Machine Learning Tutorial Chap 6| Part-2 Dealing with Outlier | Rohit Ghosh | GreyAtom
Channel: GreyAtom EduTech
Date: 21/06/2019

Welcome to the #DataScienceFridays Rohit Ghosh, a deep learning scientist, and an Instructor at GreyAtom will take us through Feature Engineering in machine learning through a simple introduction series. 

Feature Engineering is the process of using domain knowledge of the data to create features that make machine learning algorithms work. If feature engineering is done correctly, it increases the predictive power of machine learning algorithms by creating features from raw data that help facilitate the machine learning process.


Study Feature Engineering in Machine Learning through this introductory session for beginners. 

This is the 2nd in 5 videos about Feature Engineering in Machine Learning. 

In this video, we will explore Dealing with Outlier.

Dealing With Outliers 
Many machine learning algorithms are sensitive to the range and distribution of attribute values in the input data. Outliers in input data can skew and mislead the training process of machine learning algorithms resulting in longer training times, less accurate models and ultimately poorer results.

Complete Playlist for the Course: https://bit.ly/2Q1zvK6 

After completing our 5-part Series on Feature Engineering in Machine Learning, you will be able to do the following:

Learn to handle outliers
Learn to handle missing values
Learn to deal with skewness
Learn centering and scaling
Learn to encode categorical variables 


Here’s the full syllabus of our 5-part video on Feature Engineering in Machine Learning:


1. Handling Outliers
2. Handling Missing Values 
3. Handling Skewness 
4. Handling Skewness 
5. Feature Extraction - Standardization - Scaling 
6. Algebraic Transformations 
7. Feature Engineering - Applying Domain Expertise - Combining Feature - Encoding of categoricalvariables

#machinelearningtutorial #featureengineering #DataScience101 #Greyatom

Please feel free to post your doubts, questions, feedback in the Comments section and we will sure to get back to you. 

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