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Master Classification with Pandas and Python [2024]
Published 7/2024
Created by Henrik Johansson
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 68 Lectures ( 28h 30m ) | 11 GB[/center]
Learn to Master Classification with Pandas and Python for Data Science and Machine Learning[2024]

What you'll learn:
Master Classification both in theory and practice
Master Classification models from Logistic Regression to the Gaussian Naïve Bayes Classifier model
Use practical classification hands-on theory and learn to execute advanced Classification tasks with ease
Use advanced Decision Tree, Random Forest, and Voting Classifier models
Use Feedforward Multilayer Networks and Advanced Classifier model Structures
Use effective decision surfaces graphs and other tools to judge Classifier performance
Use the Scikit-learn library for Classification supported by Matplotlib, Seaborn, Pandas, and Python
Master Python 3 programming with Python's native data structures, data transformers, functions, object orientation, and logic
Use and design advanced Python constructions and execute detailed Data Handling tasks with Python incl. File Handling
Use Python's advanced object-oriented programming and make your own custom objects, functions and how to generalize functions
Manipulate data and use advanced multi-dimensional uneven data structures
Master the Pandas 2 and 3 library for Advanced Data Handling
Use the language and fundamental concepts of the Pandas library and to handle all aspects of creating, changing, and selecting Data from a Pandas DataFrame
Use file handling with Pandas and how to combine Pandas DataFrames with Pandas concat, join, and merge functions/methods
Perform advanced data preparation including advanced model-based imputation of missing data and the scaling and standardizing of data
Make advanced data descriptions and statistics with Pandas. Rank, sort, cross-tabulate, pivot, melt, transpose, and group data
Make advanced Data Visualizations with Pandas, Matplotlib, and Seaborn
Cloud computing: Use the Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). Learn to use Cloud computing resources
Option: To use the Anaconda Distribution (for Windows, Mac, Linux)

Requirements:
Everyday experience using a computer with either Windows, MacOS, iOS, Android, ChromeOS, or Linux is recommended
Access to a computer with an internet connection
Programming experience is not needed and you will be taught everything you need
The course only uses costless software
Walk-you-through installation and setup videos for Cloud computing and Windows 10/11 is included





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