Introduction to Machine Learning: Supervised Learning from Coursera

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

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.

Traffic lights

Read about what's good

what should give you pause

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

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

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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

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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

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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

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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

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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.

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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

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Read An Introduction to Statistical Learning

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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:

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.

See salaries and explore the career path for Machine Learning Engineer

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Data Analyst

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.

See salaries and explore the career path for Statistician

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.

See salaries and explore the career path for Financial 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.

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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.

See salaries and explore the career path for Operations Research Analyst

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.

See salaries and explore the career path for Software Engineer

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.

See salaries and explore the career path for Computer Scientist

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.

See salaries and explore the career path for Data Engineer

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.

See salaries and explore the career path for Quantitative 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.

See salaries and explore the career path for Risk Analyst

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.

See salaries and explore the career path for Market 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.

See salaries and explore the career path for UX 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.

See salaries and explore the career path for Medical Researcher