Feature Engineering For Data Science & Machine Learning A-Z™

Posted in: Tutorials | By: AD-TEAMSSS | 13-01-2022, 04:34 | 0 Comments
13
January
2022

Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 8.62 GB | Duration: 15h 45m

</b>Master How To Deal With Messy Data(outliers, missing values, data imbalance, data leakage etc.


)
Know How To Deal With Complex Data Cleaning Issues In Python
Learn Automated Modern Tools And Libraries For Professional Data Cleaning And Analysis
Get The Skill Needed To Be Part Of The Top 10% Data Science
Learn How To Professionally Prepare Your Data For Machine Learning Algorithms
Master Different Techniques Of Dealing With Raw Data
Perform Industry Level Data Eeering
Learn Feature Eeering
Learn Feature Encoding
Learn Data Eeering

According to Forbes: "60% of the Data Scientist's or Data Analyst's is spent in cleaning and organising the data..."
In this course, you will not just get to know the industry level strats but also I will practically demonstrate them for better understanding.
This course has been practically and carefully designed by industry experts to reflect the real-world scenario of working with messy data.
This course will help you learn complex Data Analytic techniques and concepts for easier understanding and data manipulations.
We will walk you through step-by-step on each topic explaining each line of code for your understanding.
This course has been structured in the following form
How To Properly Deal With Data Types in Python
How To Properly Deal With Date and In Python
How To Properly Deal With Missing Values
How To Properly Deal With Outliers
How To Properly Deal With Data Imbalance
How To Properly Deal With Data Leakage
How To Properly Deal With Categorical Values
Machine Learning Hyper-parameter Tuning and Mode Performance
Different Feature Eeering Techniques including
Feature Encoding
Feature Scaling
Feature Transformation
Feature Normalisation
Automated Feature EDA Tools
pandas-profiling
Dora
Autoviz
Sweetviz
Automated Feature Eeering
RFECV
FeatureTools
FeatureSelector
Autofeat
This course aims to help bners, as well as an intermediate data analyst, students, business analyst, data science, and machine learning enthusiasts, master the foundations of confidently working with data in the real world.

Anyone who wants to learn professional data eeering
Any student interested in learning how to prepare data to build Machine Learning models
Interested in learning techniques to deal with messy data




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