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There are 5 modules in this course
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.
Artificial Intelligence is not new, but it is new in a sense that it is easier than ever to get started using Machine Learning in business settings. In this module, we will go over a quick introduction to AI and Machine Learning and we will visit a brief history of the modern AI. We will also explore some of the current applications of AI and Machine Learning for you, to think about how you want to leverage them in your day to day business practice or personal projects.
Introduction to Artificial Intelligence and Machine Learning•6 minutes
Machine Learning and Deep Learning•10 minutes
History of AI•8 minutes
History of Machine Learning and Deep Learning•5 minutes
Modern AI•7 minutes
Applications•3 minutes
Machine Learning Workflow•7 minutes
2 readings•Total 7 minutes
Course Prerequisites•4 minutes
Review•3 minutes
3 assignments•Total 40 minutes
Practice Quiz: Artificial Intelligence and Machine Learning•5 minutes
Practice Quiz: Modern AI Applications and Workflows •5 minutes
Graded Quiz: Module 1 - Modern AI and its Applications•30 minutes
1 discussion prompt•Total 10 minutes
Optional: Say hi or reach out for help•10 minutes
Retrieving and Cleaning Data
Module 2•3 hours to complete
Module details
Good data is the fuel that powers Machine Learning and Artificial Intelligence. In this module, you will learn how to retrieve data from different sources, how to clean it to ensure its quality.
What's included
7 videos3 readings3 assignments3 app items
Show info about module content
7 videos•Total 43 minutes
Retrieving Data from CSV and JSON Files•5 minutes
Retrieving Data from Databases, APIs, and the Cloud•9 minutes
[Optional] Lab Solution: Reading Data Jupyter Notebook - Part A•7 minutes
[Optional]Lab Solution: Reading in Database Files - Part B•5 minutes
Data Cleaning•6 minutes
Handling Missing Values and Outliers•7 minutes
Handling Missing Values and Outliers using Residuals•4 minutes
3 readings•Total 7 minutes
[Optional] Download Assets for Lab: Reading Data in Database Files - Part A•2 minutes
[Optional] Download Assets for Lab: Reading Data in Jupyter Notebook - Part B•2 minutes
Summary/Review•3 minutes
3 assignments•Total 44 minutes
Practice Quiz: Retrieving Data•9 minutes
Practice Quiz: Data Cleaning•5 minutes
Graded Quiz: Module 2 - Retrieving Data and Cleaning Data•30 minutes
3 app items•Total 85 minutes
Demo Lab: Reading Data in Database Files - Part A•20 minutes
Demo Lab: Reading Data in Jupyter Notebook - Part B•20 minutes
Practice Lab: Data Cleaning•45 minutes
Exploratory Data Analysis and Feature Engineering
Module 3•5 hours to complete
Module details
In this module you will learn how to conduct exploratory analysis to visually confirm it is ready for machine learning modeling by feature engineering and transformations.
What's included
15 videos3 readings3 assignments4 app items
Show info about module content
15 videos•Total 104 minutes
Introduction to Exploratory Data Analysis (EDA)•6 minutes
EDA with Visualization•6 minutes
Grouping Data for EDA•6 minutes
[Optional]Solution: EDA Notebook - Part 1•6 minutes
[Optional]Solution: EDA Notebook - Part 2•11 minutes
[Optional]Solution: EDA Notebook - Part 3•7 minutes
[Optional]Solution: EDA Notebook - Part 4•8 minutes
Feature Engineering and Variable Transformation - Background•3 minutes
Variable Transformation•5 minutes
Feature Encoding•4 minutes
Feature Scaling•5 minutes
Common Variable Transformations in Python•3 minutes
[Optional] Solution: Feature Engineering Lab - Part 1•12 minutes
[Optional] Solution: Feature Engineering Lab - Part 2•13 minutes
[Optional] Solution: Feature Engineering Lab - Part 3•10 minutes
3 readings•Total 14 minutes
[Optional] Download Assets for Lab: Exploratory Data Analysis Lab•2 minutes
[Optional] Download Assets for Lab: Feature Engineering Demo •2 minutes
Summary/Review•10 minutes
3 assignments•Total 40 minutes
Practice Quiz: Exploratory Data Analysis•5 minutes
Practice Quiz: Feature Engineering and Variable Transformation•5 minutes
Graded Quiz: Module 3 - Exploratory Data Analysis and Feature Engineering•30 minutes
4 app items•Total 135 minutes
Demo Lab: Exploratory Data Analysis•30 minutes
Practice Lab: Exploratory Data Analysis•30 minutes
Demo Lab: Feature Engineering•30 minutes
Practice Lab: Feature Engineering•45 minutes
Inferential Statistics and Hypothesis Testing
Module 4•3 hours to complete
Module details
Inferential statistics and hypothesis testing are two types of data analysis often overlooked at early stages of analyzing your data. They can give you quick insights about the quality of your data. They also help you confirm business intuition and help you prescribe what to analyze next using Machine Learning. This module looks at useful definitions and simple examples that will help you get started creating hypothesis around your business problem and how to test them.
In this assignment, you will apply the skills learned throughout the course to analyze a dataset of your choice, either from the course materials or an external source. You will perform data cleaning, feature engineering, exploratory data visualization, and hypothesis testing to derive meaningful insights. Upon completion, your work will be evaluated automatically by an AI grading tool.
What's included
4 readings1 app item
Show info about module content
4 readings•Total 26 minutes
Project Overview•10 minutes
Submission Guidelines•10 minutes
Congratulations & Next Steps•5 minutes
Thanks from the Course Team•1 minute
1 app item•Total 60 minutes
Final Project Submission and Evaluation•60 minutes
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Learner reviews
4.6
2,563 reviews
5 stars
73.01%
4 stars
19.18%
3 stars
4.44%
2 stars
1.83%
1 star
1.52%
Showing 3 of 2563
B
BD
5·
Reviewed on Apr 23, 2024
The course includes hands-on exercises that allows us to apply the learned EDA techniques to real-world data. This practical approach helps solidify my understanding.
A
AP
5·
Reviewed on Feb 25, 2023
This course was amazing. I always assumed that EDA was the challenging part of ML, But in this course I found it so cool. can't wait for the next course.
D
DS
4·
Reviewed on Nov 30, 2020
The only reason that I do not give it 5 stars is because the website of coursera is not good enough to handle the peer review assignments at the end of the course.
When will I have access to the lectures and assignments?
To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Certificate?
When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.