Get Familiar with ML basics in a Kaggle Competition

4.4
stars
15 ratings
Offered By
In this Guided Project, you will:

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
Beginner
No download needed
Split-screen video
English
Desktop only

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.

Skills you will develop

  • Python Programming

  • Machine Learning (ML) Algorithms

  • Predictive Modelling

  • Kaggle

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  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

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

Reviews

TOP REVIEWS FROM GET FAMILIAR WITH ML BASICS IN A KAGGLE COMPETITION

View all reviews

Frequently asked questions

By purchasing a Guided Project, you'll get everything you need to complete the Guided Project including access to a cloud desktop workspace through your web browser that contains the files and software you need to get started, plus step-by-step video instruction from a subject matter expert.

Because your workspace contains a cloud desktop that is sized for a laptop or desktop computer, Guided Projects are not available on your mobile device.

Guided Project instructors are subject matter experts who have experience in the skill, tool or domain of their project and are passionate about sharing their knowledge to impact millions of learners around the world.

You can download and keep any of your created files from the Guided Project. To do so, you can use the “File Browser” feature while you are accessing your cloud desktop.

Guided Projects are not eligible for refunds. See our full refund policy.

Financial aid is not available for Guided Projects.

Auditing is not available for Guided Projects.

At the top of the page, you can press on the experience level for this Guided Project to view any knowledge prerequisites. For every level of Guided Project, your instructor will walk you through step-by-step.

Yes, everything you need to complete your Guided Project will be available in a cloud desktop that is available in your browser.

You'll learn by doing through completing tasks in a split-screen environment directly in your browser. On the left side of the screen, you'll complete the task in your workspace. On the right side of the screen, you'll watch an instructor walk you through the project, step-by-step.

More questions? Visit the Learner Help Center.