Exploratory Data Analysis for ML

Exploratory Data Analysis for ML

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Gain practical experience in AI and Machine Learning for business, focusing on data extraction, feature engineering, outlier management, and feature scaling for aspiring data scientists with foundational math and Python skills.

Key AI Functions:["Artificial Intelligence (AI)","Machine Learning","Feature Engineering","Statistical Hypothesis Testing","Exploratory "Data Analysis"]

Description for Exploratory Data Analysis for ML

Features of Course

  • Acquire the ability to extract data from a variety of sources, such as APIs, NoSQL databases, and the cloud.
  • Comprehend and execute conventional feature engineering, feature selection methodologies, and the management of ordinal, categorical, and missing values.
  • Utilize a variety of methodologies to detect and reduce outliers, and comprehend the significance of feature scaling through the application of diverse scaling methods.
  • Aimed at aspiring data scientists with a foundational understanding of Calculus, Linear Algebra, Probability, Statistics, and Python programming, this course provides practical experience with AI and Machine Learning in a business setting.
  • Level: Intermediate

    Certification Degree: Yes

    Languages the Course is Available: 22

    Offered by: On Coursera provided by IBM

    Duration: 14 hours (approximately)

    Schedule: Flexible

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