Get Familiar with ML basics in a Kaggle Competition

4.4
별점
14개의 평가
제공자:
Coursera Project Network
학습자는 이 안내 프로젝트에서 다음을 수행하게 됩니다.

How to get familiar with Machine Learning basics and how to start a model prediction using basic supervised Machine Learning models.

Build, train, test and evaluate the performance of some models.

Submit your first solution on the Kaggle platform.

Clock2 hours
Beginner초급
Cloud다운로드 필요 없음
Video분할 화면 동영상
Comment Dots영어
Laptop데스크톱 전용

In this 1-hour long project, you will be able to understand how to predict which passengers survived the Titanic shipwreck and make your first submission in an Machine Learning competition inside the Kaggle platform. Also, you as a beginner in Machine Learning applications, will get familiar and get a deep understanding of how to start a model prediction using basic supervised Machine Learning models. We will choose classifiers to learn, predict, and make an Exploratory Data Analysis (also called EDA). At the end, you will know how to measure a model performance, and submit your model to the competition and get a score from Kaggle. This guided project is for beginners in Data Science who want to do a practical application using Machine Learning. You will get familiar with the methods used in machine learning applications and data analysis. In order to be successful in this project, you should have an account on the Kaggle platform (no cost is necessary). Be familiar with some basic Python programming, we will use numpy and pandas libraries. Some background in Statistics is appreciated, like as knowledge in probability, but it’s not a requirement.

개발할 기술

  • Python Programming
  • Machine Learning (ML) Algorithms
  • Predictive Modelling
  • Kaggle

단계별 학습

작업 영역이 있는 분할 화면으로 재생되는 동영상에서 강사는 다음을 단계별로 안내합니다.

  1. Getting Started with Kaggle

  2. Exploratory Data Analysis (EDA)

  3. Preprocessing I - Taking care of Missing Values

  4. Preprocessing II - Taking care of Missing Values

  5. Preprocessing III - Encoding Categorical Data

  6. Split the Train & Test datasets

  7. Building our Machine Learning Models

  8. Submit your project on Kaggle

안내형 프로젝트 진행 방식

작업 영역은 브라우저에 바로 로드되는 클라우드 데스크톱으로, 다운로드할 필요가 없습니다.

분할 화면 동영상에서 강사가 프로젝트를 단계별로 안내해 줍니다.

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GET FAMILIAR WITH ML BASICS IN A KAGGLE COMPETITION의 최상위 리뷰

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