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Data Cleaning Techniques In Data Science & Machine Learning

23-11-2022, 18:07, posted by Baturi
Data Cleaning Techniques In Data Science & Machine Learning
Last updated 1/2020
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.96 GB | Duration: 4h 55m
Explore all the concepts of Data Cleaning for AI & Data Science to become an expert with this complete online tutorial.


What you'll learn
Professional ways for handling the data
Learn Standard visualization techniques like Histograms, Scatterplots etc
How to locate discrepancies, and deal with issues
Requirements
Basic Knowledge of Python
Description
One of the most essential aspects of Data Science or Machine Learning is Data Cleaning. In order to get the most out of the data, your data must be clean as uncleaned data can make it harder for you to train ML models. In regard to ML & Data Science, data cleaning generally filters & modifies your data making it easier for you to explore, understand and model.A good statistician or a researcher must spend at least 90% of his/her time on collecting or cleaning data for developing a hypothesis and remaining 10% on the actual manipulation of the data for analyzing or deriving the results. Despite these facts, data cleaning is not commonly discussed or taught in detail in most of the data science or ML courses. With the rise of big data & ML, now data cleaning has also become equally important.Why should you learn Data Cleaning?Improve decision makingImprove the efficiencyIncrease productivityRemove the errors and inconsistencies from the datasetIdentifying missing valuesRemove duplicationWhy should you take this course?Data Cleaning is an essential part of Data Science & AI, and it has become an equally important skill for a programmer. It's true that you will find hundreds of online tutorials on Data Science and Artificial Intelligence but only a few of them cover data cleaning or just give the basic overview. This online guide for data cleaning includes numerous sections having over 5 hours of video which are enough to teach anyone about all its concepts from the very beginning. Enroll in this course now to learn all the concepts of Data Cleaning. This course teaches you everything including the basics of Data Cleaning, Data Reading, merging or splitting datasets, different visualization tools, locate or handling missing/absurd values and hands-on sessions where you'll be introduced to the dataset for ensuring complete learning of Data Cleaning.Enroll in this course now to learn about data cleaning concepts and techniques in detail!
Overview
Section 1: Introduction
Lecture 1 Identifying the task
Lecture 2 Model building
Lecture 3 Some common solutions
Lecture 4 Training and test data
Lecture 5 Cross validation
Lecture 6 Feature selection
Lecture 7 Accuracy measures
Lecture 8 Overfitting
Section 2: Playing with the Data
Lecture 9 Reading the data
Lecture 10 Structure of the data
Lecture 11 Merging/Splitting
Lecture 12 Integrity check
Lecture 13 Knowing the domain
Lecture 14 Range of variables
Lecture 15 Inquiring dependencies
Section 3: Variables and Correlations
Lecture 16 Type of variables
Lecture 17 More variable types
Lecture 18 Single variable plots
Lecture 19 Plotting interrelations
Lecture 20 Measuring correlations
Lecture 21 Need for transformation
Lecture 22 Discretizing features
Section 4: Missing Values and Outliers
Lecture 23 Absurd or Missing values
Lecture 24 Finding their distribution in the dataset
Lecture 25 Deciding what to do with them
Lecture 26 Looking for outliers
Section 5: Exercises
Lecture 27 Exercise-1
Lecture 28 Exercise-2
Lecture 29 Exercise-3
Lecture 30 Exercise-4
Students who want to learn the basics of Data Cleaning


Homepage
https://www.udemy.com/course/data-science-and-ml-data-cleaning-techniques/




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