17.01.2022, 22:45
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Feature Engineering A-Z™ | Beginner To Advanced
Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 8.62 GB | Duration: 15h 45m
Data Engineering | Data Imbalance |Transformation | Feature Encoding | EDA | Scaling | Normalisation | Data Leakage
What you'll learn
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 Engineering
Learn Feature Engineering
Learn Feature Encoding
Learn Data Engineering
Description
Building Machine Learning models is important but what is more important is how well you prepare your data to build these models
According to Forbes: "60% of the Data Scientist's or Data Analyst's time is spent in cleaning and organising the data..."
In this course, you will not just get to know the industry level strategies 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 Time 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
Beginner To Advanced Data Visualisation
Different Feature Engineering Techniques including
Feature Encoding
Feature Scaling
Feature Transformation
Feature Normalisation
Automated Feature EDA Tools
pandas-profiling
Dora
Autoviz
Sweetviz
Automated Feature Engineering
RFECV
FeatureTools
FeatureSelector
Autofeat
This course aims to help beginners, 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.
Who this course is for
Anyone ready to learn how to deal with complex machine learning problems such as imbalance data, data leakage, basic to advanced Feature Engineering etc. is str
Anyone who wants to learn professional data engineering
Any student interested in learning how to prepare data to build Machine Learning models
Interested in learning techniques to deal with messy data
Homepage
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