Feature Engineering For Data Science
Published 07/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 208 lectures (13h 13m) | Size: 5.75 GB

Learn Data Engineering | Feature Encoding | Feature Normalization | Top 10% Skills of Data Scientist Skills

What you'll learn
Master Exploratory Data Analysis (EDA) With Python
Master How To Deal With Messy Data
Master How To Deal With Outliers
Master How To Deal With Missing Data
Master How To Deal With Data Leakage
Learn How Deal With Poor Machine Learning Algorithms & Models
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 Analytics and Data Science
Learn The Best Ways To Prepare Your Data To Build Machine Learning Models
Master Different Techniques Of Dealing With Raw Data
Perform Industry Level Data Engineering
Learn Feature Encoding
Learn Feature Normalization
Any student ready to learn how to deal with complex machine learning problems such as imbalance data, data leakage, basic to advanced Feature Engineering etc.

Requirements
This is a beginner friendly course and does not require any pre-requisite

Description
Interested in the field of Data Analytics, Business Analytics, Data Science or Machine Learning?

Do you want to learn Beginner to Advanced level Feature Engineering?

Do you want to know the best ways to clean data and derive useful insights from it?

Do you want to save time and easily perform Exploratory Data Analysis(EDA)?

Then this course is for you!!

According to Forbes: "60% of the Data Scientist's or Data Analyst's time is spent in cleaning and organizing 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

Introduction To Basic Concepts

How To Properly Deal With Python Data Types

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 Visualization

Different Feature Engineering Techniques including

Feature Encoding

Feature Scaling

Feature Transformation

Feature Normalization

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 interested in becoming a Data Scientist
Anyone interested in becoming a Machine Learning Engineer
Any student interested in learning the best ways to prepare your data for building Machine Learning algorithm & models
Anyone interested in knowing how Data Engineering is done in the industry

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https://rapidgator.net/file/93ecbaa7b59ceabb7fa8a4b18b5720b5/Feature_Engineering_For_Data_Science_A-Z.part1.rar
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