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Exploratory Data Analysis for Machine Learning

Approx. 8 hours to complete

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Course Summary

This course provides learners with a foundational understanding of exploratory data analysis (EDA) and its role in machine learning (ML). Students will learn how to apply EDA techniques to analyze datasets and prepare data for ML models.

Key Learning Points

Learn the importance of exploratory data analysis in machine learning
Practice using Python libraries for data analysis
Apply statistical techniques to analyze and visualize data

Learning Outcomes

Understand the importance of EDA in machine learning
Gain proficiency in Python libraries for data analysis
Apply statistical techniques to analyze and visualize data

Prerequisites or good to have knowledge before taking this course

Basic understanding of Python programming
Familiarity with basic statistical concepts

Course Difficulty Level

Intermediate

Course Format

Online, self-paced
Video lectures
Hands-on exercises

Similar Courses

Data Science Essentials
Python for Data Science

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Description

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.

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 Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience with Machine Learning and Artificial Intelligence in a business setting. What skills should you have? 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.

Outline

A Brief History of Modern AI and its Applications
Welcome/Introduction Video
Introduction to Artificial Intelligence and Machine Learning
Machine Learning and Deep Learning
History of AI
History of Machine Learning and Deep Learning
Modern AI
Applications
Machine Learning Workflow
Course Prerequisites
Summary/Review
Check for Understanding
Check for Understanding
Module 1 Quiz

Retrieving Data, Exploratory Data Analysis, and Feature Engineering
Retrieving Data
Demo: Reading Data Demo Jupyter Notebook
Lab Solution: Reading in Database Files
Data Cleaning
Handling Missing Values and Outliers
EDA - Part 1
EDA - Part 2
Solution: EDA Notebook - Part 1
Solution: EDA Notebook - Part 2
Solution: EDA Notebook - Part 3
Solution: EDA Notebook - Part 4
Feature Engineering and Variable Transformation - Part 1
Feature Engineering and Variable Transformation - Part 2
Solution: Feature Engineering Lab - Part 1
Solution: Feature Engineering Lab - Part 2
Solution: Feature Engineering Lab-Part 3
Demo: Reading in Database Files (Activity)
Lab: Reading in Database Files (Activity)
Exploratory Data Analysis Lab (Activity)
Feature Engineering Demo (Activity)
Summary/Review
Check for Understanding
Check for Understanding
Check for Understanding
Check for Understanding
Module 2 Quiz

Inferential Statistics and Hypothesis Testing
Estimation and Inference - Part 1
Estimation and Inference - Part 2
Estimation and Inference - Part 3
Hypothesis Testing
Type 1 vs Type 2 Error
Significance Level and P-Values - Part 1
Significance Level and P-Values - Part 2
Hypothesis Testing Demo - Part 1
Hypothesis Testing Demo - Part 2
Correlation vs Causation
Hypothesis Testing Demo (Activity)
Summary/Review
Check for Understanding
Check for Understanding
Module 3 Quiz

Summary of User Reviews

Explore the world of machine learning through IBM's Exploratory Data Analysis course on Coursera. This course has received high praise from users, with many pointing to its comprehensive coverage of the subject matter as a key strength.

Key Aspect Users Liked About This Course

Comprehensive coverage of the subject matter

Pros from User Reviews

In-depth coverage of machine learning concepts and techniques
Detailed explanations of key concepts and algorithms
Hands-on exercises and projects to reinforce learning
Engaging and knowledgeable instructors
Flexible schedule and self-paced learning

Cons from User Reviews

Some users found the course content to be too basic
Lack of interaction with instructors and other students
Occasional technical issues with the online platform
Some users found the course to be too time-consuming
Lack of practical applications for certain topics

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