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Computer Science • Series

Introduction to Machine Learning

Next session on Sep 7, 2024

Piyush A

Series Details

Sessions

Public Discussion

Series Details

About

[More classes will be added to the series soon!]
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This series will provide a practical introduction to machine learning. Topics include regression (linear & polynomial), regularization, classification, clustering, retrieval, recommender systems, and deep learning, with a focus on an intuitive understanding grounded in real-world applications. Intelligent applications are designed and used to make predictions on large, complex datasets. 
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Prerequisites include a basic understanding of Python syntax and some statistical knowledge. Even if you don't meet these requirements, I will try my best to make it easier to understand.
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I will provide datasets and practice problems via GitHub, and I expect everyone to attend Office Hours (optional) if they have any questions. Machine learning is a hard course, but focusing on understanding the concepts will make it easier.

✋ ATTENDANCE POLICY

Attendance is not tracked, but do note that the lectures are the best time to understand concepts and ask me questions so it is sort a requirement to come.

Dates

July 4 - September 8

Learners

24 / 30

Total Sessions

About the Tutor

Hey 👋🏽, I'm Piyush Acharya! As a rising junior, some of my achievements include winning the U.S. Department of Energy National Science Bowl Finals in Washington D.C., two-time winner of the U.S. House of Representatives Congressional App Challenge, USACO Silver, and placing 2nd in the 2024 Science Olympiad Robot Tour event. I received a 5 on AP Chemistry, AP Environmental Science, AP World History, AP Computer Science A, and AP Computer Science Principles. In my spare time, you can find me exploring the latest in technology, running cross country, or flying my DJI Mavic 3. I look forward to helping you all achieve academic success!

View Piyush A's Profile

Upcoming Sessions

Sep

Session 33

Artificial Intelligence

Live now!
Today, 11:00 PM

Classification

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