In this project, you will build a model to recognize handwritten digits (such as those found in the MNIST dataset). This project will introduce you to the basics of image classification and give you a good understanding of how to build a machine learning model from scratch.
Here is a detailed syllabus for a Handwritten Digit Recognition project:
1. Introduction to Machine Learning
Definition of Machine Learning
Types of Machine Learning
Applications of Machine Learning
2. Image Classification
Introduction to Image Classification
Characteristics of Image Data
Preprocessing of Image Data
3. The MNIST Dataset
Introduction to the MNIST Dataset
Loading the MNIST Dataset
Exploring the MNIST Dataset
4. Simple Digit Recognition Model
Training a Simple Model
Evaluating the Model Performance
Overfitting and Regularization
5. Advanced Digit Recognition Model
Convolutional Neural Networks (CNNs)
Building a CNN for Digit Recognition
Hyperparameter Tuning
6. Improving Model Performance
Data Augmentation
Transfer Learning
Ensemble Methods
7. Evaluation Metrics
Introduction to Evaluation Metrics
Accuracy, Precision, Recall, and F1-Score
Confusion Matrix
8. 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 digit recognition models. In addition, they will learn how to perform exploratory data analysis, preprocess image data, train machine learning models, and evaluate their performance. This syllabus is designed to give students a comprehensive understanding of how to build a Handwritten Digit Recognition model and apply their knowledge to real-world problems.
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