Get Familiar with ML basics in a Kaggle Competition

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In diesem angeleitetes Projekt werden Sie:

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
BeginnerAnfänger
CloudKein Download erforderlich
VideoVideo auf geteiltem Bildschirm
Comment DotsEnglisch
LaptopNur Desktop

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.

Kompetenzen, die Sie erwerben werden

Python ProgrammingMachine Learning (ML) AlgorithmsPredictive ModellingKaggle

Schritt für Schritt lernen

In einem Video, das auf einer Hälfte Ihres Arbeitsbereichs abgespielt wird, führt Sie Ihr Dozent durch diese Schritte:

  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

Ablauf angeleiteter Projekte

Ihr Arbeitsbereich ist ein Cloud-Desktop direkt in Ihrem Browser, kein Download erforderlich

Ihr Dozent leitet Sie in einem Video mit geteiltem Bildschirm Schritt für Schritt an.

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