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Deep Learning & Machine Learning

Machine Learning, Just the Essentials, Fast!

This is a lecture covering machine learning theory and practical examples based on <Machine Learning, Just the Essentials!> (Insight, 2025).

(4.5) 4 reviews

108 learners

  • haesunpark
머신러닝
딥러닝
scikit-learn
keras
pytorch
Artificial Neural Network
CNN
linear-regression
RNN
ensembles

Reviews from Early Learners

What you will learn!

  • Mathematical Foundations of Machine Learning

  • Linear models, trees, support vector machines, neural networks

  • Ensemble learning such as boosting, bagging, random forest, and gradient boosting

  • Gradient descent, feature engineering, underfitting/overfitting, regularization, evaluation, tuning

  • Unsupervised learning such as clustering and dimensionality reduction

Book Introduction

We've removed the complex theories and included only the essential core content!

The Most Concise Guide to Learning Machine Learning

This book is a bestseller translated into 11 languages worldwide and used as a textbook in thousands of universities, explaining machine learning concisely and clearly. It systematically covers everything from basic mathematical concepts to core algorithms, deep learning, and neural networks, providing complete tools for solving modern machine learning problems including clustering, topic modeling, metric learning, and recommendation systems. By connecting theory with practical implementation, focusing on essential skills for real-world applications, anyone can learn quickly and effectively.

The author explains techniques that can be directly applied to real projects, including feature engineering, normalization, imbalanced dataset handling, ensemble methods, and model evaluation, based on rich practical experience. Composed of intuitive explanations and examples without being constrained by complex formulas, it is useful for everyone from beginners who want to solidify their foundations to practitioners looking to expand their practical capabilities.

Reviews

"It's remarkable that such a diverse range of topics has been packed into just over 100 pages. Most thin books omit the mathematics, but this book doesn't skip it. I also really liked the way it clearly explains core concepts in short sentences. This book is useful for beginners and also helpful for experienced practitioners who need a broad perspective."
- Aurélien Géron (Senior AI Engineer, Author of "Hands-On Machine Learning")

"Andri attempted the impossible task of summarizing all of machine learning in about 100 pages, and brilliantly selected theoretical and practical content that is useful for practitioners. This book will provide an excellent foundation as an introductory guide for readers encountering machine learning for the first time."
- Peter Norvig (Director of Research at Google, co-author of the world-renowned AI textbook AIMA)

Book Purchase

Recommended for
these people

Who is this course right for?

  • Those who want to study along with the book <Machine Learning, Just the Essentials Quickly!>

  • Those who want to build a solid theoretical foundation after learning from hands-on machine learning introductory books

  • Those who want to work through practical examples along with theory

  • Those who want to learn about machine learning/deep learning libraries such as scikit-learn, Keras, and PyTorch

Need to know before starting?

  • Python

Hello
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21,662

Learners

285

Reviews

110

Answers

4.9

Rating

10

Courses

기계공학을 전공했지만 졸업 후엔 줄곧 코드를 읽고 쓰는 일을 했습니다. Google AI/Cloud GDE, Microsoft AI MVP입니다. 텐서 플로우 블로그(tensorflow.blog)를 운영하고 있고, 머신러닝과 딥러닝에 관한 책을 집필하고 번역하면서 소프트웨어와 과학의 경계를 흥미롭게 탐험하고 있습니다.

『혼자 만들면서 공부하는 딥러닝』(한빛미디어, 2025), 『혼자 공부하는 머신러닝+딥러닝(개정판)』(한빛미디어, 2025), 『혼자 공부하는 데이터 분석 with 파이썬』(한빛미디어, 2023), 『챗GPT로 대화하는 기술』(한빛미디어, 2023), 『Do it! 딥러닝 입문』(이지스퍼블리싱, 2019)을 집필했습니다.

『밑바닥부터 만들면서 배우는 LLM』(길벗, 2025), 『핸즈온 LLM』(한빛미디어, 2025), 『머신 러닝 Q & AI』(길벗, 2025), 『개발자를 위한 수학』(한빛미디어, 2024), 『실무로 통하는 ML 문제 해결 with 파이썬』(한빛미디어, 2024), 『머신러닝 교과서: 파이토치 편』(길벗, 2023), 『스티븐 울프럼의 챗GPT 강의』(한빛미디어, 2023), 『핸즈온 머신러닝 3판』(한빛미디어, 2023), 『만들면서 배우는 생성 딥러닝 2판』(한빛미디어, 2023), 『코딩 뇌를 깨우는 파이썬』(한빛미디어, 2023), 『트랜스포머를 활용한 자연어 처리』(한빛미디어, 2022), 『케라스 창시자에게 배우는 딥러닝 2판』(길벗, 2022), 『개발자를 위한 머신러닝&딥러닝』(한빛미디어, 2022), 『XGBoost와 사이킷런을 활용한 그레이디언트 부스팅』(한빛미디어, 2022), 『구글 브레인 팀에게 배우는 딥러닝 with TensorFlow.js』(길벗, 2022), 『(개정2판)파이썬 라이브러리를 활용한 머신러닝』(한빛미디어, 2022)을 포함하여 수십여 권의 책을 우리말로 옮겼습니다.

Curriculum

All

7 lectures ∙ (49min)

      Published: 
      Last updated: 

      Reviews

      All

      4 reviews

      4.5

      4 reviews

      • 김용바님의 프로필 이미지
        김용바

        Reviews 2

        Average Rating 5.0

        5

        43% enrolled

        이해하기 쉬워요

        • 박해선
          Instructor

          감사합니다! :)

      • galaxia999님의 프로필 이미지
        galaxia999

        Reviews 5

        Average Rating 5.0

        5

        71% enrolled

        강의 감사합니다.

        • 박해선
          Instructor

          넵! 모쪼록 도움이 되시길 바라겠습니다! :)

      • HuaZ님의 프로필 이미지
        HuaZ

        Reviews 2

        Average Rating 5.0

        5

        60% enrolled

        차분하고 목소리가 듣기 편합니다

        • 박해선
          Instructor

          감사합니다. 앞으로 더 잘 준비하겠습니다. 기대해 주세요! :)

      • ikhwang님의 프로필 이미지
        ikhwang

        Reviews 2

        Average Rating 3.0

        3

        60% enrolled

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