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Kaggle Competition

Join this online free Kaggle Competition course to get introduced to the Kaggle platform. Learn why it is an excellent choice for the coders to interact and build their expertise in data science by using datasets, and code files.

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Intermediate

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1.5 Hrs

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2.9K+

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About this Free Certificate Course

This course will first introduce you to the basics of Kaggle. Next, you will learn about the Kaggle community and the various courses available to learn. Later, you will get familiar with some important topics such as Kaggle datasets and code files. Moving ahead, you will experience your first Kaggle competition. At the end of the course, you will code on IPL Kaggle data. Once you finish this free course, take the quiz and earn a completion certificate.

Are you ready to learn Kaggle Competition? Look no further! Our professional Data Science course covers every skill you need to become an accomplished expert in the domain.

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Course Outline

Introduction to Kaggle

In the first module of the course, you will be provided with an introduction to Kaggle. Questions such as what Kaggle is, the importance of Kaggle, the different uses of Kaggle, and the benefits of Kaggle are answered.  

Kaggle Community and Courses

This module talks about the Kaggle community and the benefits offered by the community. The community allows the coders to interact with each other and work on their skills as a collective entity. You will also get to know about different courses available that can be used for learning purposes.

Kaggle Datasets and Code Files

This module will guide through the use of Kaggle Datasets, the process of importing datasets already available on Kaggle, the use of code files, the method of downloading such files, and the process of uploading new code files.

First Kaggle Competition

In this module, you will get to know about different competitions held by the Kaggle Platform. You can learn and upgrade your skills by participating in such competitions. By the end of this lecture, you will be able to appreciate the various opportunities provided by Kaggle.

Coding on IPL Kaggle Data

This module will provide you with a demonstration of using Kaggle to code on IPL datasets. With the help of this example, you will be able to appreciate the importance of Kaggle in simplifying the process of developing machine learning models using data sets. You can follow the tutorial for a better understanding.

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Kaggle Competition

1.5 Learning Hours . Intermediate

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Frequently Asked Questions

What are the prerequisites required to learn the free Kaggle Competition course?

No prerequisites are required to learn this free online Kaggle Competition course.

How long does it take to complete the free Kaggle Competition course?

The total duration of the free online Kaggle Competition course is 1 hour. You can learn the course at your convenience since it is self-paced.

Will I have lifetime access to the free Kaggle Competition course?

Yes, Great Learning provides lifetime access to any of its free online courses, provided you have enrolled successfully.

What are my next learning options after the Kaggle Competition course?

After completing this course, you can choose the next available course as per your requirement. You can also choose a professional Data Science program to upskill your career in the domain.

Is it worth learning Kaggle?

Yes, Kaggle is one of the most demanded skills in the field of Data Science and Machine Learning. Around 80-90 percent of Data scientists or Machine Learning Engineers use Kaggle to learn to develop models and machine learning-based algorithms or to analyze their skills by participating in various competitions. It is a vast platform to explore and is a great help for beginners. 

What is Kaggle used for?

Kaggle is used by learners of the field of Data Science and Machine Learning to participate in various competitions, test their skills, and improve them further. Also, various other benefits, such as interaction with the community, will help you to get confident in your skills. You can always discuss your models and algorithms and learn new ways to better them. 

Why is Kaggle so popular?​

The popularity of Kaggle can be gauged from the fact that around 80-90 percent of people invested in the field of Data Science and Machine Learning use Kaggle as their learning platform. You can participate in various competitions and upgrade your skills. Also, other benefits such as support from the community and varied difficulty courses make it popular.

What jobs demand that you learn Kaggle?

Nowadays, Kaggle is in high demand. After learning Kaggle, you can go for positions like: Data Scientist, Machine Learning Engineer, AI engineer, or Data Analyst.

Will I get a certificate after completing this Kaggle Competition course?

Yes, we provide a certificate after completing the Kaggle Competition successfully.

What knowledge and skills will I gain upon completing the free Kaggle Competition course?

The knowledge and skills you will gain upon completing the Kaggle Competition course are: learn to use Kaggle to develop efficient models used for predictions and also write better algorithms.

How much does the Kaggle Competition course cost?

Kaggle Competition course is a free online course.

Is there a limit on how many times I can take the Kaggle Competition course?

No, there is no limit. You can take the Kaggle Competition course as often as you wish until you completely understand it.

Can I sign up for multiple courses from Great Learning Academy at the same time?

Yes, you can opt for multiple courses as per your time suitability.

Why choose Great Learning Academy for this Kaggle Competition course?

The quality of videos of both content coverage and recording quality is very good and clear.

 ● Assignments and quizzes are provided to check the learning progress.

 ● Provides a perfect platform to build a career with a well-designed curriculum, faculty, and supporting management.

 ● The Great Learning team is pragmatic that includes well-qualified mentors having vast experience in the relevant field.

 ● The Great Learning team aims to teach the learners, whether it is technical or non-technical, the concepts from the basics and to the end in a great way.

 ● Learners are provided with friendly hands-on sessions.

 

Who is eligible to take the Kaggle Competition course?

Anyone interested in learning about ML, wanting to develop high-performing Data Science and Machine Learning models, and wanting to learn the ability to write algorithms effortlessly and test their coding abilities can take up the Introduction to Machine Learning course.

What are the steps to enroll in the Kaggle Competition course?

Follow the following steps to enroll in the Kaggle Competition course:

 ● Go to the HTTP://www.mygreatlearning.com website.

 ● In the search on the page, type Kaggle Competition.

 ● Now click on "ENROLL FOR FREE."

 ● Start learning the course after completing the course successfully.

 

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Kaggle Competition

Kaggle, a subsidiary of Google LLC, can be defined as a web community of data scientists, data science enthusiasts, and machine learning practitioners. Kaggle allows users to hunt out and publish data sets, explore and build models during competitions while working with other data scientists and machine learning engineers on a web-based data science environment to unravel data science challenges.

Let’s understand Kaggle a bit about what kind of competition we can have on Kaggle because Kaggle has many competitions. We can participate in any of them according to our interests. 

Common Competition Types

Featured: Featured Kaggle data science competitions are the kind of competitions that Kaggle is perhaps best known for. Featured competitions attract a number of the foremost formidable experts and offer prize pools going as high as 1,000,000 dollars. However, they continue to be accessible to anyone and everybody.

Research: Research competitions are another common sort of competition on Kaggle. Research competitions have more innovative problems than featured competition problems, for instance. Research competitions with more innovative problems usually offer prizes or points thanks to their experimental nature. But they offer an opportunity to hold on to problems statements that don’t have a clean or easy solution and are vital to a designated domain or area through a slightly less competitive environment.

Getting Started: Getting started with competitions is the simplest, most most straightforward way to enter Kaggle competitions. Being a semi-permanent competition, they are employed only by new users just getting their foot in the door within the field of machine learning. They provide no prizes or points. Due to their long-running nature, Getting Started competitions are perhaps the foremost heavily materialized problems in machine learning - just what a newcomer must get started!

Digit Recognizer: Getting Started competitions to have two-month rolling leaderboards. Once a submission is quite two months old, it'll be invalidated and not count towards the leader board. Beginners can leverage the Kaggle Learn platform and engage in several available tracks, and it fits for those who are curious about free hands-on data science learning, from pandas to deep learning. You’ll learn all the talents you would like to dive into Kaggle Competitions.

Playground: Playground competitions are a kind of “for fun” Kaggle data science competition that is more difficult than Getting Started. These competitions always provide relatively machine learning tasks and are similarly targeted at newcomers or Kagglers curious about practicing a replacement problem during a lower-stakes setting. Prizes for this type of Kaggle data science competition range from kudos to small cash prizes. Some samples of Playground competitions are:

Other Competition Types
Recruitment
In Recruitment competitions, only individuals can compete to create machine learning models for corporation-curated provocations. At the competition’s ending time, interested participants can upload their resumes for consideration by the company or organizations for their future recruitment. The prize is (potentially) an employment interview at the corporate or organization hosting the competition.

Annual
While not a stringent competition type fundamentally, Kaggle preserves two-yearly competition.

Limited Participation
Kaggle rarely hosts competitions with limited participation, and these competitions are mostly private or in which only invited persons can participate.

Beneath are some special Kaggle competition formats available. 

Simple Competitions: Simple competitions follow the quality Kaggle competition format. During a simple competition, users can access the entire datasets at the start of the competition after accepting the competition’s rules. As a participant, you have to download the information or data provided by the organizer, build models locally or in Notebooks. You will generate a prediction file, and after that, you will upload your prediction files as a submission on Kaggle. On Kaggle, most competitions follow this format.

Two-stage Competitions: The challenges are split into two parts in two-stage competitions. Stage 2 is built on the results achieved from Stage 1, and stage 2 engages in replacing the test dataset that's released at the beginning of the stage. Eligibility for Stage 2 generally needs making a successful submission in Stage 1. In two-stage competitions, it’s essential to read and understand the competition’s specific rules and timeline, which varies for each competition.

Code Competitions: Some competitions are code competitions. All submissions are made up of inside a Kaggle Notebook in these competitions, and it's impossible to upload submissions directly to the competition.

These competitions have two attractive features. The competition is more stable, as all users have commensurate hardware allowances. And therefore, the winning models lead to be far more straightforward than the winning models in other competitions such as Code Chef and code forces, as they require to run the code within the compute constraints imposed by the platform.

Code competitions are generated with their unique attributes(constraints) on the Notebooks you'll submit. These could also be restricted by attributes like CPU or GPU runtime, capacity to use obvious data, and access to the web. to find out the constraints you want to adhere to, review the wants for that specific competition.

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