In this project, you will build a model to classify emails as either spam or not spam. This project will give you a good understanding of how to work with text data, as well as how to build a simple machine learning model.
Here is a detailed syllabus for a Spam Mail Detection Using Machine Learning project:
Introduction to Machine Learning
Definition of Machine Learning
Types of Machine Learning
Applications of Machine Learning
Text Classification
Introduction to Text Classification
Characteristics of Text Data
Preprocessing of Text Data
The Spam Collection Dataset
Introduction to the Spam Collection Dataset
Loading the Spam Collection Dataset
Exploring the Spam Collection Dataset
Simple Spam Mail Detection Model
Training a Simple Model
Evaluating the Model Performance
Overfitting and Regularization
Advanced Spam Mail Detection Model
Recurrent Neural Networks (RNNs)
Building an RNN for Spam Mail Detection
Hyperparameter Tuning
Improving Model Performance
Text Embedding
Transfer Learning
Ensemble Methods
Evaluation Metrics
Introduction to Evaluation Metrics
Precision, Recall, and F1-Score
Confusion Matrix
Conclusion
Summary of Key Points
Challenges and Limitations
Future Work
Throughout the syllabus, students will use popular machine learning libraries such as scikit-learn, TensorFlow, and Keras to build and evaluate their spam mail detection models. In addition, they will learn how to perform exploratory data analysis, preprocess text data, train machine learning models, and evaluate their performance. This syllabus is designed to give students a comprehensive understanding of how to build a Spam Mail Detection model and apply their knowledge to real-world problems.
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