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Amber Israelsen
Machine learning is one of the hottest fields in the world today. This course will teach you how to get started solving business problems using Python.
You’ve probably heard about how machine learning is shaping our world—from facial recognition to package delivery, from speech recognition to product recommendations. But how do you get started in this exciting field?
In this course, Data Science with Python: Foundations of Machine Learning, you’ll gain the ability to solve business problems using Python.
First, you’ll explore how to identify and frame various types of machine learning problems.
Read more
Machine learning is one of the hottest fields in the world today. This course will teach you how to get started solving business problems using Python.
You’ve probably heard about how machine learning is shaping our world—from facial recognition to package delivery, from speech recognition to product recommendations. But how do you get started in this exciting field?
In this course, Data Science with Python: Foundations of Machine Learning, you’ll gain the ability to solve business problems using Python.
First, you’ll explore how to identify and frame various types of machine learning problems.
Read more
Machine learning is one of the hottest fields in the world today. This course will teach you how to get started solving business problems using Python.
You’ve probably heard about how machine learning is shaping our world—from facial recognition to package delivery, from speech recognition to product recommendations. But how do you get started in this exciting field?
In this course, Data Science with Python: Foundations of Machine Learning, you’ll gain the ability to solve business problems using Python.
First, you’ll explore how to identify and frame various types of machine learning problems.
Next, you’ll discover how to train a machine learning model, using prepared data.
Finally, you’ll learn how to evaluate the performance of a machine learning model using various evaluation metrics.
When you’re finished with this course, you’ll have the skills and knowledge of the machine learning process to effectively build, train, and evaluate your own models using Python.
Register for this course and see more details by visiting:
OpenCourser.com/course/k8kmmw/data
What's inside
Syllabus
Course Overview
Understanding Machine Learning Basics
Evaluating Machine Learning Models
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops skills and knowledge of the machine learning process to effectively build, train, and evaluate models using Python
Introduces machine learning terminology, concepts, and models
Provides a foundation for further learning in data science
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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 Data Science with Python: Foundations of Machine Learning with these
activities:
Create a Course Summary
Show steps
Consolidating your notes and materials into a comprehensive summary will enhance your retention and understanding of the course content.
Show steps
-
Review and organize your notes, assignments, and quizzes.
-
Create a summary document that outlines the key concepts and takeaways from each module.
Review Python Basics
Show steps
Refresh your understanding of Python's basic syntax and data structures, ensuring a strong foundation for effective machine learning implementation.
Browse courses on
Python Basics
Show steps
-
Revisit online tutorials or documentation on Python basics.
-
Solve coding exercises or puzzles to practice Python skills.
Review Python Fundamentals
Show steps
Warming up with Python fundamentals will prepare you to confidently tackle the challenges in this course.
Browse courses on
Python Basics
Show steps
-
Go through the official Python tutorial.
-
Complete beginner-level Python exercises and challenges.
Seven other activities
Expand to see all activities and additional details
Show all ten activities
Join a Study Group
Show steps
Engaging with peers in a study group will foster collaboration, discussion, and deeper understanding of the course material.
Show steps
-
Connect with other students enrolled in the course.
-
Schedule regular meetings to discuss course topics.
Build a Machine Learning Resource Collection
Show steps
Create a comprehensive collection of valuable resources, including tutorials, articles, and tools, to support your ongoing machine learning journey.
Show steps
-
Identify and gather relevant resources from online sources.
-
Organize and categorize the resources based on topic or purpose.
-
Share your collection with fellow learners or online communities.
Practice Machine Learning Algorithms
Show steps
Solving practical machine learning problems will deepen your understanding of the algorithms and techniques covered in this course.
Browse courses on
Linear Regression
Show steps
-
Work through the exercises and coding challenges in the course materials.
-
Implement machine learning algorithms from scratch.
Solve Machine Learning Puzzles
Show steps
Tackling puzzles will sharpen your problem-solving skills and reinforce the concepts covered in this course.
Browse courses on
Machine Learning Algorithms
Show steps
-
Participate in online machine learning coding competitions.
-
Work through puzzle books or websites dedicated to machine learning.
Evaluate Machine Learning Model Performance
Show steps
Gain hands-on experience in evaluating the performance of machine learning models, ensuring their effectiveness and reliability for real-world applications.
Show steps
-
Implement various evaluation metrics to assess model performance.
-
Create visualizations to present evaluation results and identify areas for improvement.
-
Document your findings and insights in a comprehensive report.
Explore Advanced Machine Learning Concepts
Show steps
Delving into advanced machine learning concepts will broaden your knowledge and equip you for future challenges in the field.
Browse courses on
Deep Learning
Show steps
-
Follow online tutorials and workshops on advanced machine learning topics.
-
Read research papers and articles on the latest advancements in machine learning.
Develop a Machine Learning Project
Show steps
Building a machine learning project from scratch will provide hands-on experience and showcase your skills.
Show steps
-
Identify a real-world problem that can be solved using machine learning.
-
Gather and prepare data.
-
Train and evaluate machine learning models.
-
Deploy and monitor the machine learning model.
Create a Course Summary
Show steps
Consolidating your notes and materials into a comprehensive summary will enhance your retention and understanding of the course content.
Show steps
Show steps
Show steps
- Review and organize your notes, assignments, and quizzes.
- Create a summary document that outlines the key concepts and takeaways from each module.
Review Python Basics
Show steps
Refresh your understanding of Python's basic syntax and data structures, ensuring a strong foundation for effective machine learning implementation.
Browse courses on
Python Basics
Show steps
Show steps
Show steps
- Revisit online tutorials or documentation on Python basics.
- Solve coding exercises or puzzles to practice Python skills.
Review Python Fundamentals
Show steps
Warming up with Python fundamentals will prepare you to confidently tackle the challenges in this course.
Browse courses on
Python Basics
Show steps
Show steps
Show steps
- Go through the official Python tutorial.
- Complete beginner-level Python exercises and challenges.
Seven other activities
Expand to see all activities and additional details
Show all ten activities
Join a Study Group
Show steps
Engaging with peers in a study group will foster collaboration, discussion, and deeper understanding of the course material.
Show steps
Show steps
Show steps
- Connect with other students enrolled in the course.
- Schedule regular meetings to discuss course topics.
Build a Machine Learning Resource Collection
Show steps
Create a comprehensive collection of valuable resources, including tutorials, articles, and tools, to support your ongoing machine learning journey.
Show steps
Show steps
Show steps
- Identify and gather relevant resources from online sources.
- Organize and categorize the resources based on topic or purpose.
- Share your collection with fellow learners or online communities.
Practice Machine Learning Algorithms
Show steps
Solving practical machine learning problems will deepen your understanding of the algorithms and techniques covered in this course.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Work through the exercises and coding challenges in the course materials.
- Implement machine learning algorithms from scratch.
Solve Machine Learning Puzzles
Show steps
Tackling puzzles will sharpen your problem-solving skills and reinforce the concepts covered in this course.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Participate in online machine learning coding competitions.
- Work through puzzle books or websites dedicated to machine learning.
Evaluate Machine Learning Model Performance
Show steps
Gain hands-on experience in evaluating the performance of machine learning models, ensuring their effectiveness and reliability for real-world applications.
Show steps
Show steps
Show steps
- Implement various evaluation metrics to assess model performance.
- Create visualizations to present evaluation results and identify areas for improvement.
- Document your findings and insights in a comprehensive report.
Explore Advanced Machine Learning Concepts
Show steps
Delving into advanced machine learning concepts will broaden your knowledge and equip you for future challenges in the field.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Follow online tutorials and workshops on advanced machine learning topics.
- Read research papers and articles on the latest advancements in machine learning.
Develop a Machine Learning Project
Show steps
Building a machine learning project from scratch will provide hands-on experience and showcase your skills.
Show steps
Show steps
Show steps
- Identify a real-world problem that can be solved using machine learning.
- Gather and prepare data.
- Train and evaluate machine learning models.
- Deploy and monitor the machine learning model.
Career center
Learners who complete Data Science with Python: Foundations of Machine Learning will develop knowledge and skills
that may be useful to these careers:
Financial Analyst
Financial Analyst
Financial Analysts may find the course, *Data Science with Python: Foundations of Machine Learning* helpful. This course will provide Financial Analysts with the knowledge and skills necessary to apply machine learning techniques to their work. By learning how to train and evaluate machine learning models, Financial Analysts can gain hands-on experience in using these powerful tools to make better investment decisions and manage risk.
Risk Manager
Risk Manager
Risk Managers may find the course, *Data Science with Python: Foundations of Machine Learning* helpful. This course will provide Risk Managers with the knowledge and skills necessary to apply machine learning techniques to their work. By learning how to train and evaluate machine learning models, Risk Managers can gain hands-on experience in using these powerful tools to better assess risk and make informed decisions.
Statistician
Statistician
Statisticians may find the course, *Data Science with Python: Foundations of Machine Learning* helpful. This course will provide Statisticians with the knowledge and skills necessary to apply machine learning techniques to their work. By learning how to train and evaluate machine learning models, Statisticians can gain hands-on experience in using these powerful tools.
Actuary
Actuary
Actuaries may find the course, *Data Science with Python: Foundations of Machine Learning* helpful. This course will provide Actuaries with the knowledge and skills necessary to apply machine learning techniques to their work. By learning how to train and evaluate machine learning models, Actuaries can gain hands-on experience in using these powerful tools to better assess risk and make informed decisions.
Data Scientist
Data Scientist
Individuals who wish to become Data Scientists may find the course, *Data Science with Python: Foundations of Machine Learning* helpful. This course will provide Data Scientists with the knowledge and skills necessary to identify and frame various types of machine learning problems. By learning how to train machine learning models using prepared data, Data Scientists can gain hands-on experience in building models without having to spend time on data collection and preparation.
Machine Learning Engineer
Machine Learning Engineer
The course, *Data Science with Python: Foundations of Machine Learning* can assist Machine Learning Engineers in building a foundation of python-based machine learning principles. Through modules on evaluating machine learning models, this course can help Machine Learning Engineers learn to measure and improve model performance.
Data Engineer
Data Engineer
Data Engineers who take the course, *Data Science with Python: Foundations of Machine Learning* can gain an understanding of the machine learning process from model inception to evaluation. This course may be particularly useful for Data Engineers who are looking to make career transitions to Machine Learning Engineering or Data Science.
Data Analyst
Data Analyst
Data Analysts who enroll in the course, *Data Science with Python: Foundations of Machine Learning* will gain insight into the process of applying machine learning to address business problems. This course may be especially useful for Data Analysts seeking to gain practical knowledge in Python-based machine learning to enhance their skillset.
Data Architect
Data Architect
Individuals who wish to become Data Architects may find the course, *Data Science with Python: Foundations of Machine Learning* helpful. This course will provide Data Architects with the knowledge and skills necessary to design and manage data architectures that support machine learning applications. By learning how to evaluate the performance of machine learning models, Data Architects can ensure that their data architectures are able to meet the demands of these applications.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts who take the course, *Data Science with Python: Foundations of Machine Learning* can gain an understanding of the machine learning process from model inception to evaluation. This course may be particularly useful for Operations Research Analysts who are looking to make career transitions to Machine Learning Engineering or Data Science.
Software Engineer
Software Engineer
Individuals who wish to become Software Engineers may find the course, *Data Science with Python: Foundations of Machine Learning* helpful. This course will provide Software Engineers with the knowledge and skills necessary to integrate machine learning models within their software projects.
Product Manager
Product Manager
Product Managers with an interest in machine learning could benefit from taking the course *Data Science with Python: Foundations of Machine Learning*. The course will provide Product Managers with a foundational understanding of the machine learning process, including problem framing, model training, and evaluation. This knowledge can be valuable for Product Managers who want to integrate machine learning into their products and make data-driven decisions.
Quantitative Analyst
Quantitative Analyst
Although not a perfect match, the course *Data Science with Python: Foundations of Machine Learning* may be useful for Quantitative Analysts interested in applying machine learning to financial and risk modeling. This course can provide an introduction to the machine learning process, including problem framing, model training, and evaluation.
Management Consultant
Management Consultant
Although not a perfect match, the course *Data Science with Python: Foundations of Machine Learning* may be useful for Management Consultants interested in gaining an understanding of machine learning applications in business. This course can provide an introduction to the machine learning process, including problem framing, model training, and evaluation.
Business Analyst
Business Analyst
Although not a perfect match, the course *Data Science with Python: Foundations of Machine Learning* may be useful for Business Analysts interested in exploring machine learning. This course can provide an introduction to the machine learning process, including problem framing, model training, and evaluation.
For more career information including salaries, visit:
OpenCourser.com/course/k8kmmw/data
Reading list
We've selected 13 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
Data Science with Python: Foundations of Machine Learning.
An ideal companion to the course as it offers a practical, hands-on approach to machine learning with popular Python libraries.
Focuses on deep learning, which specialized area of machine learning.
Explores machine learning from a probabilistic perspective, offering a solid theoretical foundation.
Offers a comprehensive review of the mathematical concepts underlying machine learning.
A classic textbook in the field, providing a comprehensive overview of machine learning algorithms and techniques.
Explores statistical learning methods with a focus on sparsity, which key concept in machine learning.
Save
Offers a concise introduction to machine learning concepts and algorithms.
Provides a hands-on approach to machine learning, suitable for learners with some programming experience.
This widely acclaimed textbook offers a comprehensive and in-depth exploration of statistical learning methods, including machine learning.
Provides a gentle introduction to statistical learning, using the R programming language.
Offers a practical guide to machine learning using the R programming language.
Presents machine learning concepts with a focus on real-world applications.
Provides a comprehensive overview of data science techniques using Python, including machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/k8kmmw/data
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Time
40 hours
Level
Introductory
Via
Pluralsight
Instructor
Amber Israelsen
Language
English
Good to know
Develops skills and knowledge of the machine learning process to effectively build, train, and evaluate models using Python
Introduces machine learning terminology, concepts, and models
Provides a foundation for further learning in data science
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