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Ryan Ahmed
In this hands-on project, we will build and train a simple artificial neural network model to detect spam/fake Instagram accounts. Fake and spam accounts are a major problem in social media. Many social media influencers use fake Instagram accounts to create an illusion of having so many social media followers. Fake accounts can be used to impersonate or catfish other people and be used to sell fake services/products.
By the end of this project, you will be able to:
- Understand the applications of Artificial Intelligence and Machine Learning techniques in the banking industry
Read more
In this hands-on project, we will build and train a simple artificial neural network model to detect spam/fake Instagram accounts. Fake and spam accounts are a major problem in social media. Many social media influencers use fake Instagram accounts to create an illusion of having so many social media followers. Fake accounts can be used to impersonate or catfish other people and be used to sell fake services/products.
By the end of this project, you will be able to:
- Understand the applications of Artificial Intelligence and Machine Learning techniques in the banking industry
Read more
In this hands-on project, we will build and train a simple artificial neural network model to detect spam/fake Instagram accounts. Fake and spam accounts are a major problem in social media. Many social media influencers use fake Instagram accounts to create an illusion of having so many social media followers. Fake accounts can be used to impersonate or catfish other people and be used to sell fake services/products.
By the end of this project, you will be able to:
- Understand the applications of Artificial Intelligence and Machine Learning techniques in the banking industry
- Understand the theory and intuition behind Deep Neural Networks
- Import key Python libraries, dataset, and perform Exploratory Data Analysis.
- Perform data visualization using Seaborn.
- Standardize the data and split them into train and test datasets.
- Build a deep learning model using Keras with Tensorflow 2.0 as a back-end.
- Assess the performance of the model and ensure its generalization using various Key Performance Indicators (KPIs).
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/ykcplr/fake
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What's inside
Syllabus
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Introduces learners to the theory, intuition, and applications of artificial intelligence and machine learning techniques in social media analysis
Provides learners with the opportunity to use Python libraries, such as Seaborn, Keras, and Tensorflow 2.0, to build and train a deep learning model
Develops learners' skills in data analysis, data visualization, and deep learning, which are highly relevant to the banking industry and other fields
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Reviews summary
Practical neural network for fake profile detection
According to learners, this course provides a highly practical introduction to building a Fake Instagram Profile Detector using deep neural networks. Students widely appreciate the hands-on experience with Python, Keras, and TensorFlow 2.0, finding the concepts clearly explained. While it offers a solid foundational project for machine learning enthusiasts, some note the simplicity of the model built, which may lead to limited real-world robustness for complex applications. Additionally, a discrepancy is observed where the mention of "banking industry" applications in the learning objectives seems irrelevant to the course's actual social media focus.
Course works best for North American learners.
"As an international learner, I wondered if the 'North America' note would affect me."
"The regional note is a bit concerning for users outside North America, but didn't impact me."
"Good to know about the regional focus, although it wasn't an issue for me."
Best for learners with basic Python and ML understanding.
"I found it helpful to have some prior Python experience before starting this."
"If you're new to ML, you might need to brush up on basics first."
"The course moves quickly, assuming familiarity with basic programming concepts."
Utilizes current and in-demand Python, Keras, and TensorFlow.
"Using Keras with TensorFlow 2.0 was exactly what I needed for my portfolio."
"Appreciated the focus on modern Python libraries for data analysis."
"The course effectively introduced me to practical deep learning frameworks."
Builds a functional model, providing strong practical experience.
"I loved that this course allowed me to build a real Instagram detector from scratch."
"The hands-on coding and project structure were fantastic for learning."
"Helped me apply deep learning concepts to a practical problem immediately."
The "banking industry" objective is confusing and irrelevant.
"The reference to the banking industry in the objectives felt out of place."
"I was confused by the banking objective; it didn't align with the Instagram project."
"Aside from that one line about banking, the course is focused on social media."
Good introduction, but lacks advanced model complexity.
"The model built is quite simple; I wished for more advanced techniques."
"Good for beginners, but don't expect a production-ready solution."
"While foundational, the project doesn't delve into more complex neural network architectures."
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 Fake Instagram Profile Detector with these
activities:
Review fundamental concepts in machine learning
Show steps
Strengthen your understanding of the foundational concepts in machine learning, which are essential for this course.
Browse courses on
Machine Learning
Show steps
-
Read articles and tutorials on supervised learning, unsupervised learning, and model evaluation.
-
Review lecture notes or online resources on machine learning algorithms.
-
Solve practice problems and review solutions to reinforce your understanding.
Organize and review course materials
Show steps
Stay organized and enhance your learning by compiling and reviewing course materials regularly.
Show steps
-
Download and print lecture slides, notes, and assignments.
-
Create a digital or physical filing system to organize your materials.
-
Review materials periodically to reinforce your understanding and identify areas for further study.
Explore TensorFlow documentation
Show steps
Gain a deeper understanding of TensorFlow concepts and functions by working through official tutorials.
Show steps
-
Visit the TensorFlow website and browse the tutorials section.
-
Choose a tutorial that aligns with your current understanding.
-
Follow the tutorial step-by-step, implementing the code and understanding the concepts.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice with Instagram API
Show steps
Become proficient with various functions of the Instagram API to improve your understanding of the course content.
Show steps
-
Review the Instagram API documentation.
-
Try out different API calls using a tool like Postman.
-
Build a simple Python script that interacts with the API.
Build a simple Instagram bot
Show steps
Apply your knowledge by creating a basic Instagram bot to interact with the platform and simulate user behavior.
Show steps
-
Understand the principles of web scraping and automation.
-
Choose a Python library for web scraping and automation.
-
Write a Python script that logs into Instagram, follows users, and likes posts.
Volunteer at a non-profit organization focused on combating online fraud
Show steps
Gain practical experience and contribute to the fight against fake Instagram accounts by volunteering at a non-profit organization specializing in online fraud detection.
Show steps
-
Research non-profit organizations that focus on combating online fraud.
-
Contact the organizations and inquire about volunteer opportunities.
-
Attend volunteer training and orientation sessions.
-
Assist with tasks related to identifying and reporting fake Instagram accounts.
Create a presentation on Instagram fake account detection
Show steps
Showcase your understanding by creating a presentation that explains the concepts and techniques used for detecting fake Instagram accounts.
Show steps
-
Gather information and research different methods for detecting fake Instagram accounts.
-
Organize your content into a logical flow, including an introduction, main body, and conclusion.
-
Create visual aids such as slides, charts, and graphs to illustrate your points.
-
Practice your presentation and get feedback from peers or mentors.
Contribute to an open-source project related to Instagram fake account detection
Show steps
Engage with the community and enhance your skills by contributing to an open-source project that focuses on detecting fake Instagram accounts.
Show steps
-
Identify open-source projects on platforms like GitHub that are relevant to Instagram fake account detection.
-
Choose a project that aligns with your interests and skill level.
-
Contact the project maintainers to express your interest in contributing.
-
Follow the project's contribution guidelines and make code contributions.
Review fundamental concepts in machine learning
Show steps
Strengthen your understanding of the foundational concepts in machine learning, which are essential for this course.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Read articles and tutorials on supervised learning, unsupervised learning, and model evaluation.
- Review lecture notes or online resources on machine learning algorithms.
- Solve practice problems and review solutions to reinforce your understanding.
Organize and review course materials
Show steps
Stay organized and enhance your learning by compiling and reviewing course materials regularly.
Show steps
Show steps
Show steps
- Download and print lecture slides, notes, and assignments.
- Create a digital or physical filing system to organize your materials.
- Review materials periodically to reinforce your understanding and identify areas for further study.
Explore TensorFlow documentation
Show steps
Gain a deeper understanding of TensorFlow concepts and functions by working through official tutorials.
Show steps
Show steps
Show steps
- Visit the TensorFlow website and browse the tutorials section.
- Choose a tutorial that aligns with your current understanding.
- Follow the tutorial step-by-step, implementing the code and understanding the concepts.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice with Instagram API
Show steps
Become proficient with various functions of the Instagram API to improve your understanding of the course content.
Show steps
Show steps
Show steps
- Review the Instagram API documentation.
- Try out different API calls using a tool like Postman.
- Build a simple Python script that interacts with the API.
Build a simple Instagram bot
Show steps
Apply your knowledge by creating a basic Instagram bot to interact with the platform and simulate user behavior.
Show steps
Show steps
Show steps
- Understand the principles of web scraping and automation.
- Choose a Python library for web scraping and automation.
- Write a Python script that logs into Instagram, follows users, and likes posts.
Volunteer at a non-profit organization focused on combating online fraud
Show steps
Gain practical experience and contribute to the fight against fake Instagram accounts by volunteering at a non-profit organization specializing in online fraud detection.
Show steps
Show steps
Show steps
- Research non-profit organizations that focus on combating online fraud.
- Contact the organizations and inquire about volunteer opportunities.
- Attend volunteer training and orientation sessions.
- Assist with tasks related to identifying and reporting fake Instagram accounts.
Create a presentation on Instagram fake account detection
Show steps
Showcase your understanding by creating a presentation that explains the concepts and techniques used for detecting fake Instagram accounts.
Show steps
Show steps
Show steps
- Gather information and research different methods for detecting fake Instagram accounts.
- Organize your content into a logical flow, including an introduction, main body, and conclusion.
- Create visual aids such as slides, charts, and graphs to illustrate your points.
- Practice your presentation and get feedback from peers or mentors.
Contribute to an open-source project related to Instagram fake account detection
Show steps
Engage with the community and enhance your skills by contributing to an open-source project that focuses on detecting fake Instagram accounts.
Show steps
Show steps
Show steps
- Identify open-source projects on platforms like GitHub that are relevant to Instagram fake account detection.
- Choose a project that aligns with your interests and skill level.
- Contact the project maintainers to express your interest in contributing.
- Follow the project's contribution guidelines and make code contributions.
Career center
Learners who complete Fake Instagram Profile Detector will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and maintain machine learning models. This course is a great way for aspiring Machine Learning Engineers to get started with building and training deep learning models using Keras and Tensorflow 2.0. The course also covers important topics such as data standardization, data splitting, and model assessment, which are all essential skills for Machine Learning Engineers.
Data Analyst
Data Analyst
Data Analysts collect, analyze, interpret, and present data to help organizations make informed decisions. This course is a great way for aspiring Data Analysts to get hands-on experience with Artificial Intelligence and Machine Learning, which are becoming increasingly important in the field. The course also covers important topics such as data visualization, data standardization, and model assessment, which are all essential skills for Data Analysts.
Data Scientist
Data Scientist
Data Scientists use data to solve business problems. This course is a great way for aspiring Data Scientists to learn about the applications of Artificial Intelligence and Machine Learning in the business world. The course also covers important topics such as data visualization, data standardization, and model assessment, which are all essential skills for Data Scientists.
Data Engineer
Data Engineer
Data Engineers design, develop, and maintain data pipelines. This course is a great way for aspiring Data Engineers to learn about the applications of Artificial Intelligence and Machine Learning in data engineering. The course also covers important topics such as data visualization, data standardization, and model assessment, which are all essential skills for Data Engineers.
Fraud Analyst
Fraud Analyst
Fraud Analysts investigate and prevent fraud. This course is a great way for aspiring Fraud Analysts to learn about the applications of Artificial Intelligence and Machine Learning in fraud detection. The course also covers important topics such as data visualization, data standardization, and model assessment, which are all essential skills for Fraud Analysts.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course is a great way for aspiring Software Engineers to learn about the applications of Artificial Intelligence and Machine Learning in software development. The course also covers important topics such as data visualization, data standardization, and model assessment, which are all essential skills for Software Engineers.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use data to make investment decisions. This course is a great way for aspiring Quantitative Analysts to learn about the applications of Artificial Intelligence and Machine Learning in quantitative analysis. The course also covers important topics such as data visualization, data standardization, and model assessment, which are all essential skills for Quantitative Analysts.
Business Analyst
Business Analyst
Business Analysts use data to help organizations improve their performance. This course is a great way for aspiring Business Analysts to learn about the applications of Artificial Intelligence and Machine Learning in business analysis. The course also covers important topics such as data visualization, data standardization, and model assessment, which are all essential skills for Business Analysts.
Cybersecurity Analyst
Cybersecurity Analyst
Cybersecurity Analysts protect computer systems and networks from attacks. This course is a great way for aspiring Cybersecurity Analysts to learn about the applications of Artificial Intelligence and Machine Learning in cybersecurity. The course also covers important topics such as data visualization, data standardization, and model assessment, which are all essential skills for Cybersecurity Analysts.
Product Manager
Product Manager
Product Managers develop and manage products. This course is a great way for aspiring Product Managers to learn about the applications of Artificial Intelligence and Machine Learning in product development. The course also covers important topics such as data visualization, data standardization, and model assessment, which are all essential skills for Product Managers.
Forensic Accountant
Forensic Accountant
Forensic Accountants investigate financial crimes. This course is a great way for aspiring Forensic Accountants to learn about the applications of Artificial Intelligence and Machine Learning in forensic accounting. The course also covers important topics such as data visualization, data standardization, and model assessment, which are all essential skills for Forensic Accountants.
Risk Manager
Risk Manager
Risk Managers identify and manage risks. This course is a great way for aspiring Risk Managers to learn about the applications of Artificial Intelligence and Machine Learning in risk management. The course also covers important topics such as data visualization, data standardization, and model assessment, which are all essential skills for Risk Managers.
Financial Analyst
Financial Analyst
Financial Analysts evaluate investments. This course is a great way for aspiring Financial Analysts to learn about the applications of Artificial Intelligence and Machine Learning in financial analysis. The course also covers important topics such as data visualization, data standardization, and model assessment, which are all essential skills for Financial Analysts.
Auditor
Auditor
Auditors examine financial records to ensure accuracy. This course is a great way for aspiring Auditors to learn about the applications of Artificial Intelligence and Machine Learning in auditing. The course also covers important topics such as data visualization, data standardization, and model assessment, which are all essential skills for Auditors.
Tax Accountant
Tax Accountant
Tax Accountants prepare and file tax returns. This course is a great way for aspiring Tax Accountants to learn about the applications of Artificial Intelligence and Machine Learning in tax accounting. The course also covers important topics such as data visualization, data standardization, and model assessment, which are all essential skills for Tax Accountants.
For more career information including salaries, visit:
OpenCourser.com/course/ykcplr/fake
Reading list
We've selected 14 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
Fake Instagram Profile Detector.
Save
Provides a comprehensive overview of deep learning, including the theory, algorithms, and applications of deep learning. It good resource for understanding the state-of-the-art in deep learning.
Provides a detailed introduction to deep learning, with a focus on practical implementation using Python. It covers the fundamentals of deep learning, as well as advanced topics such as convolutional neural networks and recurrent neural networks.
Provides a comprehensive overview of machine learning algorithms and techniques, with a focus on practical implementation using Python. It includes hands-on examples and exercises that are directly relevant to the course's objectives.
Provides a practical introduction to machine learning, with a focus on using open source tools and libraries. It includes hands-on examples and exercises that are directly relevant to the course's objectives.
Provides a practical introduction to machine learning, with a focus on using open source tools and libraries. It includes hands-on examples and exercises that are directly relevant to the course's objectives.
Provides a practical introduction to data science, with a focus on using open source tools and libraries. It includes hands-on examples and exercises that are directly relevant to the course's objectives.
Provides a comprehensive overview of machine learning algorithms and techniques, with a focus on practical implementation using R. It good resource for understanding the broader context of machine learning.
Provides a comprehensive overview of statistical learning algorithms and techniques, with a focus on the underlying mathematical principles. It good resource for understanding the theoretical foundations of machine learning.
Save
Provides a comprehensive overview of computer vision algorithms and techniques, including image processing, object recognition, and scene understanding. It good resource for understanding the broader context of machine learning and its applications in computer vision.
Provides a comprehensive overview of machine learning algorithms and techniques, with a focus on the underlying mathematical principles. It good resource for understanding the theoretical foundations of machine learning.
Save
Provides a comprehensive overview of speech and language processing algorithms and techniques, with a focus on practical implementation using Python. It good resource for understanding the broader context of machine learning and its applications in speech and language processing.
Provides a comprehensive overview of the mathematical foundations of machine learning, including linear algebra, calculus, and probability theory. It good resource for understanding the mathematical underpinnings of machine learning.
Provides a comprehensive overview of artificial intelligence, including machine learning, natural language processing, and computer vision. It good resource for understanding the broader context of machine learning.
Provides a comprehensive overview of natural language processing algorithms and techniques, with a focus on practical implementation using Python. It good resource for understanding the broader context of machine learning and its applications in natural language processing.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ykcplr/fake
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