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Aije Egwaikhide and Yasmine Hemmati

This three-module course introduces machine learning and data science for everyone with a foundational understanding of machine learning models. You’ll learn about the history of machine learning, applications of machine learning, the machine learning model lifecycle, and tools for machine learning. You’ll also learn about supervised versus unsupervised learning, classification, regression, evaluating machine learning models, and more. Our labs give you hands-on experience with these machine learning and data science concepts. You will develop concrete machine learning skills as well as create a final project demonstrating your proficiency.

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This three-module course introduces machine learning and data science for everyone with a foundational understanding of machine learning models. You’ll learn about the history of machine learning, applications of machine learning, the machine learning model lifecycle, and tools for machine learning. You’ll also learn about supervised versus unsupervised learning, classification, regression, evaluating machine learning models, and more. Our labs give you hands-on experience with these machine learning and data science concepts. You will develop concrete machine learning skills as well as create a final project demonstrating your proficiency.

After completing this program, you’ll be able to realize the potential of machine learning algorithms and artificial intelligence in different business scenarios. You’ll be able to identify when to use machine learning to explain certain behaviors and when to use it to predict future outcomes. You’ll also learn how to evaluate your machine learning models and to incorporate best practices.

This Course Is Part of Multiple Programs

You can also leverage the learning from the program to complete the remaining two courses of the six-course IBM Machine Learning Professional Certificate and power a new career in the field of machine learning.

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What's inside

Syllabus

Machine Learning for Everyone
Welcome to the world of machine learning. Machine learning is a branch of artificial intelligence (AI) and computer science that focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. Machine learning is an important component in the growing field of data science. Using statistical methods, algorithms are trained to make classifications or predictions, uncovering key insights within data mining projects. These insights subsequently drive decision-making within applications and businesses, ideally impacting key growth metrics. As big data continues to expand and grow, the market demand for data scientists will increase, requiring them to assist in the identification of the most relevant business questions and subsequently the data to answer them. In this module, you will explore some of the fundamental concepts behind machine learning. You will learn to differentiate between AI, machine, and deep learning. Further, you will also explore the importance and requirements of each process in the lifecycle of a machine learning product.
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Machine Learning Topics
Machine learning is a hot topic, and everyone is trying to understand what it is about. With the amount of information that is out there about machine learning, you can get quickly overwhelmed. In this module, you will explore the most important topics in machine learning that you need to know. You will dive into supervised and unsupervised learning, classification, deep and reinforcement learning, as well as regression. Further, you will learn how to evaluate a machine learning model.
(Optional Honors) Final Project

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Introduces core concepts and foundational knowledge of machine learning
Develops understanding of various machine learning models and their applications
Provides hands-on experience through labs to apply machine learning concepts
Taught by instructors with expertise in machine learning and data science
Ideal for individuals with a foundational understanding of machine learning
Course materials provided in English, limiting accessibility for non-native speakers

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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 Machine Learning Introduction for Everyone with these activities:
Introduction to Python
Python is a popular programming language used in machine learning. Refreshing your knowledge of Python will help you better understand the concepts taught in this course.
Browse courses on Python
Show steps
  • Review the basics of Python syntax
  • Practice writing simple Python programs
Read and summarize chapters from Introduction to Machine Learning
Build a foundational understanding of machine learning concepts and algorithms
Show steps
  • Read selected chapters from the book.
  • Summarize the key points of each chapter.
  • Identify and describe the different types of machine learning algorithms.
Practice machine learning algorithms on datasets using Python
Develop practical skills in applying machine learning algorithms to real-world datasets
Show steps
  • Choose a dataset that is relevant to your interests.
  • Identify the appropriate machine learning algorithm for the dataset.
  • Implement the algorithm in Python.
  • Evaluate the performance of the algorithm.
One other activity
Expand to see all activities and additional details
Show all four activities
Create a visual representation of a machine learning model
Enhance understanding of machine learning models by creating visual representations
Browse courses on Machine Learning Models
Show steps
  • Choose a machine learning model that you have learned about in the course.
  • Identify the key components of the model.
  • Create a visual representation of the model using a tool like diagrams.
  • Share your visualization with others.

Career center

Learners who complete Machine Learning Introduction for Everyone will develop knowledge and skills that may be useful to these careers:
Machine Learning Scientist
Machine Learning Scientists are in charge of the collection, analysis, and interpretation of data. They design and develop models to predict outcomes and identify trends. By taking this course, you will gain a solid foundation in machine learning concepts and techniques. You will also learn how to apply these techniques to real-world problems. This course will help you develop the skills and knowledge you need to succeed as a Machine Learning Scientist.
Data Scientist
Data Scientists use their knowledge of statistics, mathematics, and computer science to extract insights from data. They use these insights to solve business problems and make better decisions. This course will help you develop the skills and knowledge you need to succeed as a Data Scientist. You will learn about the different types of machine learning algorithms, how to evaluate their performance, and how to apply them to real-world problems.
Data Analyst
Data Analysts are responsible for collecting, cleaning, and analyzing data. They use their findings to identify trends, patterns, and insights. This course will help you develop the skills and knowledge you need to succeed as a Data Analyst. You will learn about the different types of data analysis techniques, how to use statistical software, and how to communicate your findings effectively.
Business Analyst
Business Analysts use their knowledge of business processes and data analysis to identify opportunities for improvement. They develop and implement solutions to improve efficiency and profitability. This course will help you develop the skills and knowledge you need to succeed as a Business Analyst. You will learn about the different types of business analysis techniques, how to use data analysis software, and how to communicate your findings effectively.
Software Engineer
Software Engineers design, develop, and maintain software applications. They use their knowledge of computer science to create software that meets the needs of users. This course will help you develop the skills and knowledge you need to succeed as a Software Engineer. You will learn about the different types of software engineering methodologies, how to use software development tools, and how to test and debug software.
Quantitative Analyst
Quantitative Analysts use their knowledge of mathematics and statistics to develop models for financial markets. They use these models to make investment decisions and manage risk. This course will help you develop the skills and knowledge you need to succeed as a Quantitative Analyst. You will learn about the different types of financial models, how to use statistical software, and how to communicate your findings effectively.
Operations Research Analyst
Operations Research Analysts use their knowledge of mathematics and computer science to solve complex business problems. They develop and implement solutions to improve efficiency and profitability. This course will help you develop the skills and knowledge you need to succeed as an Operations Research Analyst. You will learn about the different types of operations research techniques, how to use optimization software, and how to communicate your findings effectively.
Market Researcher
Market Researchers use their knowledge of statistics and consumer behavior to conduct market research studies. They use their findings to develop marketing strategies and products that meet the needs of consumers. This course will help you develop the skills and knowledge you need to succeed as a Market Researcher. You will learn about the different types of market research techniques, how to use statistical software, and how to communicate your findings effectively.
Product Manager
Product Managers are responsible for the development and launch of new products. They work with engineers, designers, and marketers to bring products to market that meet the needs of customers. This course will help you develop the skills and knowledge you need to succeed as a Product Manager. You will learn about the different stages of the product development process, how to conduct market research, and how to launch and market new products.
Project Manager
Project Managers are responsible for planning, executing, and closing projects. They work with stakeholders to ensure that projects are completed on time, within budget, and to the required quality standards. This course will help you develop the skills and knowledge you need to succeed as a Project Manager. You will learn about the different stages of the project management lifecycle, how to develop project plans, and how to manage project risks.
Consultant
Consultants provide advice and guidance to businesses and organizations. They help businesses solve problems, improve efficiency, and achieve their goals. This course will help you develop the skills and knowledge you need to succeed as a Consultant. You will learn about the different types of consulting services, how to develop consulting proposals, and how to manage client relationships.
Teacher
Teachers plan, prepare, and deliver lessons to students. They assess student learning and provide feedback. This course will help you develop the skills and knowledge you need to succeed as a Teacher. You will learn about the different teaching methods and strategies, how to create lesson plans, and how to manage a classroom.
Librarian
Librarians help people find and use information. They develop and manage library collections, provide reference services, and teach information literacy skills. This course will help you develop the skills and knowledge you need to succeed as a Librarian. You will learn about the different types of library services, how to develop library collections, and how to provide reference services.
Archivist
Archivists preserve and manage historical records. They work with researchers, historians, and other professionals to ensure that these records are accessible and available for future generations. This course will help you develop the skills and knowledge you need to succeed as an Archivist. You will learn about the different types of archival materials, how to preserve and manage these materials, and how to provide access to these materials.
Museum curator
Museum Curators are responsible for the care and display of museum collections. They work with conservators, educators, and other professionals to ensure that these collections are accessible and available to the public. This course will help you develop the skills and knowledge you need to succeed as a Museum Curator. You will learn about the different types of museum collections, how to care for and display these collections, and how to provide access to these collections.

Featured in The Course Notes

This course is mentioned in our blog, The Course Notes. Read one article that features Machine Learning Introduction for Everyone:

Reading list

We've selected seven 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 Machine Learning Introduction for Everyone.
Practical guide to deep learning for programmers. It covers a wide range of topics, including supervised and unsupervised learning, classification, regression, and deep learning.
Comprehensive guide to deep learning, a subfield of machine learning that has been used to achieve state-of-the-art results on a wide range of tasks, including image recognition, natural language processing, and speech recognition.
Comprehensive guide to machine learning techniques and applications. It covers a wide range of topics, including supervised and unsupervised learning, classification, regression, and deep learning.
Practical guide to machine learning, using Python and the scikit-learn, Keras, and TensorFlow libraries. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and model evaluation.
Provides a comprehensive overview of machine learning concepts, including supervised and unsupervised learning, classification, regression, and deep learning. It is written in a clear and concise style, making it accessible to beginners.
Clear and concise introduction to machine learning. It covers a wide range of topics, including supervised and unsupervised learning, classification, regression, and deep learning.

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