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Joseph Santarcangelo
and
Svitlana (Lana) Kramar
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
Read more
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
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/4wq1g4/exploratory
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What's inside
Syllabus
A Brief History of Modern AI and its Applications
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.
Read more
Syllabus
A Brief History of Modern AI and its Applications
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.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Introduces learners to the foundational concepts of Machine Learning, preparing them for further study in the IBM Machine Learning Professional Certificate
Provides hands-on experience with real-world data, equipping learners with practical skills for data analysis and Machine Learning
Focuses on data retrieval, cleaning, feature engineering, and hypothesis testing, building a solid foundation for data analysis
Designed for aspiring data scientists seeking to apply Machine Learning and Artificial Intelligence in business settings
Taught by experienced instructors, Joseph Santarcangelo and Svitlana (Lana) Kramar, known for their contributions to Machine Learning
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Reviews summary
Practical data preparation for machine learning
According to students, this course offers a highly practical and foundational approach to Exploratory Data Analysis for Machine Learning. Learners consistently praise its emphasis on hands-on labs and coding exercises, which effectively build skills in data cleaning, feature engineering, and preparing data for ML models. While many find the instructor's explanations clear and the course structure effective for aspiring data scientists, some reviewers with prior experience suggest the content, particularly in inferential statistics and hypothesis testing, can feel superficial or too basic. Overall, it's considered excellent for beginners seeking real-world data preparation skills.
Most learners find the instructor's explanations clear and easy to follow.
"The instructor did a decent job, and the concepts were explained clearly."
"The instructor explains everything in a very digestible manner."
"Perfect for beginners who want to understand EDA from a practical perspective. The instructor is very clear and guides you through complex topics."
Delivers essential skills in data retrieval, cleaning, and feature engineering.
"The emphasis on data quality and preparation is crucial and well-covered. This is a must-take for anyone serious about a career in ML."
"Getting data ready for ML is a huge hurdle, and this course equips you with the fundamental skills to overcome it."
"Phenomenal course! Covered data cleaning, feature engineering, and visualization techniques effectively. The hands-on parts are key."
Reinforces concepts through hands-on Python exercises and real-world scenarios.
"The hands-on labs were incredibly helpful, especially the feature engineering and data cleaning modules."
"The best part about this course is its practical approach. I loved how they integrated labs after each major concept."
"I found the optional 'HONORS project' a great opportunity to apply all the skills, although it could use a bit more guidance or example solutions."
The inferential statistics module could benefit from more detailed coverage.
"Some parts felt a bit rushed, particularly the inferential statistics and hypothesis testing section."
"I had to supplement with outside resources for a deeper understanding of stats."
"The section on inferential statistics was a bit brief for my liking, but overall, it taught me valuable skills for my data science journey."
Content may feel basic for experienced learners, but is solid for beginners.
"Honestly, I was quite disappointed. The course content felt too superficial given the title. It barely scratches the surface..."
"If you're an absolute beginner, it's probably fine, but for someone with some experience, it might feel slow."
"Waste of time if you're not an absolute beginner. The content is extremely basic and doesn't align with what an 'aspiring data scientist' needs."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Exploratory Data Analysis for Machine Learning with these
activities:
Solve introductory Python exercises
Show steps
Reinforce your Python skills by solving introductory exercises, solidifying your understanding of data types, operators, and control flow.
Browse courses on
Python Basics
Show steps
-
Run simple Python programs to print values and perform basic operations.
-
Work through exercises involving variables, data types, and operators.
-
Practice using conditional statements and loops to control program flow.
Review Calculus textbook
Show steps
Reviewing a calculus textbook will provide foundational knowledge necessary for understanding machine learning concepts.
View
Algebra and Trigonometry
on Amazon
Show steps
-
Read Chapter 1: Limits and Continuity
-
Practice solving problems from Section 1.1
-
Complete the practice quiz at the end of Section 1.2
Read Introduction to Machine Learning, 4th Edition
Show steps
Supplement the course content with a detailed overview of Machine Learning to reinforce your understanding of the concepts
View
Machine Learning, revised and updated edition
on Amazon
Show steps
-
Read the book
-
Take notes
-
Complete the exercises
12 other activities
Expand to see all activities and additional details
Show all 15 activities
Review linear regression
Show steps
Brush up on the fundamentals of linear regression to enhance your comprehension of supervised learning models.
Show steps
-
Revisit the concept of linear equations and their graphical representation.
-
Recall the formula for calculating the slope and intercept of a line.
-
Practice fitting a linear model to a dataset using a statistical software package.
Follow tutorials on Python for Data Science
Show steps
Guided tutorials will provide hands-on practice with Python, a programming language commonly used in machine learning.
Browse courses on
Python
Show steps
-
Sign up for a free account on Codecademy
-
Enroll in the 'Python for Data Science' course
-
Complete the first three modules
Complete Python coding exercises
Show steps
Exercise your understanding of programming on a Python development environment to prepare yourself to jump into the course content
Browse courses on
Python
Show steps
-
Find online Python coding exercises
-
Complete the exercises
-
Review the solutions
Create a machine learning project
Show steps
Solidify your knowledge and skills by practicing your understanding of programming on a Python development environment, and in Calculus, Linear Algebra, Probability, and Statistics
Browse courses on
Linear Algebra
Show steps
-
Identify a dataset and a business problem
-
Load, clean, and explore the data
-
Build a machine learning model
-
Evaluate the model's performance
-
Deploy the model
Join a study group to discuss machine learning concepts
Show steps
Participating in a study group will provide opportunities to collaborate with others and deepen your understanding of machine learning.
Browse courses on
Machine Learning
Show steps
-
Find a study group or create your own
-
Meet regularly to discuss course materials
-
Work together on assignments and projects
Practice data cleaning and feature engineering
Show steps
Practice drills will reinforce data cleaning and feature engineering techniques, which are essential for preparing data for machine learning algorithms.
Browse courses on
Data Cleaning
Show steps
-
Download the 'UCI Machine Learning Repository' dataset
-
Clean the dataset by removing duplicate rows and missing values
-
Create new features by combining existing features
Follow a Machine Learning tutorial
Show steps
Expand your knowledge by diving into tutorials outside of the course to enhance your understanding of Machine Learning
Browse courses on
Machine Learning
Show steps
-
Find a Machine Learning tutorial
-
Follow the tutorial
-
Apply what you learned
Build a machine learning model to predict customer churn
Show steps
Building a machine learning model will provide practical experience with the entire machine learning pipeline.
Browse courses on
Machine Learning
Show steps
-
Collect customer data from your CRM system
-
Clean and prepare the data
-
Train a machine learning model to predict customer churn
-
Evaluate the performance of the model
Create a presentation on the applications of machine learning
Show steps
Creating a presentation will help you synthesize your understanding of the applications of machine learning and communicate it effectively.
Browse courses on
Machine Learning
Show steps
-
Research different applications of machine learning
-
Develop a presentation outline
-
Create slides that are visually appealing and informative
-
Practice presenting your slides
Write a blog post about Machine Learning
Show steps
Solidify your understanding of Machine Learning and enhance your communication skills by writing a blog post summarizing your key takeaways from the course
Browse courses on
Machine Learning
Show steps
-
Research the topic
-
Write the blog post
-
Choose a topic
-
Publish the blog post
Join a study group
Show steps
Enhance your understanding of the course material, share knowledge, and receive support by participating in a study group
Browse courses on
Machine Learning
Show steps
-
Find a study group
-
Attend study group meetings
-
Participate in discussions
Participate in a Machine Learning competition
Show steps
Challenge yourself, apply your skills, and potentially win prizes by participating in a Machine Learning competition related to the course
Browse courses on
Machine Learning
Show steps
-
Find a Machine Learning competition
-
Register for the competition
-
Build a model
-
Submit your model
Solve introductory Python exercises
Show steps
Reinforce your Python skills by solving introductory exercises, solidifying your understanding of data types, operators, and control flow.
Browse courses on
Python Basics
Show steps
Show steps
Show steps
- Run simple Python programs to print values and perform basic operations.
- Work through exercises involving variables, data types, and operators.
- Practice using conditional statements and loops to control program flow.
Review Calculus textbook
Show steps
Reviewing a calculus textbook will provide foundational knowledge necessary for understanding machine learning concepts.
View
Algebra and Trigonometry
on Amazon
Show steps
Show steps
Show steps
- Read Chapter 1: Limits and Continuity
- Practice solving problems from Section 1.1
- Complete the practice quiz at the end of Section 1.2
Read Introduction to Machine Learning, 4th Edition
Show steps
Supplement the course content with a detailed overview of Machine Learning to reinforce your understanding of the concepts
View
Machine Learning, revised and updated edition
on Amazon
Show steps
Show steps
Show steps
- Read the book
- Take notes
- Complete the exercises
12 other activities
Expand to see all activities and additional details
Show all 15 activities
Review linear regression
Show steps
Brush up on the fundamentals of linear regression to enhance your comprehension of supervised learning models.
Show steps
Show steps
Show steps
- Revisit the concept of linear equations and their graphical representation.
- Recall the formula for calculating the slope and intercept of a line.
- Practice fitting a linear model to a dataset using a statistical software package.
Follow tutorials on Python for Data Science
Show steps
Guided tutorials will provide hands-on practice with Python, a programming language commonly used in machine learning.
Browse courses on
Python
Show steps
Show steps
Show steps
- Sign up for a free account on Codecademy
- Enroll in the 'Python for Data Science' course
- Complete the first three modules
Complete Python coding exercises
Show steps
Exercise your understanding of programming on a Python development environment to prepare yourself to jump into the course content
Browse courses on
Python
Show steps
Show steps
Show steps
- Find online Python coding exercises
- Complete the exercises
- Review the solutions
Create a machine learning project
Show steps
Solidify your knowledge and skills by practicing your understanding of programming on a Python development environment, and in Calculus, Linear Algebra, Probability, and Statistics
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Identify a dataset and a business problem
- Load, clean, and explore the data
- Build a machine learning model
- Evaluate the model's performance
- Deploy the model
Join a study group to discuss machine learning concepts
Show steps
Participating in a study group will provide opportunities to collaborate with others and deepen your understanding of machine learning.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find a study group or create your own
- Meet regularly to discuss course materials
- Work together on assignments and projects
Practice data cleaning and feature engineering
Show steps
Practice drills will reinforce data cleaning and feature engineering techniques, which are essential for preparing data for machine learning algorithms.
Browse courses on
Data Cleaning
Show steps
Show steps
Show steps
- Download the 'UCI Machine Learning Repository' dataset
- Clean the dataset by removing duplicate rows and missing values
- Create new features by combining existing features
Follow a Machine Learning tutorial
Show steps
Expand your knowledge by diving into tutorials outside of the course to enhance your understanding of Machine Learning
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find a Machine Learning tutorial
- Follow the tutorial
- Apply what you learned
Build a machine learning model to predict customer churn
Show steps
Building a machine learning model will provide practical experience with the entire machine learning pipeline.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Collect customer data from your CRM system
- Clean and prepare the data
- Train a machine learning model to predict customer churn
- Evaluate the performance of the model
Create a presentation on the applications of machine learning
Show steps
Creating a presentation will help you synthesize your understanding of the applications of machine learning and communicate it effectively.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Research different applications of machine learning
- Develop a presentation outline
- Create slides that are visually appealing and informative
- Practice presenting your slides
Write a blog post about Machine Learning
Show steps
Solidify your understanding of Machine Learning and enhance your communication skills by writing a blog post summarizing your key takeaways from the course
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Research the topic
- Write the blog post
- Choose a topic
- Publish the blog post
Join a study group
Show steps
Enhance your understanding of the course material, share knowledge, and receive support by participating in a study group
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find a study group
- Attend study group meetings
- Participate in discussions
Participate in a Machine Learning competition
Show steps
Challenge yourself, apply your skills, and potentially win prizes by participating in a Machine Learning competition related to the course
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find a Machine Learning competition
- Register for the competition
- Build a model
- Submit your model
Career center
Learners who complete Exploratory Data Analysis for Machine Learning will develop knowledge and skills
that may be useful to these careers:
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial intelligence engineers design, develop, and maintain artificial intelligence systems. This course helps build a foundation for building artificial intelligence systems by teaching you how to work with data, clean it, feature engineer it, and have it ready for preliminary analysis and hypothesis testing.
Machine Learning Engineer
Machine Learning Engineer
Machine learning engineers build and maintain the machine learning models that power artificial intelligence applications. This course helps build a foundation for building machine learning models by teaching you how to work with data, clean it, feature engineer it, and have it ready for preliminary analysis and hypothesis testing.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data. The Exploratory Data Analysis for Machine Learning course may be useful for aspiring statisticians who want to learn how to use data to solve problems.
Data Engineer
Data Engineer
Data engineers design, build, and maintain the infrastructure that stores and processes data. The Exploratory Data Analysis for Machine Learning course may be useful for aspiring data engineers who want to learn how to clean and prepare data for analysis.
Data Analyst
Data Analyst
Data analysts collect, analyze, interpret, and present data. The Exploratory Data Analysis for Machine Learning course may be useful for aspiring data analysts who want to learn how to retrieve data, clean it, and explore it for insights.
Software Developer
Software Developer
Software developers design, develop, and maintain software applications. The Exploratory Data Analysis for Machine Learning course may be useful for software developers who want to learn how to use data to improve the software they develop.
Business Analyst
Business Analyst
Business analysts help businesses understand their data and make better decisions. This course may be useful for aspiring business analysts who want to learn how to use data to solve business problems.
Data Architect
Data Architect
Data architects design and build the systems that store and process data. The Exploratory Data Analysis for Machine Learning course may be useful for aspiring data architects who want to learn how to design and build systems that can handle large amounts of data.
Data Scientist
Data Scientist
A data scientist analyzes data using scientific methods to draw conclusions from it and communicate the results in a way that supports decision-making. The Exploratory Data Analysis for Machine Learning course may be useful for aspiring data scientists who want to learn how to delve into data and model it, a foundational skill for data scientists.
Operations Research Analyst
Operations Research Analyst
Operations research analysts use mathematical and analytical techniques to solve business problems. The Exploratory Data Analysis for Machine Learning course may be useful for aspiring operations analysts who want to learn how to use data to improve business processes.
Database Administrator
Database Administrator
Database administrators maintain the databases that store data. The Exploratory Data Analysis for Machine Learning course may be useful for aspiring database administrators who want to learn how to manage and maintain databases.
Financial Analyst
Financial Analyst
Financial analysts use data to make investment recommendations. The Exploratory Data Analysis for Machine Learning course may be useful for aspiring financial analysts who want to learn how to use data to make better investment decisions.
Software Engineer
Software Engineer
Software engineers design, develop, and maintain software applications. The Exploratory Data Analysis for Machine Learning course may be useful for software engineers who want to learn how to use data to improve the software they develop.
Market Researcher
Market Researcher
Market researchers collect, analyze, and interpret data to understand customer needs and preferences. The Exploratory Data Analysis for Machine Learning course may be useful for aspiring market researchers who want to learn how to use data to make better marketing decisions.
Product Manager
Product Manager
Product managers develop and manage products. The Exploratory Data Analysis for Machine Learning course may be useful for aspiring product managers who want to learn how to use data to make better decisions about product development.
For more career information including salaries, visit:
OpenCourser.com/course/4wq1g4/exploratory
Reading list
We've selected nine books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Exploratory Data Analysis for Machine Learning.
Save
Provides a comprehensive overview of the process of predictive modeling, from data preparation to model evaluation. It valuable resource for anyone who wants to learn about the fundamentals of predictive modeling or who wants to improve their skills in this area.
Classic textbook on statistical learning. It covers a wide range of topics, from linear regression to support vector machines. It valuable resource for anyone who wants to learn about the theoretical foundations of statistical learning.
Practical guide to machine learning using Python. It covers a wide range of topics, from data preparation to model evaluation. It valuable resource for anyone who wants to learn about the practical aspects of machine learning using Python.
Comprehensive overview of machine learning using R. It covers a wide range of topics, from data preparation to model evaluation. It valuable resource for anyone who wants to learn about the practical aspects of machine learning using R.
Save
Comprehensive overview of data mining. It covers a wide range of topics, from data preparation to data visualization. It valuable resource for anyone who wants to learn about the foundations of data mining.
Comprehensive overview of statistical methods for machine learning. It covers a wide range of topics, from probability to Bayesian statistics. It valuable resource for anyone who wants to learn about the theoretical foundations of machine learning.
Practical guide to machine learning. It covers a wide range of topics, from data preparation to model evaluation. It valuable resource for anyone who wants to learn about the practical aspects of machine learning.
Practical guide to machine learning. It covers a wide range of topics, from data preparation to model evaluation. It valuable resource for anyone who wants to learn about the practical aspects of machine learning.
Save
Comprehensive overview of deep learning. It covers a wide range of topics, from the basics of deep learning to the latest advances in the field. It valuable resource for anyone who wants to learn about the theoretical foundations of deep learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/4wq1g4/exploratory
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Rating
4.6
Effort
Approx. 14 hours
Level
Intermediate
Via
Coursera
Institution
IBM
Instructors
Joseph Santarcangelo
Svitlana (Lana) Kramar
Language
English
Transcripts
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آذربایجان دیلی
This course belongs to a
collection
called
IBM Introduction to Machine Learning. It's comprised of four courses:
- Supervised Machine Learning: Regression
- Exploratory Data Analysis for Machine Learning (viewing)
- Supervised Machine Learning: Classification
- Unsupervised Machine Learning
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