Tutorial: Detector de perfiles falsos de Instagram

4.2
estrellas
14 calificaciones
ofrecido por
Coursera Project Network
En este Tutorial guiado, tú:

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
BeginnerPrincipiante
CloudNo se necesita descarga
VideoVideo de pantalla dividida
Comment DotsInglés (English)
LaptopSolo escritorio

In this guided tutorial, 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 tutorial, 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.

Habilidades que desarrollarás

Deep LearningMachine LearningPython ProgrammingclassificationArtificial Intelligence(AI)

Aprende paso a paso

En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:

  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

Cómo funcionan los tutoriales guiados

Tu espacio de trabajo es un escritorio virtual directamente en tu navegador, no requiere descarga.

En un video de pantalla dividida, tu instructor te guía paso a paso

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