How to enter a Kaggle competition

How to enter a Kaggle competition, available at $49.99, has an average rating of 3.75, with 7 lectures, based on 2 reviews, and has 60 subscribers.

You will learn about Open a Kaggle User Account Join a Kaggle competition Enter or modify code in Python Save a Jupyter Notebook in Kaggle Submit predictions in Kaggle Look on the Leaderboard for the submission score Be familiar with tabular competition questions, to include classification problems, regression problems, image recognition problems, and time series problems This course is ideal for individuals who are This course is for individuals who would like to learn how to enter a competition in Kaggle, submit their predictions, and get on the Leaderboard. It is particularly useful for This course is for individuals who would like to learn how to enter a competition in Kaggle, submit their predictions, and get on the Leaderboard.

Enroll now: How to enter a Kaggle competition

Summary

Title: How to enter a Kaggle competition

Price: $49.99

Average Rating: 3.75

Number of Lectures: 7

Number of Published Lectures: 7

Number of Curriculum Items: 7

Number of Published Curriculum Objects: 7

Original Price: ￡19.99

Quality Status: approved

Status: Live

What You Will Learn

Open a Kaggle User Account

Join a Kaggle competition

Enter or modify code in Python

Save a Jupyter Notebook in Kaggle

Submit predictions in Kaggle

Look on the Leaderboard for the submission score

Be familiar with tabular competition questions, to include classification problems, regression problems, image recognition problems, and time series problems

Who Should Attend

This course is for individuals who would like to learn how to enter a competition in Kaggle, submit their predictions, and get on the Leaderboard.

Target Audiences

This course is for individuals who would like to learn how to enter a competition in Kaggle, submit their predictions, and get on the Leaderboard.

The learner will open a go into the Kaggle website and open their own account.  He will go into the competitions page of Kaggle and will enter five Kaggle competitions. He will review the code of the following Kaggle competitions:-

1. Titanic competition

2. Ames House Price competition

3. Disaster Tweet competition

4 Recognise the Digit competition

5. Predict Future Sales competition.

When the learner reviews the code, he will learn a methodical procedure for writing code onto Kaggle’s Jupyter Notebook for the competition.  He learner will learn how to accomplish the following tasks when he enters the competitions:-

1. He will learn how to import libraries into the Jupyter Notebook. 

2. The learner will learn the purpose of the libraries he is importing, to include pandas, numpy, os, sklearn, math, matplotlib, seaborn, nltk, and string, just to name a few. 

3. The learner will learn how to read a csv file into the program by using the pandas library to convert it into a dataframe.

4. The learner will learn how to analyse the csv and determine what type of features it consists of.

5. The learner will learn how to analyse the target and use matplotlib or seaborn to graph the data points. 

6. The learner will learn how to clean up code and replace missing values.

7. The learner will learn how to combine two dataframes together to form one dataframe.

8. The learner will learn the difference between numerical and categorical values, and will learn how to encode categorical values.

9. The learner will learn how to make a heatmap to carry out feature selection of the model, thereby reducing potential noise in the system.

10. The learner will learn how to define features that will be used to make a prediction.

11. The learner will learn how to define the independent and dependent variables.

12. The learner will learn how to normalise and standardise the the independent variable because because predictions can be more accurate by using this preprocessing technique.

13. The learner will learn how to split the dataset into training and validatiog sets to be used in training, fitting and making predictions.

14. The learner will be able to use sklearn, a machine learning library, so select the most appropriate model to train, fit and predict on the data.

15. The learner will be able to make predictions on the test dataset, which will form the basis of the submission of predictions to Kaggle.

16. The learner will be able to prepare the predictions for submission and submit them to Kaggle for scoring.

17. Once the predictions have been submitted to Kaggle, the learner will be able to view his score and to look at his place on the leaderboard.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction to course

Lecture 2: Titanic Competition with Kaggles introductory code

Chapter 2: Titanic

Lecture 1: Titanic Competition with instructors code

Chapter 3: Ames House Prices

Lecture 1: Ames House Prices with instructors code

Chapter 4: Natural Language Processing

Lecture 1: Disaster Tweets competition with instructors code

Chapter 5: MNIST

Lecture 1: MNIST competition with instructors code

Chapter 6: Predict Future Sales

Lecture 1: Predict Future Sales competition with instructors code

Instructors

Tracy Renee
Data Scientist

Rating Distribution

1 stars: 0 votes

2 stars: 1 votes

3 stars: 0 votes

4 stars: 0 votes

5 stars: 1 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!