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PROJECT
Deep Learning for Email Classification with LSTM and Word2Vec
In this project, we’ll learn to construct a robust spam email detection model by using data preprocessing techniques, Word2Vec embeddings, and LSTM models.
You will learn to:
Gain proficiency in natural language processing (NLP) techniques for text-based classification tasks.
Understand the Word2Vec technique to generate word embeddings for text data.
Construct and train a LSTM (Long Short-Term Memory) model for spam email detection using Keras.
Master the compilation, training, and evaluation steps in deep learning model development.
Skills
Machine Learning
Deep Learning
Natural Language Processing
Prerequisites
Knowledge of machine learning concepts and techniques
Familiarity with data manipulation using pandas
Basic understanding of deep learning concepts
Exposure to text data preprocessing techniques
Technologies
Numpy
Pandas
Gensim
Tensorflow
Scikit-learn
Project Description
In this project, we’ll learn how to build an effective model for discerning spam emails using natural language processing (NLP) and deep learning. We’ll start off with some data preprocessing steps, which include extracting email texts and labels from the dataset, splitting the dataset, and tokenizing sequences for model input.
We’ll then train a Word2Vec model to generate word embeddings and construct a Long Short-Term Memory (LSTM) model using these embeddings for robust spam detection. The model’s construction and training, along with evaluation and reporting tasks, will provide a comprehensive journey into email classification.
By the end of this project, we will have a well-rounded understanding of spam email detection and a functional model ready for deployment.
Project Tasks
1
Get Started
Task 0: Introduction
Task 1: Import Libraries
Task 2: Load the Dataset
2
Data Preprocessing
Task 3: Extract Email Texts and Labels
Task 4: Split the Dataset
Task 5: Tokenize and Pad Sequences
3
Text Preprocessing and Model Building
Task 6: Train a Word2Vec Model
Task 7: Prepare the Embedding Matrix
Task 8: Build an LSTM Model
4
Model Construction and Training
Task 9: Compile the Model
Task 10: Train the Model
Task 11: Evaluate the Model
5
Evaluation and Reporting
Task 12: Generate Predictions
Task 13: Print the Classification Report
Congratulations!
Relevant Courses
Use the following content to review prerequisites or explore specific concepts in detail.