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Nawas Naziru
This project is for anyone with foundation in programming and machine learning who wants to develop Data science and Machine learning projects but having limited resources on their computer and limited time. You will learn how to use the Google Colaboratory...
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This project is for anyone with foundation in programming and machine learning who wants to develop Data science and Machine learning projects but having limited resources on their computer and limited time. You will learn how to use the Google Colaboratory via your web browser to develop a Fake and Real News Detection Data Science Project. You will start by learning how to launch Google Colaboratory from your web browser then create runtime environment for your project, create a python notebook to house the project, understand the project design, learn how to import your training data into Google Colaboratory, develop the project, train and evaluate your model performance and finally, learn how to extract the model as deliverable for use in your application of choice, be it web application or native application. This Guided Project was created by a Coursera community member.
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This course is tailored for individuals with a background in programming and machine learning who aspire to develop projects but face constraints in terms of computing resources and time constraints
Utilizes Google Colaboratory as the platform for project development, enabling learners to access cloud-based computational resources
Focuses on developing a practical project in fake news detection, providing learners with hands-on experience in a real-world application
Provides step-by-step guidance throughout the project development process, from data import to model evaluation
Teaches essential skills for deploying machine learning models in real-world applications

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Reviews summary

Beginner-friendly data science with google colaboratory

This course is a great introduction to using Google Colaboratory for data science projects. It's well-structured and easy to follow, and the hands-on approach helps you learn by doing. The course covers all the essential steps of a data science project, from data import and cleaning to model training and evaluation.
Course is beginner-friendly.
Course is practical and hands-on.
"...Well-structured and easy to follow, and the hands-on approach helps you learn by doing..."
Code explanations are not provided at many places.
"...Code explanations are not provided at many places. This can be imporved..."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Developing Data Science Projects With Limited Computer Resources Using Google Colaboratory with these activities:
Machine Learning for Beginners
Read a beginner-friendly book on machine learning to build a solid foundation for the course.
Show steps
  • Purchase or borrow the book
  • Read the book thoroughly
  • Take notes and highlight important concepts
Google Colab Tutorial
Follow a guided tutorial to learn the basics of Google Colab, the platform you will use for your project.
Browse courses on Google Colab
Show steps
  • Find a reputable tutorial online
  • Follow the教程steps carefully
  • Practice using Google Colab on your own
Study Group Discussions
Join or start a study group to discuss course concepts, share insights, and support each other's learning.
Show steps
  • Find or form a study group with classmates
  • Set regular meeting times
  • Discuss course materials, ask questions, and share resources
Four other activities
Expand to see all activities and additional details
Show all seven activities
Data Preprocessing Drills
Complete practice drills to improve your skills in data preprocessing, a crucial step in machine learning projects.
Browse courses on Data Preprocessing
Show steps
  • Find a resource with data preprocessing drills
  • Complete the drills regularly
  • Review your results and identify areas for improvement
Data Science Glossary
Create a glossary of data science terms to enhance your understanding and retention of key concepts.
Show steps
  • Compile a list of data science terms
  • Define each term clearly and concisely
  • Organize the glossary alphabetically or by topic
Data Science Workshop
Attend a workshop to gain hands-on experience with data science tools and techniques.
Browse courses on Data Science
Show steps
  • Find a relevant workshop in your area
  • Register for the workshop
  • Attend the workshop and actively participate
Fake News Detection Model
Develop a machine learning model to detect fake news, putting into practice the skills you learn in the course.
Browse courses on Fake News Detection
Show steps
  • Gather a dataset of fake and real news articles
  • Build a machine learning model using the dataset
  • Evaluate the performance of your model
  • Deploy your model as a web application or API

Career center

Learners who complete Developing Data Science Projects With Limited Computer Resources Using Google Colaboratory will develop knowledge and skills that may be useful to these careers:
Data Scientist
Data Scientists use their knowledge of mathematics, statistics, and computer science to extract insights from data. This course can help Data Scientists develop the skills they need to use Google Colaboratory to develop and deploy machine learning models. By learning how to use Google Colaboratory, Data Scientists can save time and resources, and focus on developing models that can solve real-world problems.
Machine Learning Engineer
Machine Learning Engineers design and implement machine learning models. This course can help Machine Learning Engineers develop the skills they need to use Google Colaboratory to develop and deploy machine learning models. By learning how to use Google Colaboratory, Machine Learning Engineers can save time and resources, and focus on developing models that can solve real-world problems.
Software Engineer
Software Engineers design, develop, and maintain software applications. This course can help Software Engineers develop the skills they need to use Google Colaboratory to develop and deploy machine learning models. By learning how to use Google Colaboratory, Software Engineers can save time and resources, and focus on developing models that can solve real-world problems.
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns. This course can help Data Analysts develop the skills they need to use Google Colaboratory to develop and deploy machine learning models. By learning how to use Google Colaboratory, Data Analysts can save time and resources, and focus on developing models that can solve real-world problems.
Business Analyst
Business Analysts use data to solve business problems. This course can help Business Analysts develop the skills they need to use Google Colaboratory to develop and deploy machine learning models. By learning how to use Google Colaboratory, Business Analysts can save time and resources, and focus on developing models that can solve real-world problems.
Product Manager
Product Managers are responsible for the development and launch of new products. This course can help Product Managers develop the skills they need to use Google Colaboratory to develop and deploy machine learning models. By learning how to use Google Colaboratory, Product Managers can save time and resources, and focus on developing models that can solve real-world problems.
Market Researcher
Market Researchers collect and analyze data to understand consumer behavior. This course can help Market Researchers develop the skills they need to use Google Colaboratory to develop and deploy machine learning models. By learning how to use Google Colaboratory, Market Researchers can save time and resources, and focus on developing models that can solve real-world problems.
Statistician
Statisticians collect, analyze, and interpret data. This course can help Statisticians develop the skills they need to use Google Colaboratory to develop and deploy machine learning models. By learning how to use Google Colaboratory, Statisticians can save time and resources, and focus on developing models that can solve real-world problems.
Database Administrator
Database Administrators manage and maintain databases. This course can help Database Administrators develop the skills they need to use Google Colaboratory to develop and deploy machine learning models. By learning how to use Google Colaboratory, Database Administrators can save time and resources, and focus on developing models that can solve real-world problems.
Data Engineer
Data Engineers design and build data pipelines. This course can help Data Engineers develop the skills they need to use Google Colaboratory to develop and deploy machine learning models. By learning how to use Google Colaboratory, Data Engineers can save time and resources, and focus on developing models that can solve real-world problems.
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. This course can help Quantitative Analysts develop the skills they need to use Google Colaboratory to develop and deploy machine learning models. By learning how to use Google Colaboratory, Quantitative Analysts can save time and resources, and focus on developing models that can solve real-world problems.
Web Developer
Web Developers design and develop websites. This course can help Web Developers develop the skills they need to use Google Colaboratory to develop and deploy machine learning models. By learning how to use Google Colaboratory, Web Developers can save time and resources, and focus on developing models that can solve real-world problems.
Computer Scientist
Computer Scientists research and develop new computer technologies. This course can help Computer Scientists develop the skills they need to use Google Colaboratory to develop and deploy machine learning models. By learning how to use Google Colaboratory, Computer Scientists can save time and resources, and focus on developing models that can solve real-world problems.
Software Developer
Software Developers design, develop, and test software applications. This course can help Software Developers develop the skills they need to use Google Colaboratory to develop and deploy machine learning models. By learning how to use Google Colaboratory, Software Developers can save time and resources, and focus on developing models that can solve real-world problems.
Data Visualization Specialist
Data Visualization Specialists create visual representations of data. This course can help Data Visualization Specialists develop the skills they need to use Google Colaboratory to develop and deploy machine learning models. By learning how to use Google Colaboratory, Data Visualization Specialists can save time and resources, and focus on developing models that can solve real-world problems.

Reading list

We've selected 12 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Developing Data Science Projects With Limited Computer Resources Using Google Colaboratory.
Covers a wide range of data science topics, including data cleaning, munging, visualization, and modeling.
Provides a comprehensive overview of speech and language processing, covering a wide range of topics from acoustics and phonetics to natural language understanding.
Provides a thorough introduction to deep learning, covering the basics of neural networks, convolutional neural networks, and recurrent neural networks.
Provides a comprehensive overview of the mathematical foundations of machine learning, including linear algebra, probability theory, and optimization.
Provides a gentle introduction to machine learning, covering the basics of supervised and unsupervised learning, feature engineering, and model evaluation.
Provides a gentle introduction to biostatistics, covering the basics of probability, hypothesis testing, and regression analysis.
Provides a gentle introduction to Python programming, covering the basics of syntax, data types, and control flow.

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