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

    Posted By: BlackDove
    Feature Engineering For Data Science & Machine Learning A-Z™

    Feature Engineering For Data Science & Machine Learning A-Z™
    Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
    Language: English | Size: 8.62 GB | Duration: 15h 45m


    Data Engineering |Data Imbalance |Messy Data |Feature Encoding|EDA |Scaling |Normalisation &Transformation| 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

    Machine Learning Hyper-parameter Tuning and Mode Performance

    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