Course Description:
This course will focus on the task of classifying handwritten digits using the MNIST dataset. The course will introduce the basics of image classification and machine learning, and provide hands-on experience in building and training machine learning models to accurately classify the MNIST dataset.
Course Objectives:
By the end of this course, students will be able to:
Understand the basics of image classification and machine learning
Identify the different approaches for solving the MNIST classification problem
Build and train machine learning models using popular frameworks such as Scikit-Learn and TensorFlow
Evaluate the performance of machine learning models using various metrics
Apply transfer learning techniques to improve the accuracy of the MNIST classification problem
Prerequisites:
Basic programming knowledge (preferably Python)
Familiarity with linear algebra and calculus
Basic understanding of machine learning concepts and terminology.
Course Outline:
Introduction
Overview of image classification and machine learning
Introduction to the MNIST dataset
Preparing the dataset for machine learning
Approaches to solving the MNIST classification problem
Basic image classification algorithms
Convolutional Neural Networks (CNN)
Transfer learning techniques
Building and training machine learning models
Implementing basic image classification algorithms using Scikit-Learn
Building and training CNNs using TensorFlow
Applying transfer learning techniques using pre-trained models
Evaluation of machine learning models
Metrics for evaluating the performance of machine learning models
Confusion matrices and classification reports
Improving model performance through hyperparameter tuning
Conclusion
Summary of the course
Future directions and advanced topics in image classification and machine learning
Throughout the course, students will learn how to develop a machine learning model for MNIST Handwritten Digit Classification. They will gain skills and knowledge related to data preprocessing, model selection, and evaluation. Students will learn how to preprocess the image data, select and optimize the best machine learning algorithm for the classification task, and evaluate the performance of the model. Additionally, students will learn how to visualize and interpret the model results to communicate the findings effectively. Upon completion of the course, students will have the skills necessary to build robust machine learning models for image classification tasks.
How can Codersarts help in this project?
Consultation: Codersarts can provide expert consultation on your project and offer guidance on best practices for preprocessing text data, model selection, and deployment.
Custom Development: Codersarts can develop custom software solutions for your project, including data preprocessing tools, feature extraction scripts, and machine learning models for toxic comment classification.
Code Review: Codersarts can review your code and offer suggestions for improving efficiency, scalability, and maintainability.
Training: Codersarts can provide online training courses on natural language processing and machine learning to help you and your team develop the skills you need for your project.
Contact us
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