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Machine Learning Fundamentals

Unit 1

Unit 1: Data Preprocessing

Introduction to Data Preprocessing Data Cleaning Feature Engineering

Unit 2

Unit 2: Image Classification

Introduction to Image Classification Convolutional Neural Networks for Image Classification Transfer Learning for Image Classification

Unit 3

Introduction to SVM Kernel Methods for SVM Hyperparameter Tuning for SVM

Unit 1 • Chapter 2

Data Cleaning

Video Summary

Data cleaning is the process of removing errors and inconsistencies from data. It is an important step in data preparation, as it can help to improve the accuracy and reliability of data analysis. Data cleaning can be performed manually or with the help of data cleaning tools. The specific steps involved in data cleaning will vary depending on the type of data and the sources from which it was collected. However, some common data cleaning tasks include: identifying and removing duplicate records, correcting errors in data values, and filling in missing values. Data cleaning is an essential part of the data science process, as it can help to ensure that data is fit for use in analysis and modeling.

Knowledge Check

Data cleaning is the process of removing what from data?

errors, bias, noise

bias, noise, garbage

errors, garbage, bias

errors, noise, garbage

Which of the following is NOT a type of data cleaning task: filling in missing values, removing outliers, removing duplicates, or removing noise?

filling in missing values

removing outliers

removing duplicates

removing noise

Which of the following is a common data cleaning method: data scrubbing, data scrubbing, data cleansing, or data cleaning?

data scrubbing

data scrubbing

data cleansing

data cleaning

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Introduction to Data Preprocessing

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Feature Engineering