[Free] Introduction To Image Classification With Python: A Beginner
Learn Image Classification with Python with Convolutional Neural Networks – Free Course
What you’ll learn
- Fundamentals of Image Classification: Understand the basics of image classification, including what it is and its various applications.
- Data Preprocessing Techniques: Learn how to preprocess image data, including normalization, one-hot encoding, and splitting data into training and validation se
- Building and Training Convolutional Neural Networks (CNNs): Build, train, and evaluate CNN models using Keras, and understand how to fine-tune and optimize mode
- Handling Imbalanced Data: Apply techniques to manage imbalanced datasets to improve model fairness and accuracy.
Requirements
- Basic knowledge of Python programming is recommended
Description
Welcome to “Introduction to Image Classification with Python: A Beginner’s Guide”! This course is designed to provide you with a comprehensive understanding of image classification, an essential task in the field of machine learning and artificial intelligence. Whether you’re a student, a hobbyist, or a professional looking to dive into the world of image processing, this course is perfect for you.Throughout this course, you’ll learn the fundamentals of image classification, starting with setting up your Python environment using Google Colab. You’ll get hands-on experience installing and configuring Python, creating a virtual environment, and installing essential libraries like NumPy, Pandas, Matplotlib, OpenCV, and TensorFlow/Keras.
We will explore the CIFAR-10 dataset, teaching you how to load, visualize, and understand the data. You’ll learn important preprocessing techniques such as normalization, one-hot encoding, and splitting data into training and validation sets. Building on this foundation, you’ll dive into the world of Convolutional Neural Networks (CNNs), understanding their architecture and building your first CNN model using Keras.Training and evaluating your model will be covered in depth, along with fine-tuning and optimizing your model for better performance. You’ll also learn how to handle imbalanced data using various techniques to ensure your model is fair and accurate.
Finally, we’ll guide you through saving, loading, and deploying your trained models, giving you practical experience in taking your models from development to production.
By the end of this course, you’ll have a solid foundation in image classification and the skills needed to tackle more advanced projects. Join us and start your journey into the exciting world of image classification with Python!
Author(s): Meenakshi Nair
