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

Engineering For Data Science & Machine Learning – 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
  • This course is a beginner to advance level course with a step-by-step walk through.
  • No special prerequisites is needed


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.

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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.

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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

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