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Machine Learning Essentials

Chris Callison-Burch and Victor Preciado

Use statistical learning techniques like linear regression and classification to solve common machine learning problems. Complete short coding assignments in Python.

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What's inside

Syllabus

In this module, you will learn how to include categorical (discrete) inputs in your linear regression problem, as well as nonlinear effects, such as polynomial and interaction terms. As a companion to this theoretical content, there are two recitation videos that demonstrate how to solve linear regression problems in Python. You will need to use this knowledge to complete a programming project.
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Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Taught by Victor Preciado, who is recognized for their work in machine learning
Taught by Chris Callison-Burch, who is recognized for their work in machine learning
Develops foundational skills in statistical learning, which are core for machine learning
Requires no prior programming knowledge, making it ideal for beginners
Covers basic concepts of linear and logistic regression, which are widely used in practice

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Reviews summary

Foundational machine learning with practical python

According to students, "Machine Learning Essentials" offers a solid foundational understanding of key statistical learning techniques. Learners widely appreciate the clear and concise lectures that effectively break down complex topics like linear regression and classification. While many found the Python coding assignments rewarding and practical for applying concepts, a minority felt the hands-on components could benefit from more guidance or advanced examples. The course provides a strong theoretical grounding, which is a positive for those seeking a deeper understanding, but also a warning for those expecting solely practical, recipe-based learning without a strong math or statistics background. Overall, it is considered a good stepping stone for further study in ML.
Course emphasizes theory, which can be challenging for some.
"The theoretical explanations were robust, perhaps a bit too much for someone just looking for practical application."
"This course is too theoretical. I was looking for more hands-on machine learning, not just statistical concepts."
"The content covers important topics, but it's not a pure 'hands-on' course as some might expect."
"It's a waste of time if you want practical ML. Too much theory, not enough real-world application."
Offers practical coding assignments in Python for concept application.
"The Python assignments were challenging but extremely rewarding, allowing me to apply the concepts immediately."
"The coding exercises in Python were helpful, and a good stepping stone for further study."
"The Python coding assignments were practical and helped reinforce the concepts."
"The Python exercises were well-integrated and applied the concepts effectively."
Provides robust statistical learning theory and core concepts.
"This course provided an incredibly solid foundation in machine learning."
"Excellent introduction to statistical learning! The instructor explains the bias-variance tradeoff beautifully."
"A fantastic course for anyone wanting a solid theoretical grounding in ML. The lectures were incredibly insightful..."
"I gained a very strong theoretical understanding of statistical learning from this course."
Pace can be fast if lacking a strong math or statistics background.
"Sometimes the pace was a bit fast, especially if you're not strong in math or statistics."
"If you don't have a strong math background, you will struggle with the theoretical derivations."
"I wish there was more review on the statistical prerequisites before diving into the advanced topics."
Some found Python assignments challenging without enough guidance.
"I struggled with the Python assignments. Felt like I needed more guidance or supplementary material for the coding aspects."
"The Python programming assignments felt a bit rushed or undersupported. I had to look up a lot of external resources..."
"The Python parts were basic and poorly explained, making it difficult to follow along sometimes."

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 Essentials with these activities:
Read 'Introduction to Statistical Learning'
This book provides a comprehensive overview of statistical learning methods and can help you deepen your understanding of the concepts covered in the course
View An Introduction to Statistical Learning: with... on Amazon
Show steps
  • Purchase the book or borrow it from a library
  • Read the book carefully and take notes
  • Work on the exercises at the end of each chapter
Watch Python Video Tutorials
Watching video tutorials can help you learn Python and reinforce the concepts covered in the course
Browse courses on Python
Show steps
  • Find Python tutorials on YouTube
  • Follow along with the tutorials
  • Take notes on the concepts you learn
Organize Notes and Assignments
Putting your notes and coursework in order will help you gain a better understanding of the materials and be organized when it comes time to study
Browse courses on Linear Regression
Show steps
  • Review lecture slides and organize them by topic
  • Review old assignments and quizzes
  • Create a study guide or outline
Five other activities
Expand to see all activities and additional details
Show all eight activities
Join a Study Group
Joining a study group can help you learn from others, clarify concepts, and prepare for exams
Browse courses on Regression
Show steps
  • Find a study group online or through your university
  • Meet with the group regularly to discuss course material
  • Work on practice problems together
Practice Coding in Python
Regular practice with Python will help you get more comfortable with coding in the language
Browse courses on Python
Show steps
  • Find online coding exercises
  • Work on practice problems from the course website
  • Build small Python projects
Attend a Python Workshop
Attending a Python workshop can help you learn about new Python libraries and techniques
Browse courses on Python
Show steps
  • Find a Python workshop in your area
  • Register for the workshop
  • Attend the workshop and take notes
Build a Machine Learning Project
Building a machine learning project will help you apply the concepts you learn in the course and gain practical experience
Browse courses on Machine Learning
Show steps
  • Choose a project idea
  • Gather data
  • Build a model
  • Evaluate your model
Find a Mentor in the Field
Connecting with a mentor who has experience in the field will help you gain insights and guidance
Show steps
  • Identify potential mentors in your field
  • Reach out to mentors and introduce yourself
  • Set up a meeting to discuss your career goals

Career center

Learners who complete Machine Learning Essentials will develop knowledge and skills that may be useful to these careers:

Reading list

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Course image
Effort
4 weeks of study, 8-12 hours/week
Level
Intermediate
Via
Coursera
Institution
University of Pennsylvania
Instructors
Chris Callison-Burch
Victor Preciado
Language
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Taught by Victor Preciado, who is recognized for their work in machine learning
Taught by Chris Callison-Burch, who is recognized for their work in machine learning
Develops foundational skills in statistical learning, which are core for machine learning
Requires no prior programming knowledge, making it ideal for beginners
Covers basic concepts of linear and logistic regression, which are widely used in practice
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