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[Tensorflow2] Complete conquest of Python machine learning - Marathon record prediction project

This is a comprehensive machine learning project course that learns various useful machine learning regression and classification projects along with theory using Python and TensorFlow 2 based on Boston Marathon big data.

(4.1) 25 reviews

380 learners

  • nomad
Tensorflow
Machine Learning(ML)
Keras
Computer Vision(CV)

Reviews from Early Learners

What you will learn!

  • Build machine learning models and programs

  • Problem Solving with TensorFlow

  • Predicting machine learning classification results

  • Predicting machine learning regression results

  • Understanding artificial intelligence, machine learning, and deep learning

  • Data processing for machine learning and deep learning

  • Data processing and analysis with Python Pandas

  • Data Analysis Using Python

[TensorFlow 2] Complete Python Machine Learning - Marathon Record Prediction Project

Learn both the concepts and practical techniques of machine learning using Python and TensorFlow2.
We will hone your skills by working together on five different projects based on core topics.

Boston Marathon Big Data with Python and TensorFlow

Along with the basic concepts of machine learning, the core topics of regression and classification are covered.

Develop concepts and practical application skills while learning five projects together.

This is a fun and useful machine learning project course.

Project 1. Basics of Linear Regression
: Predicting the remaining marathon time

Learn the concept of linear regression, the foundation of machine learning.

Learn the fundamentals of linear regression and use Python TensorFlow to analyze and predict Boston Marathon data using machine learning.

Using big data from approximately 80,000 Boston Marathon events, we select a desired runner and machine learning learns their times up to 30km. We then use linear regression to predict the remaining 35, 40, and 42.195km times and compare them with actual data. We learn the concepts and techniques for solving linear regression problems using TensorFlow.

Project 2. Multi-Variable Regression
: Marathon completion record prediction

Understand multi-variable regression problems and learn how to solve them.

Learn the basics and solutions to multi-variable regression problems, then predict your and your friends' Boston Marathon completion times by inputting gender, age, and pace data.

Check out the results of machine learning, which uses multi-variable regression techniques to predict completion times based on gender, age, and pace values, and then trained on approximately 80,000 Boston Marathon big data sets. Even without running the marathon, machine learning predicts times based on data learned and analyzed.

Project 3. Multi-Variable, Output Regression
: Marathon section record prediction

Understand multi-variable, output regression problems and learn how to solve them.

In this lesson, you'll learn the basics and solutions of regression problems, which take multiple variables as input and produce multiple outputs. By inputting gender, age, and pace data, you'll predict not only the Boston Marathon completion time but also the predicted times for the 10, 20, and 30 km sections.

Using the Multi Variable Input and Multi Output Regression technique, the machine learning that receives gender, age, and pace values and learns from about 80,000 Boston Marathon big data predicts not only the completion record but also the records for each 10, 20, and 30 km section.

Project 4. Binary Logistic Classification
: Check if you qualify for the marathon

Understand the concept of Binary Classification, the basis of Logistic Regression/Classification, and learn how to solve it.

Before applying for a marathon, enter your gender, age, and pace data to see if you qualify. Based on your past Boston Marathon performance, we'll predict your qualification.

Using the binary classification technique, a fundamental part of logistic regression, we will predict whether a participant will qualify for the marathon. We will also learn how to add qualifying information to the existing Boston Marathon data set of approximately 80,000 entries, utilizing advanced Python Pandas techniques.

Project 5. Multinomial Logistic Classification
: Marathon record class prediction

Understand the concept of Multinomial Classification in Logistic Regression/Classification and learn how to solve it.

Before entering a marathon, enter your gender, age, and pace data to check your predicted time class. Based on past Boston Marathon times, we'll predict your time class.

Using the Multinomial Classification technique of Logistic Regression, we divide your marathon completion record into three grades: 'Outstanding (>25%)', 'Average (25~75%)', and 'Below (<75%)', and predict your expected grade.

Special lecture

'Model accuracy over 99%  We have added a special lecture titled 'Raising the bar'. This lecture is titled ' [ Raspberry Pi ] IoT Deep Learning Computer Vision Practice'.  The project began with a question from students in the MNIST handwriting model: "Why can't the MNIST handwriting model say '7' is '7'?" While the model's accuracy is a factor, as are the program's exception handling and the raw MNIST data, the existing Nueral Network model was too simple for training purposes, so I reconfigured it to increase its accuracy to 99.38%.

 

Please look forward to future lectures on deep learning, IoT, and other topics utilizing machine learning.
The materials and program sources used in the lecture can be found on the website Creapple (www.creapple.com), a knowledge learning platform I run.

Taking a course on Python fundamentals and data visualization and analysis will be of great help in carrying out your project.

Python 100-Minute Core Course
The core and fundamentals of Python
Once you master the skills, it will be a great help in other courses.
Python Data Visualization Analysis Practical Project

Machine learning using Python's Pandas, Matplotlib, and Seaborn,
It can be used in various projects such as deep learning.
Learn data visualization and analysis techniques all at once.

Recommended for
these people

Who is this course right for?

  • Those who want to use artificial intelligence in practice

  • Those who want to develop basic knowledge for deep learning

  • Anyone who wants to learn data science

  • For those who want to use TensorFlow directly

  • Those who want to develop machine learning concepts and practical skills at the same time

  • Anyone working on a data analysis project

  • For those preparing for machine learning/deep learning projects

Need to know before starting?

  • Python Data Processing, Visualization - Python Data Visualization Analysis Practical Project

  • Python Basics - Python 100-minute core lecture

  • Willingness to study hard

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Courses

"노마드크리에이터: 당신의 꿈, 우리의 여정"

대한민국과 NVIDIA가 인정한 딥테크, 싱가포르가 선택한 핀테크 스타트업, 글로벌 무대에서 당신의 가능성을 실현합니다.

노마드크리에이터는 개인의 성장을 넘어, 스타트업으로 도약하며 전 세계를 무대로 전문적인 IT 강의를 제공하고 있습니다.
2019년, 싱가포르 정부의 Entrepass Innovator 프로그램을 통해 시작된 우리의 여정은 곧 혁신적인 스타트업의 이야기로 확장되었습니다.
2020년에는 대한민국에서 인공지능 핀테크 솔루션을 개발하며 딥테크 분야의 선두주자로 자리매김했고, NVIDIA 협업 프로그램 최우수 프로젝트ASUS Global Startup Challenge Award를 포함한 다양한 글로벌 인정과 성과를 이뤘습니다.
2023년에는 NVIDIA의 지원으로 미국에 회사를 설립하며 글로벌 시장을 향한 도약을 시작했습니다.

image.png

경험을 넘어, 비전을 공유합니다.

스타트업 창업 이전, LG CNS와 티머니 등에서 25년간 System Engineer, Project Manager, IT Consultant로서 글로벌 프로젝트를 이끌며 실전 경험과 전문성을 쌓았습니다.
PMP, SAP BW, SCJP, MCSE+DBA, OCP-DBA와 같은 전문 자격을 기반으로, 프로그램 개발, 프로젝트 관리, IT 솔루션 설계 등 다양한 분야에서 성공적인 도전을 이어왔습니다.

이제, 노마드크리에이터는 이러한 경험과 노하우를 집약하여 누구나 쉽고 재미있게 배울 수 있는 교육 콘텐츠를 제공합니다. 실무 중심의 강의부터 최신 기술 트렌드를 반영한 전문 과정까지, 개인의 성장을 위한 맞춤형 학습을 제안합니다.

우리의 미션: "꿈을 현실로, 도전을 기회로"

기술과 교육의 융합으로 더 많은 사람들이 자신만의 가능성을 실현하도록 돕습니다.

노마드크리에이터와 함께라면, 당신의 꿈은 더 이상 멀리 있지 않습니다.

지금 이 순간에도 누군가는 새로운 것을 배우고, 더 나은 자신이 되기 위해 노력하고 있습니다.

하지만 정보의 홍수 속에서 필요한 지식을 찾는 데 소중한 시간을 잃는 일이 얼마나 많습니까?

노마드크리에이터는 이 문제를 해결하고자 합니다.

우리는 지식을 창의적으로 엮어내어, 시간을 아끼고, 가치를 극대화하는 경험을 제공합니다. 우리의 목표는 단순한 정보 전달을 넘어, 지식을 작품처럼 아름답게 전달하는 것입니다.

노마드크리에이터와 함께라면, 당신의 배움은 더 쉽고, 빠르며, 가치 있는 결과를 만들어낼 것입니다.

"배움의 여정에 가치를 더하다, 노마드크리에이터."

이것이 우리가 꿈꾸는 미래입니다.

Curriculum

All

42 lectures ∙ (8hr 51min)

Published: 
Last updated: 

Reviews

All

25 reviews

4.1

25 reviews

  • pong8503011703님의 프로필 이미지
    pong8503011703

    Reviews 5

    Average Rating 4.4

    3

    24% enrolled

    選手講座を聞かないと理解をするのがとても力強いです。 Keras講座を申請し、以前講義を選ばなければ理解する部分が多く、機械学習完全征服講座を申請しましたが.... また、以前講義を申請しなければならないことが発生して勉強の効率性が非常に低下しました。 選手講義の受講がオプションではなく義務にならなければ正確にアウトプットを伴う講座になりそうです

    • nomad
      Instructor

      こんにちは?良いコメントありがとうございます。おっしゃった部分は講師で多くの悩みになる部分です。以前にAll in One式の講義を作った時、読者の方々のレベルが異なり、本人が知っている内容が多いという意見もあり、講義をモジュール化すれば希望する部分だけ受講し、受講生の費用を減らさないかと講義をモジュール化しています。そのため、今後のディープラーニングやTensorflow.js、Tensorflow IOTなどを別途講義でモジュール化する計画です。しかし、こうしてみると、読者様がおっしゃったところで、複数の科目を受講しなければならない不便も悩みます。そこで今、私のクリアフル(www.creapple.com)に定期的に読んで、すべての講義を利用できる機能を作っています。今月中に改編オープン予定ですが、お役に立てば幸いです。ありがとうございます。

    • ノーマドクリエイター講師 回答ありがとうございます。 おっしゃったとおり、定期的な毒の形であれば、より良いアウトプットが伴うようです。 そして講師様講座の個別内容は本当に役に立ち、モチベーションもアップになります。 ただ、現在ではアバンジャースの前編を見ずに裏側を見なければならないそんな気持ちです。

  • bmkingsong7020님의 프로필 이미지
    bmkingsong7020

    Reviews 3

    Average Rating 4.3

    5

    62% enrolled

    サンプルにしてみてよかったです。

    • twotone3654382님의 프로필 이미지
      twotone3654382

      Reviews 24

      Average Rating 4.5

      4

      100% enrolled

      とてもお得な講義でした。

      • chrischina7429님의 프로필 이미지
        chrischina7429

        Reviews 4

        Average Rating 5.0

        5

        50% enrolled

        良い説明ありがとうございます。

        • 4europa1007님의 프로필 이미지
          4europa1007

          Reviews 14

          Average Rating 4.9

          5

          100% enrolled

          ありがとうございます。

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