Exploratory Data Analysis for Machine Learning – W7101G WBT

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Details


Course Code: W7101G

Brand: Cloud & Data Platform

Category: Cloud

Skill Level: Intermediate

Duration: 8.00H

Modality: WBT

 

 

Audience


This course targets aspiring data scientists interested in acquiring hands-on experience with Machine Learning and Artificial Intelligence in a business setting.

 

Prerequisites


To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Calculus, Linear Algebra, Probability, and Statistics.

 

Short Summary


This course introduces you to Machine Learning. You will learn the importance of good, quality data, and how to retrieve it, clean it, and apply feature engineering.

 

Overview


This first course in the IBM Machine Learning Professional Certificate introduces you to Machine Learning and the content of the professional certificate. In this course you will realize the importance of good, quality data. You will learn common techniques to retrieve your data, clean it, apply feature engineering, and have it ready for preliminary analysis and hypothesis testing.

 

IBM Clients and Sellers – Consider this course as part of an individual or enterprise subscription service:

  • IBM Data/AI Individual Subscription (SUBR003G)
  • IBM Digital Learning Subscription — IBM Data/AI Enterprise Subscription (SUBR004G)
  • IBM Learning Individual Subscription with Red Hat Learning Services (SUBR013G)

 

Topic


1. A Brief History of Modern AI and its Applications

2. Retrieving Data, Exploratory Data Analysis, and Feature Engineering

3. Inferential Statistics and Hypothesis Testing

 

Objectives


By the end of this course you should be able to:
– Retrieve data from multiple data sources: SQL, NoSQL databases, APIs, Cloud. Describe and use common feature selection and feature engineering techniques. 

– Handle categorical and ordinal features, as well as missing values. 

– Use a variety of techniques for detecting and dealing with outliers. 

– Articulate why feature scaling is important and use a variety of scaling techniques.

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