logic/0406.Exception Types.mp4 51.2 MB
  • 04.If Statements and Basic Programming Logic/0407.Problem.mp4 7.7 MB
  • 04.If Statements and Basic Programming Logic/0408.Answer.mp4 25.1 MB
  • 05.Lists, Tuples, Dictionaries and ForWhile Loops/0501.Lists and tuples.mp4 71 MB
  • 05.Lists, Tuples, Dictionaries and ForWhile Loops/0502.Dictionaries.mp4 64 MB
  • 05.Lists, Tuples, Dictionaries and ForWhile Loops/0503.For loops.mp4 61.9 MB
  • 05.Lists, Tuples, Dictionaries and ForWhile Loops/0504.While loops.mp4 34.7 MB
  • 05.Lists, Tuples, Dictionaries and ForWhile Loops/0505.Loop logic.mp4 50.5 MB
  • 05.Lists, Tuples, Dictionaries and ForWhile Loops/0506.ListDictionary comprehensions.mp4 47.3 MB
  • 05.Lists, Tuples, Dictionaries and ForWhile Loops/0507.Problem.mp4 7.1 MB
  • 05.Lists, Tuples, Dictionaries and ForWhile Loops/0508.Answer.mp4 42.3 MB
  • 06.Functions and Packages/0601.Scripting in Python.mp4 50.3 MB
  • 06.Functions and Packages/0602.Functions.mp4 49.5 MB
  • 06.Functions and Packages/0603.Function parameters and scope.mp4 39.9 MB
  • 06.Functions and Packages/0604.Packages and pip.mp4 49 MB
  • 06.Functions and Packages/0605.Reading Files and the with statement.mp4 67 MB
  • 06.Functions and Packages/0606.Writing Out to Files.mp4 81 MB
  • 06.Functions and Packages/0607.Problem.mp4 8.8 MB
  • 06.Functions and Packages/0608.Answer.mp4 31.5 MB
  • 07.Pandas and Data Frames/0701.Pandas and data Frames.mp4 51.1 MB
  • 07.Pandas and Data Frames/0702.Pandas part 2.mp4 67 MB
  • 07.Pandas and Data Frames/0703.Introduction to statistics.mp4 52.1 MB
  • 07.Pandas and Data Frames/0704.Statistics part 2.mp4 40.7 MB
  • 07.Pandas and Data Frames/0705.Practical analysis.mp4 59.5 MB
  • 07.Pandas and Data Frames/0706.Problem.mp4 17.3 MB
  • 07.Pandas and Data Frames/0707.Answer.mp4 23.4 MB
  • 08.Visualization - Scatter Plots, Bar Plots/0801.Introduction to visualization.mp4 52.7 MB
  • 08.Visualization - Scatter Plots, Bar Plots/0802.Plotting and styling.mp4 55.9 MB
  • 08.Visualization - Scatter Plots, Bar Plots/0803.Scatter plots.mp4 50.5 MB
  • 08.Visualization - Scatter Plots, Bar Plots/0804.Bar Plots and standard deviation.mp4 53.3 MB
  • 08.Visualization - Scatter Plots, Bar Plots/0805.Problem.mp4 12.6 MB
  • 08.Visualization - Scatter Plots, Bar Plots/0806.Answer.mp4 50.2 MB
  • 09.Scraping the Web with Python/0901.Scraping and HTML.mp4 47.6 MB
  • 09.Scraping the Web with Python/0902.Building a simple crawler.mp4 47.3 MB
  • 09.Scraping the Web with Python/0903.Scraping data from HTML.mp4 57 MB
  • 09.Scraping the Web with Python/0904.Problem.mp4 11.6 MB
  • 09.Scraping the Web with Python/0905.Answer.mp4 36.3 MB
  • 10.Basics of Natural Language Processing (NLP)/1001.Introduction to NLP.mp4 51.8 MB
  • 10.Basics of Natural Language Processing (NLP)/1002.Basic NLP.mp4 64 MB
  • 10.Basics of Natural Language Processing (NLP)/1003.Introduction to sentiment analysis.mp4 74.3 MB
  • 10.Basics of Natural Language Processing (NLP)/1004.Problem.mp4 8.3 MB
  • 10.Basics of Natural Language Processing (NLP)/1005.Answer.mp4 33.2 MB
  • 11.Introduction to Machine Learning/1101.Introduction to machine learning.mp4 60.4 MB
  • 11.Introduction to Machine Learning/1102.Introduction to reinforcement learning.mp4 19.6 MB
  • 11.Introduction to Machine Learning/1103.Introduction to random forest modeling.mp4 64.4 MB
  • 11.Introduction to Machine Learning/1104.Introduction to deep learning.mp4 61.4 MB
  • 11.Introduction to Machine Learning/1105.Problem.mp4 24.1 MB
  • 11.Introduction to Machine Learning/1106.Answer.mp4 29.7 MB
  • 12.Optional Classes/1201.Python Style Guidelines.mp4 44.3 MB
  • 12.Optional Classes/1202.Navigating Directories and os Package.mp4 48.4 MB
  • 12.Optional Classes/1203.Creating a Class Type.mp4 45.7 MB
  • 13.Project #1 - Analyze and visualize data on Kaggle/1301.Project 1 - Analyze and visualize data on Kaggle.mp4 17.8 MB
  • 13.Project #1 - Analyze and visualize data on Kaggle/1302.Project 1 Answer.mp4 11.9 MB
  • 14.Project #2 - Natural language processing/1401.Project 2 - Natural language processing.mp4 7 MB
  • 14.Project #2 - Natural language processing/1402.Project 2 Answer.mp4 20.1 MB
  • 15.Project #3 - Create a simple support vector machine/1501.Project 3 - Create a simple support vector machine.mp4 19.6 MB
  • 15.Project #3 - Create a simple support vector machine/1502.Project 3 Answer.mp4 43.1 MB
  • Discuss.FTUForum.com.html 31.9 KB
  • FreeCoursesOnline.Me.html 108.3 KB
  • FTUForum.com.html 100.4 KB
  • How you can help Team-FTU.txt 235 B

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