Fake Instagram Profile Detector

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Coursera Project Network
В этом Проект с консультациями вы:

Understand the theory and intuition behind Deep Neural Networks.

Build and train a deep learning model using Keras with Tensorflow 2.0 as a backend.

Assess the performance of trained model and ensure its generalization using various Key performance indicators.

Clock1.5 hours
BeginnerНачинающий
CloudЗагрузка не требуется
VideoВидео на разделенном экране
Comment DotsАнглийский
LaptopТолько для ПК

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.

Навыки, которые вы получите

  • Deep Learning
  • Machine Learning
  • Python Programming
  • classification
  • Artificial Intelligence(AI)

Будете учиться пошагово

На видео, которое откроется рядом с рабочей областью, преподаватель объяснит эти шаги:

  1. Task 1: Understand the problem statement and business case

  2. Task 2: Import Datasets and Libraries

  3. Task 3: Exploratory Data Analysis

  4. Task 4: Perform Data Visualization

  5. Task 5: Prepare the data to feed the model

  6. Task 6: Understand the theory and intuition behind Artificial Neural Networks

  7. Task 7: Build a simple Multi Layer Neural Network

  8. Task 8: Compile and train a Deep Learning Model

  9. Task 9: Assess the performance of the trained model

Как устроены проекты с консультациями

Ваше рабочее пространство — это облачный рабочий стол в браузере. Ничего не нужно загружать.

На разделенном экране видео преподаватель предоставляет пошаговые

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