Exploratory Data Analysis for Machine Learning

Overview

Title:

Exploratory Data Analysis for Machine Learning

Description:

Kickstart your journey in Machine Learning with the first course in the IBM Machine Learning Professional Certificate, offered through Coursera. This foundational course empowers you to appreciate the critical role of high-quality data in machine learning processes.

You will master various techniques to extract, clean, and prepare your data effectively. The course covers essential skills including feature engineering, handling different types of data, managing missing values, and identifying outliers.

You'll also explore the significance of feature scaling and implement numerous scaling techniques. By the course's conclusion, you will be adept at fetching data from diverse sources such as SQL and NoSQL databases, APIs, and Cloud environments.

Learning Goals:

• Retrieve data from various sources including SQL, NoSQL, APIs, and Cloud
• Understand and employ feature engineering and selection techniques
• Manage categorical and ordinal features along with missing data
• Implement methods for outlier detection and management
• Recognize the importance of feature scaling and apply multiple scaling methods

Intended Audience:

This course is ideal for aspiring data scientists who want to gain practical knowledge in Machine Learning and AI within a business context.

Prerequisites:

Participants should have experience with Python programming, and basic knowledge of Calculus, Linear Algebra, Probability, and Statistics.

Provider:

Coursera

Categories:

Artificial Intelligence Courses, Machine Learning Courses, Data Analysis Courses, Data Visualization Courses, Data Cleaning Courses

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Taught by

Mark J Grover and Miguel Maldonado

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Subjects