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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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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'

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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

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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

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Watching video tutorials can help you learn Python and reinforce the concepts covered in the course

Browse courses on Python

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Find Python tutorials on YouTube
Follow along with the tutorials
Take notes on the concepts you learn

Organize Notes and Assignments

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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

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Review lecture slides and organize them by topic
Review old assignments and quizzes
Create a study guide or outline

Five other activities

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Join a Study Group

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Joining a study group can help you learn from others, clarify concepts, and prepare for exams

Browse courses on Regression

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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

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Regular practice with Python will help you get more comfortable with coding in the language

Browse courses on Python

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Find online coding exercises
Work on practice problems from the course website
Build small Python projects

Attend a Python Workshop

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Attending a Python workshop can help you learn about new Python libraries and techniques

Browse courses on Python

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Find a Python workshop in your area
Register for the workshop
Attend the workshop and take notes

Build a Machine Learning Project

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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

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Choose a project idea
Gather data
Build a model
Evaluate your model

Find a Mentor in the Field

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Connecting with a mentor who has experience in the field will help you gain insights and guidance

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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

We've selected 50 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 Essentials.

Fluent Python

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Serves as the ultimate masterclass in idiomatic Python, guiding developers through data models, generators, metaprogramming, and concurrent concurrency patterns.

Machine Learning Essentials

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What's inside

Syllabus

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Reviews summary

Foundational machine learning with practical python

Activities

Career center

Reading list

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