Introduction to Image Classification with Python- A Beginner

Introduction to Image Classification with Python: A Beginner, available at Free, has an average rating of 4.87, with 22 lectures, based on 23 reviews, and has 487 subscribers.

You will learn about 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. This course is ideal for individuals who are This course is designed for absolute beginners who are interested in learning about image classification using Python. Its ideal for students, hobbyists, and professionals who want to gain foundational skills in deep learning. It is particularly useful for This course is designed for absolute beginners who are interested in learning about image classification using Python. Its ideal for students, hobbyists, and professionals who want to gain foundational skills in deep learning.

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Summary

Title: Introduction to Image Classification with Python: A Beginner

Price: Free

Average Rating: 4.87

Number of Lectures: 22

Number of Published Lectures: 22

Number of Curriculum Items: 22

Number of Published Curriculum Objects: 22

Original Price: Free

Quality Status: approved

Status: Live

What You Will 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.

Who Should Attend

This course is designed for absolute beginners who are interested in learning about image classification using Python. Its ideal for students, hobbyists, and professionals who want to gain foundational skills in deep learning.

Target Audiences

This course is designed for absolute beginners who are interested in learning about image classification using Python. Its ideal for students, hobbyists, and professionals who want to gain foundational skills in deep learning.

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!

Course Curriculum

Chapter 1: Introduction to Image Classification

Lecture 1: What is Image Classification?

Lecture 2: Real-World Applications of Image Classification

Chapter 2: Setting Up Your Environment

Lecture 1: Installing Python

Chapter 3: Introduction to Python for Data Science

Lecture 1: Basic Python Syntax

Lecture 2: Introduction to NumPy for Numerical Operations

Lecture 3: Introduction to Pandas for Data

Lecture 4: Introduction to Matplotlib for Data Visualization

Chapter 4: Image Processing Basics

Lecture 1: Understanding Images and Pixels

Lecture 2: Reading and Displaying Images using OpenCV

Lecture 3: Basic Image Manipulations (Resizing, Cropping, Rotating)

Chapter 5: Building Your First Image Classification Model

Lecture 1: Loading Image Dataset

Lecture 2: Data Augmentation

Lecture 3: Splitting Data into Training, Validation, and Testing Sets

Chapter 6: Building Your First Image Classification Model

Lecture 1: Defining CNN’s

Lecture 2: Defining the Model Architecture

Lecture 3: Compiling the Model

Lecture 4: Visualizing Training Results

Chapter 7: Improving Your Model

Lecture 1: Hyperparameter Tuning

Lecture 2: Adjusting Learning Rate

Lecture 3: Regularization Techniques

Lecture 4: Handling Imbalanced Data

Chapter 8: Conclusion

Lecture 1: Final Messages

Instructors

Meenakshi Nair
Instructor At Udemy

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5 stars: 18 votes

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