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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.

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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.

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What's inside

Syllabus

Instagram Fake Profile Detector
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.

Good to know

Know what's good
, what to watch for
, 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

Enthusiastically reviewed course

Learners largely agree that this good beginner-friendly project is worthwhile. Many students gave the course a perfect score, and respondants said it was nice and gave it a thumbs up. According to the reviews, the course is a worthwhile investment for those who are just starting out in the field.
Course reviews are very positive.
"Good"
"good"
"nice "
"Thumbs Up"
"Good beginner friendly project"
Course is suited for beginners.
"Thumbs Up"
"good beginner friendly project"
"If you are thinking to attend this project please don't its complete waste of money."

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
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
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
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
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
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
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
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
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.

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 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 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 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 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 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 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.
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.
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.
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.
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.
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.
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.
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 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.
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.

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.
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.
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.
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.

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