Data Science with Python: Munging, Outliers, and Feature Engineering
Data Science with Python: Munging, Outliers, and Feature Engineering
.MP4, AVC, 1920x1080, 30 fps | English, AAC, 2 Ch | 47m | 125 MB
Instructor: Dr. Ali Feizollah
Learn to preprocess and engineer features from raw data using Python. This course will teach you to handle missing values, detect outliers, create relevant features, and optimize datasets for effective data science projects.
What you'll learn
Raw data often contains missing values, outliers, and irrelevant features that can hinder the success of data science projects.
In this course, Data Science with Python: Munging, Outliers, and Feature Engineering, you'll gain the ability to preprocess and engineer features from raw data effectively using Python.
First, you'll explore techniques for handling missing data and imputing missing values to ensure your datasets remain informative and reliable for analysis.
Next, you'll discover methods for detecting and treating outliers using statistical and machine learning approaches, developing strategies to handle them appropriately.
Finally, you'll learn how to create meaningful features from raw data, transform categorical variables, and optimize datasets for efficient analysis by dropping irrelevant columns and reordering them for better readability and processing.
When you're finished with this course, you'll have the skills and knowledge of data munging and feature engineering needed to tackle real-world data science problems, transform raw data into clean, informative datasets, and enhance the performance of your machine learning models.
More Info
What you'll learn
Raw data often contains missing values, outliers, and irrelevant features that can hinder the success of data science projects.
In this course, Data Science with Python: Munging, Outliers, and Feature Engineering, you'll gain the ability to preprocess and engineer features from raw data effectively using Python.
First, you'll explore techniques for handling missing data and imputing missing values to ensure your datasets remain informative and reliable for analysis.
Next, you'll discover methods for detecting and treating outliers using statistical and machine learning approaches, developing strategies to handle them appropriately.
Finally, you'll learn how to create meaningful features from raw data, transform categorical variables, and optimize datasets for efficient analysis by dropping irrelevant columns and reordering them for better readability and processing.
When you're finished with this course, you'll have the skills and knowledge of data munging and feature engineering needed to tackle real-world data science problems, transform raw data into clean, informative datasets, and enhance the performance of your machine learning models.
More Info