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Geena Kim
In this course, you’ll be learning various supervised ML algorithms and prediction tasks applied to different data. You’ll learn when to use which model and why, and how to improve the model performances. We will cover models such as linear and logistic regression, KNN, Decision trees and ensembling methods such as Random Forest and Boosting, kernel methods such as SVM.
Read more
In this course, you’ll be learning various supervised ML algorithms and prediction tasks applied to different data. You’ll learn when to use which model and why, and how to improve the model performances. We will cover models such as linear and logistic regression, KNN, Decision trees and ensembling methods such as Random Forest and Boosting, kernel methods such as SVM.
Read more
In this course, you’ll be learning various supervised ML algorithms and prediction tasks applied to different data. You’ll learn when to use which model and why, and how to improve the model performances. We will cover models such as linear and logistic regression, KNN, Decision trees and ensembling methods such as Random Forest and Boosting, kernel methods such as SVM.
Prior coding or scripting knowledge is required. We will be utilizing Python extensively throughout the course. In this course, you will need to have a solid foundation in Python or sufficient previous experience coding with other programming languages to pick up Python quickly.
We will be learning how to use data science libraries like NumPy, pandas, matplotlib, statsmodels, and sklearn. The course is designed for programmers beginning to work with those libraries. Prior experience with those libraries would be helpful but not necessary.
College-level math skills, including Calculus and Linear Algebra, are required. Our hope for this course is that the math will be understandable but not intimidating.
This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more:
MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder
MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
Register for this course and see more details by visiting:
OpenCourser.com/course/rm2jl8/introduction
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What's inside
Syllabus
Introduction to Machine Learning, Linear Regression
This week, we will build our supervised machine learning foundation. Data cleaning and EDA might not seem glamorous, but the process is vital for guiding your real-world data projects. The chances are that you have heard of linear regression before. With the buzz around machine learning, perhaps it seems surprising that we are starting with such a standard statistical technique. In "How Not to Be Wrong: The Power of Mathematical Thinking", Jordan Ellenberg refers to linear regression as "the statistical technique that is to social science as the screwdriver is to home repair. It's the one tool you're pretty much going to use, whatever the task" (51). Linear regression is an excellent starting place for solving problems with a continuous outcome. Hopefully, this week will help you appreciate how much you can accomplish with a simple model like this.
Read more
Syllabus
Introduction to Machine Learning, Linear Regression
This week, we will build our supervised machine learning foundation. Data cleaning and EDA might not seem glamorous, but the process is vital for guiding your real-world data projects. The chances are that you have heard of linear regression before. With the buzz around machine learning, perhaps it seems surprising that we are starting with such a standard statistical technique. In "How Not to Be Wrong: The Power of Mathematical Thinking", Jordan Ellenberg refers to linear regression as "the statistical technique that is to social science as the screwdriver is to home repair. It's the one tool you're pretty much going to use, whatever the task" (51). Linear regression is an excellent starting place for solving problems with a continuous outcome. Hopefully, this week will help you appreciate how much you can accomplish with a simple model like this.
Read more
Multilinear Regression
This week we are building on last week's foundation and working with more complex linear regression models. After this week, you will be able to create linear models with several explanatory and categorical variables. Mathematically and syntactically, multiple linear regression models are a natural extension of the simpler linear regression models we learned last week. One of the differences that we must keep in mind this week is that our data space is now 3D instead of 2D. The difference between 3D and 2D has implications when considering how to do things like creating meaningful visualizations. It is essential to understand how to interpret coefficients. Machine learning involves strategically iterating and improving upon a model. In this week's lab and Peer Review, you will identify weaknesses with linear regression models and strategically improve on them. Hopefully, as you progress through this course specialization, you will get better and better at this iterative process.
Logistic Regression
Even though the name logistic regression might suggest otherwise, we will be shifting our attention from regression tasks to classification tasks this week. Logistic regression is a particular case of a generalized linear model. Like linear regression, logistic regression is a widely used statistical tool and one of the foundational tools for your data science toolkit. There are many real-world applications for classification tasks, including the financial and biomedical realms. In this week's lab, you will see how this classic algorithm will help you predict whether a biopsy slide from the famous Wisconsin Breast Cancer dataset shows a benign or malignant mass. We also advise starting the final project that you will turn in Week 7 of the course this week. This week, find a project dataset, start performing EDA and define your problem. Use the project rubric as a guide, and don't be afraid to look at a few datasets until you find one well-suited to the project.
Non-parametric Models
This week we will learn about non-parametric models. k-Nearest Neighbors makes sense on an intuitive level. Decision trees are a supervised learning model that can be used for either regression or classification tasks. In Module 2, we learned about the bias-variance tradeoff, and we've kept that tradeoff in mind as we've moved through the course. Highly flexible tree models have the benefit that they can capture complex, non-linear relationships. However, they are prone to overfitting. This week and next, we will explore strategies like pruning to avoid overfitting with tree-based models. In this week's lab, you will make a KNN classifier for the famous MNIST dataset and then build a spam classifier using a decision tree model. This week we will once again appreciate the power of simple, understandable models. Keep going with your final project. Once you've finalized your dataset and EDA, start on the initial approach for your main supervised learning task. Review the course material, read research papers, look at GitHub repositories and Medium articles to understand your topic and plan your approach.
Ensemble Methods
Last week, we learned about tree models. Despite all of the benefits of tree models, they had some weaknesses that were difficult to overcome. This week we will learn about ensembling methods to overcome tree models' tendency to overfit. The winner utilizes an ensemble approach in many machine learning competitions, aggregating predictions from multiple tree models. This week you will start by learning about random forests and bagging, a technique that involves training the same algorithm with different subset samples of the training data. Then you will learn about boosting, an ensemble method where models train sequentially. You will learn about two essential boosting algorithms: AdaBoost and Gradient Boosting. This week, work on the main analysis of your final project. Iterate and improve on your models. Compare different models. Perform hyperparameter optimization. Sometimes this part of a machine learning project can feel tedious, but hopefully, it will be rewarding to see your performance improve.
Kernel Method
This week we will be exploring another advanced topic, Support Vector Machines. Don't let the name intimidate you. This week, we will work through understanding this powerful supervised learning model. Hopefully, you will build an intuitive understanding of essential concepts like the difference between hard and soft margins, the kernel trick, and hyperparameter tuning. Next week, you will submit the three deliverables for your final project: the report, video presentation, and a link to your GitHub repository. Suppose you aim to finish iterating on your models, hyperparameter optimization, etc., this week. In that case, next week, you can polish your report, make sure your GitHub repository is ready for Peer Review, and give an excellent presentation of your work.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops concepts such as overfitting and the bias-variance tradeoff
Covers practical topics like spam classification and building a KNN classifier
Builds a foundation for more advanced methods like ensembling and kernel methods
Requires prior coding and scripting knowledge
Assumes college-level math skills, including Calculus and Linear Algebra
Save this course
Save Introduction to Machine Learning: Supervised Learning to your list so you can find it easily later:
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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 Introduction to Machine Learning: Supervised Learning with these
activities:
Organize and Review Course Materials
Show steps
Enhance your learning process by organizing and reviewing notes, assignments, quizzes, and exams to reinforce your understanding of the course content.
Browse courses on
Organization
Show steps
-
Gather all relevant course materials, including notes, slides, and assignments.
-
Organize the materials into logical sections or topics.
-
Review the materials regularly, focusing on key concepts and ideas.
-
Identify areas where you need further clarification or support.
Follow Hands-on Tutorials on Machine Learning with Scikit-Learn
Show steps
Enhance your practical skills by working through guided tutorials that provide step-by-step instructions for implementing machine learning algorithms using Scikit-Learn.
Browse courses on
Machine Learning
Show steps
-
Identify relevant tutorials that align with the course topics.
-
Set up your programming environment and install the necessary tools.
-
Follow the tutorial instructions to implement different machine learning algorithms.
-
Run the code, analyze the results, and troubleshoot any issues.
Attend and Participate in Study Groups
Show steps
Foster deeper understanding and improve your learning outcomes by actively participating in study groups where you can discuss course material, share insights, and collaborate with peers.
Show steps
-
Identify or create a study group with classmates who share similar interests.
-
Set regular meeting times and establish a consistent schedule.
-
Prepare for each session by reviewing the assigned materials.
-
Actively participate in group discussions, asking and answering questions.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Read An Introduction to Statistical Learning
Show steps
Supplement the course material by reading this textbook to gain a wider perspective and more comprehensive understanding of the principles, methods, and algorithms used in machine learning.
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
-
Review the table of contents and identify chapters relevant to course topics.
-
Read the assigned chapters and take notes on the main concepts and techniques.
-
Complete the exercises or practice problems at the end of each chapter to reinforce your understanding.
Solve Practice Problems on Machine Learning Algorithms
Show steps
Test and strengthen your understanding of machine learning concepts by solving practice problems that cover a variety of supervised and unsupervised learning algorithms.
Browse courses on
Machine Learning
Show steps
-
Find practice problems from online resources or textbooks.
-
Attempt to solve the problems on your own, using the knowledge gained in the course.
-
Check your solutions against provided answers or consult with classmates or instructors for guidance.
Seek Guidance and Support from Experienced Professionals
Show steps
Enhance your learning experience and broaden your perspectives by seeking guidance and support from experienced professionals in the field of machine learning.
Browse courses on
Mentorship
Show steps
-
Identify potential mentors through industry events, online platforms, or personal connections.
-
Reach out to potential mentors and express your interest in learning from their experiences.
-
Establish a mutually beneficial relationship, setting clear expectations and goals.
-
Regularly engage with your mentor, asking questions, seeking advice, and sharing your progress.
Create a Mini Project on a Machine Learning Application
Show steps
Apply your knowledge and skills by creating a mini project that showcases your ability to solve a real-world problem using machine learning techniques.
Browse courses on
Machine Learning
Show steps
-
Identify a problem that can be addressed using machine learning.
-
Gather and prepare the necessary data.
-
Select appropriate machine learning algorithms and implement them.
-
Evaluate the performance of your model and make necessary adjustments.
-
Present your project, highlighting your findings and insights.
Participate in Machine Learning Competitions
Show steps
Challenge yourself and showcase your skills by participating in machine learning competitions that provide a platform to apply your knowledge, collaborate with others, and win recognition for your achievements.
Browse courses on
Machine Learning
Show steps
-
Identify relevant competitions aligned with your interests and skill level.
-
Form a team or work individually to develop and submit your solutions.
-
Analyze the competition results and learn from the approaches and techniques used by top performers.
-
Share your experiences and insights with the community, contributing to the collective knowledge base.
Organize and Review Course Materials
Show steps
Enhance your learning process by organizing and reviewing notes, assignments, quizzes, and exams to reinforce your understanding of the course content.
Browse courses on
Organization
Show steps
Show steps
Show steps
- Gather all relevant course materials, including notes, slides, and assignments.
- Organize the materials into logical sections or topics.
- Review the materials regularly, focusing on key concepts and ideas.
- Identify areas where you need further clarification or support.
Follow Hands-on Tutorials on Machine Learning with Scikit-Learn
Show steps
Enhance your practical skills by working through guided tutorials that provide step-by-step instructions for implementing machine learning algorithms using Scikit-Learn.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify relevant tutorials that align with the course topics.
- Set up your programming environment and install the necessary tools.
- Follow the tutorial instructions to implement different machine learning algorithms.
- Run the code, analyze the results, and troubleshoot any issues.
Attend and Participate in Study Groups
Show steps
Foster deeper understanding and improve your learning outcomes by actively participating in study groups where you can discuss course material, share insights, and collaborate with peers.
Show steps
Show steps
Show steps
- Identify or create a study group with classmates who share similar interests.
- Set regular meeting times and establish a consistent schedule.
- Prepare for each session by reviewing the assigned materials.
- Actively participate in group discussions, asking and answering questions.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Read An Introduction to Statistical Learning
Show steps
Supplement the course material by reading this textbook to gain a wider perspective and more comprehensive understanding of the principles, methods, and algorithms used in machine learning.
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
Show steps
Show steps
- Review the table of contents and identify chapters relevant to course topics.
- Read the assigned chapters and take notes on the main concepts and techniques.
- Complete the exercises or practice problems at the end of each chapter to reinforce your understanding.
Solve Practice Problems on Machine Learning Algorithms
Show steps
Test and strengthen your understanding of machine learning concepts by solving practice problems that cover a variety of supervised and unsupervised learning algorithms.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find practice problems from online resources or textbooks.
- Attempt to solve the problems on your own, using the knowledge gained in the course.
- Check your solutions against provided answers or consult with classmates or instructors for guidance.
Seek Guidance and Support from Experienced Professionals
Show steps
Enhance your learning experience and broaden your perspectives by seeking guidance and support from experienced professionals in the field of machine learning.
Browse courses on
Mentorship
Show steps
Show steps
Show steps
- Identify potential mentors through industry events, online platforms, or personal connections.
- Reach out to potential mentors and express your interest in learning from their experiences.
- Establish a mutually beneficial relationship, setting clear expectations and goals.
- Regularly engage with your mentor, asking questions, seeking advice, and sharing your progress.
Create a Mini Project on a Machine Learning Application
Show steps
Apply your knowledge and skills by creating a mini project that showcases your ability to solve a real-world problem using machine learning techniques.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify a problem that can be addressed using machine learning.
- Gather and prepare the necessary data.
- Select appropriate machine learning algorithms and implement them.
- Evaluate the performance of your model and make necessary adjustments.
- Present your project, highlighting your findings and insights.
Participate in Machine Learning Competitions
Show steps
Challenge yourself and showcase your skills by participating in machine learning competitions that provide a platform to apply your knowledge, collaborate with others, and win recognition for your achievements.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify relevant competitions aligned with your interests and skill level.
- Form a team or work individually to develop and submit your solutions.
- Analyze the competition results and learn from the approaches and techniques used by top performers.
- Share your experiences and insights with the community, contributing to the collective knowledge base.
Career center
Learners who complete Introduction to Machine Learning: Supervised Learning will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists are responsible for collecting, cleaning, and analyzing data to help businesses make better decisions. As a Data Scientist, you would use your knowledge of supervised learning to build models that can predict outcomes, identify trends, and classify data. This course can help you build a solid foundation in supervised learning, which is a key skill for Data Scientists.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers will be in high demand as industries and companies increase their adoption of ML and AI. Machine Learning Engineers are responsible for researching, designing, and implementing ML models. They also work on deploying and maintaining these models, as well as collecting and analyzing data to improve their performance. With the emphasis on supervised learning in this course, you can develop your skills in building and using models specially designed to work on real-world problems.
Data Analyst
Data Analyst
Data Analysts are responsible for gathering, analyzing, and interpreting data to help businesses understand their customers, improve their operations, and make better decisions. This course on supervised learning can provide a solid foundation for your work as a Data Analyst.
Statistician
Statistician
Statisticians use mathematical and statistical methods to collect, analyze, interpret, and present data. They work in a variety of fields, including healthcare, finance, and market research. In this course, you will learn about supervised learning, a powerful statistical technique that can be used to build models that can predict outcomes.
Financial Analyst
Financial Analyst
Financial Analysts use financial data to make recommendations about investments and financial decisions. This course can provide a strong foundation for a career as a Financial Analyst. You will learn about supervised learning, a technique that can be used to build models that can predict financial outcomes, such as stock prices and interest rates.
Business Analyst
Business Analyst
Business Analysts identify and solve problems within an organization using data analysis. This course can provide a strong foundation for a career as a Business Analyst, as you will learn about supervised learning, a technique that can be used to build models that can predict business outcomes, such as customer churn and sales revenue.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to improve the efficiency and productivity of organizations. This course can provide a strong foundation for a career as an Operations Research Analyst, as you will learn about supervised learning, a technique that can be used to build models that can predict operational outcomes, such as production levels and supply chain performance.
Software Engineer
Software Engineer
This course can provide a strong foundation for a career as a Software Engineer, as you will learn about supervised learning, a technique that is used to build many types of software applications, such as recommendation engines and fraud detection systems.
Data Engineer
Data Engineer
This course can provide a strong foundation for a career as a Data Engineer, as you will learn about supervised learning, a technique that is used to build models that can predict and classify data, which are important tasks for Data Engineers.
Computer Scientist
Computer Scientist
This course can provide a strong foundation for a career as a Computer Scientist, as you will learn about supervised learning, an important subfield of computer science.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. This course can provide a strong foundation for a career as a Quantitative Analyst, as you will learn about supervised learning, a technique that is used to build models that can predict financial outcomes, such as stock prices and interest rates.
UX Researcher
UX Researcher
UX Researchers study how users interact with products and services. This course can provide a strong foundation for a career as a UX Researcher, as you will learn about supervised learning, a technique that is used to build models that can predict user behavior and preferences.
Risk Analyst
Risk Analyst
Risk Analysts identify and assess risks for organizations. This course can provide a strong foundation for a career as a Risk Analyst, as you will learn about supervised learning, a technique that is used to build models that can predict the likelihood and impact of risks.
Market Researcher
Market Researcher
Market Researchers conduct research to understand consumer behavior and trends. This course can provide a strong foundation for a career as a Market Researcher, as you will learn about supervised learning, a technique that is used to build models that can predict consumer behavior and preferences.
Medical Researcher
Medical Researcher
Medical Researchers conduct research to understand and treat diseases. This course may be helpful for a Medical Researcher who uses supervised learning to build models that can predict disease outcomes and identify potential treatments.
For more career information including salaries, visit:
OpenCourser.com/course/rm2jl8/introduction
Reading list
We've selected 12 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
Introduction to Machine Learning: Supervised Learning.
Provides a comprehensive overview of machine learning algorithms and techniques, making it a valuable resource for students and practitioners alike. It covers a wide range of topics, including supervised and unsupervised learning, feature selection, and model evaluation.
Provides a hands-on introduction to machine learning, using Python and the popular scikit-learn, Keras, and TensorFlow libraries. It covers a wide range of topics, including supervised and unsupervised learning, feature engineering, and model evaluation.
Save
Provides a comprehensive overview of deep learning, a subfield of machine learning that has revolutionized many industries. It covers a wide range of topics, including convolutional neural networks, recurrent neural networks, and generative adversarial networks.
Provides a probabilistic perspective on machine learning, which is essential for understanding the underlying mathematical concepts. It covers a wide range of topics, including Bayesian inference, graphical models, and reinforcement learning.
Provides a comprehensive overview of statistical learning, which is closely related to machine learning. It covers a wide range of topics, including linear regression, logistic regression, and decision trees.
Provides a comprehensive overview of pattern recognition and machine learning, with a focus on the mathematical foundations. It covers a wide range of topics, including supervised and unsupervised learning, feature selection, and model evaluation.
Provides a practical introduction to machine learning, with a focus on the Python programming language. It covers a wide range of topics, including supervised and unsupervised learning, feature engineering, and model evaluation.
Provides a practical introduction to machine learning, with a focus on the R programming language. It covers a wide range of topics, including supervised and unsupervised learning, feature engineering, and model evaluation.
Provides a practical introduction to machine learning, with a focus on the Python programming language. It covers a wide range of topics, including supervised and unsupervised learning, feature engineering, and model evaluation.
Save
Provides a comprehensive overview of machine learning, with a focus on the underlying algorithms. It covers a wide range of topics, including supervised and unsupervised learning, feature selection, and model evaluation.
Provides a concise introduction to machine learning, with a focus on the underlying concepts. It covers a wide range of topics, including supervised and unsupervised learning, feature selection, and model evaluation.
Provides a comprehensive overview of machine learning, with a focus on the underlying concepts. It covers a wide range of topics, including supervised and unsupervised learning, feature selection, and model evaluation.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/rm2jl8/introduction
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Effort
6 weeks of study
Level
Intermediate
Via
Coursera
Institution
University of Colorado Boulder
Instructor
Geena Kim
Language
English
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This course belongs to a
collection
called
Machine Learning: Theory and Hands-on Practice with Python. It's comprised of two courses:
- Introduction to Machine Learning: Supervised Learning (viewing)
- Unsupervised Algorithms in Machine Learning
Good to know
Develops concepts such as overfitting and the bias-variance tradeoff
Covers practical topics like spam classification and building a KNN classifier
Builds a foundation for more advanced methods like ensembling and kernel methods
Requires prior coding and scripting knowledge
Assumes college-level math skills, including Calculus and Linear Algebra
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