IBM - Exploratory Data Analysis for Machine Learning
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Exploratory Data Analysis for Machine Learning at Coursera Overview
Duration | 8 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Exploratory Data Analysis for Machine Learning at Coursera Highlights
- Earn a Certificate upon completion
- Taught by top companies and universities
- Learn on your own schedule
Exploratory Data Analysis for Machine Learning at Coursera Course details
- 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.
Exploratory Data Analysis for Machine Learning at Coursera Curriculum
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
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
Exploratory Data Analysis for Machine Learning at Coursera Admission Process
Important Dates
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