Developing Data Science Projects With Google Colab

Name: Developing Data Science Projects With Google Colab
Rating: 4.3 (82 reviews)

Develop fake and real news detection data science projects with just your internet browser

Created byNawas Naziru Adam

Last updated 12/2025

English

What you'll learn

How to use Google Colab through your internet browser
How to design a data science project
How to train and evaluate a machine learning model
How to deploy a machine learning model in your application

Course content

1 section • 9 lectures • 53m total length

Setting up Google Colaboratory for Data Science Project7:45
By the end of this task, you will be able to launch Google Colaboratory from your browser, get started by creating a brand new Python notebook to house your project codes in an appropriate runtime environment.
Google Colaboratory is free without limitation?
Which of the following statements are correct? Select all that apply
Project design approach and getting data6:26
By the end of this task, you will be able to explain the approach to take to detect fake and real news using machine learning, get the data to use for training your fake and real news detection machine learning model and learn how to import and use the data in your Google Colaboratory project.
Overview of the basic tools in Google Colaboratory6:35
By the end of this task, you will be able to add a new cell, add comments, delete a cell, copy and cut cell content, save your work, execute your codes and restart the session if things go wrong without losing your written codes.
Data visualization and data cleaning6:39
By the end of this task, you will be able to explore and visualise the contents of the two csv files in form of pandas dataframes, display the data in various charts, look for and fix missing values, duplicate values, class imbalance and all other relevant data cleaning tasks.
Data labelling and feature extraction3:08
By the end of this task, you will be able to merge the two datasets, label the merged data by adding a label column and convert the data to numeric form.
Model creation and training5:12
By the end of this task, you will be able to create a machine learning model and train it on the fake and real news.
Model evaluation5:18
By the end of this task, you will be able to test and evaluate the performance of your model using the test data we held out just before training.
Saving and downloading/exporting your model6:07
By the end of this task, you will learn how to save your trained model and download it for use in your application of choice.
Model deployment6:25
By the end of this task, you will learn how to use your model in real life application.

Requirements

This course is not for complete beginners in data science and machine learning and so, don't expect code explanation because it is assumed you can understand what any piece of python code does.
Familiarity with Python programming
Basic knowledge of statistics and machine learning.
Please this course is not meant to teach you programming in python but rather to give you the experience of developing a real life Data Science Project from data collection to application deployment using the knowledge you acquired so far.

Description

This project is for anyone who wants to develop Data science and Machine learning projects but having limited resources on his computer and limited time. In less than 2 hours, you will learn how to develop and deploy a fake news detection data science project!

In essence, you will learn,

- how to design a real life data science project

- how to get data to train a machine learning model

- how to clean and preprocess your data

- how to create and train a model to learn from your data

- how to evaluate the performance of the trained model

- and finally, how to deploy the model in any real-life application of your choice.

According to wikipedia,

"Google Colaboratory (also known as Colab) is a free Jupyter notebook environment that runs in the cloud and stores its notebooks on Google Drive. Colab was originally an internal Google project; an attempt was made to open source all the code and work more directly upstream, leading to the development of the "Open in Colab" Google Chrome extension, but this eventually ended, and Colab development continued internally. As of October 2019, the Colaboratory UI only allows for the creation of notebooks with Python 2 and Python 3 kernels; however, an existing notebook whose kernelspec is IR or Swift will also work, since both R and Swift are installed in the container. Julia language can also work on Colab (with e.g. Python and GPUs; Google's tensor processing units also work with Julia on Colab."

Who this course is for:

This course is for Intermediate data science and machine learning enthusiasts/learners.