Get Familiar with ML basics in a Kaggle Competition

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在此指导项目中,您将:

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.

2 hours
初级
无需下载
分屏视频
英语(English)
仅限桌面

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.

您要培养的技能

  • Python Programming

  • Machine Learning (ML) Algorithms

  • Predictive Modelling

  • Kaggle

分步进行学习

在与您的工作区一起在分屏中播放的视频中,您的授课教师将指导您完成每个步骤:

  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

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在分屏视频中,您的授课教师会为您提供分步指导

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