Fake Instagram Profile Detector
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.
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.
Skills you will develop
Learn step-by-step
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Task 1: Understand the problem statement and business case
Task 2: Import Datasets and Libraries
Task 3: Exploratory Data Analysis
Task 4: Perform Data Visualization
Task 5: Prepare the data to feed the model
Task 6: Understand the theory and intuition behind Artificial Neural Networks
Task 7: Build a simple Multi Layer Neural Network
Task 8: Compile and train a Deep Learning Model
Task 9: Assess the performance of the trained model
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In a split-screen video, your instructor guides you step-by-step
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