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Deep Learning with Python & Pytorch for Image Classification (Udemy.com)

Deep Learning and Computer Vision for Image Classification with PyTorch, Python. Train and Deploy Models on Custom data

Created by: AI & Computer Science School

Last updated June 2026

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What you will learn

  • Learn Image Classification using Advanced Deep Learning Models with Python and PyTorch
  • Learn Single-Label Image Classification and Multi-Label Image Classification with Python and PyTorch
  • Perform Image Classification by building Convolutional Neural Networks from Scratch
  • Learn Deep CNNs Architectures including LeNet, AlexNet, Resnet, GoogleNet, VGG
  • Deep Learning Pre-trained Models Such as ResNet and AlexNet for Image Classification
  • Master Transfer Learning by Employing Pre-trained Deep Learning Models.
  • Perform Data Preprocessing using Transformations with Pytorch
  • Perform Single-Label Image Classification with ResNet and AlexNet
  • Perform Multi-Label Image Classification with ResNet and AlexNet
  • Custom Dataset, Data Augmentation, Dataloaders, and Training Function
  • Deep ResNet Model FineTuning for Image Classification
  • ResNet Model HyperParameteres Optimization

How it compares

Against the other 461 rated Udemy courses in Data Science on CourseDuck:

  • Rating4.5 out of 5higher than 46% of them
  • Reviews174#359 of 462 by number of reviews
  • Length3.4 hoursshorter than 77% of them
  • Last updatedJune 2026more recently than 77% of them

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Course Description

Are you interested in unlocking the full potential of Artificial Intelligence? Do you want to learn how to create powerful image recognition systems that can identify objects with incredible accuracy? If so, then our course on Deep Learning with Python for Image Classification is just what you need! In this course, you will learn Deep Learning with Python and PyTorch for Image Classification using Pre-trained Models and Transfer Learning. Image Classification is a computer vision task to recognize an input image and predict a single-label or multi-label for the image as output using Machine Learning techniques.

Embark on a journey into the fascinating world of deep learning with Python and PyTorch, tailored specifically for image classification tasks. In this hands-on course, you'll delve deep into the principles and practices of deep learning, mastering the art of building powerful neural networks to classify images with remarkable accuracy. From understanding the fundamentals of convolutional neural networks  to implementing advanced techniques using PyTorch, this course will equip you with the knowledge and skills needed to excel in image classification projects.

Deep learning has emerged as a game-changer in the field of computer vision, revolutionizing image classification tasks across various domains. Understanding how to leverage deep learning frameworks like PyTorch to classify images is crucial for professionals and enthusiasts alike. Whether you're a data scientist, software engineer, researcher, or student, proficiency in deep learning for image classification opens doors to a wide range of career opportunities. Moreover, with the exponential growth of digital imagery in fields such as healthcare, autonomous vehicles, agriculture, and more, the demand for experts in image classification continues to soar.

Course Breakdown:

  • You will use Google Colab notebooks for writing the python code for image classification using Deep Learning models.

  • You will learn how to connect Google Colab with Google Drive and how to access data.

  • You will perform data preprocessing using different transformations such as image resize and center crop etc.

  • You will perform two types of Image Classification, single-label Classification, and multi-label Classification using deep learning models with Python.

  • Learn Convolutional Neural Networks (CNN) including LeNet, AlexNet, Resnet, GoogleNet, VGG

  • You will be able to learn Transfer Learning techniques:

    1. Transfer Learning by FineTuning the model.

    2. Transfer Learning by using the Model as Fixed Feature Extractor.

  • You will learn how to perform Data Augmentation.

  • You will learn how to load Dataset, Dataloaders.

  • You will Learn to FineTune the Deep Resnet Model.

  • You will learn how to use the Deep Resnet Model as Fixed Feature Extractor.

  • You will Learn HyperParameters Optimization and results visualization.

  • Perform Image Classification by building Convolutional Neural Networks from Scratch

  • Calculate Accuracy, Precision, Recall, and F1 Score for Image Classification

  • Calculate and Visualize Confusion Matrix for Detailed Classification Model Performance

The applications of deep learning for image classification are diverse and impactful, spanning across numerous industries and domains. Some key applications include:

  • Medical Imaging: Diagnosing diseases from medical scans such as X-rays, MRIs, and CT scans.

  • Autonomous Vehicles: Identifying objects and obstacles in real-time for safe navigation.

  • Surveillance Systems: Recognizing and tracking objects or individuals in surveillance footage.

  • Agriculture: Monitoring crop health and detecting pests or diseases from aerial images.

  • E-commerce: Improving product recommendation systems based on image analysis.

By mastering deep learning techniques for image classification, you'll be equipped to tackle real-world problems and drive innovation across various sectors. Whether you're interested in building AI-powered applications, conducting groundbreaking research, or advancing your career in the tech industry, this course will empower you to make significant strides in the exciting field of deep learning for image classification.

See you inside the class!


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Instructor Details

AI & Computer Science School

Vision: We aim to help people understand, develop and apply Artificial Intelligence (AI). Making world-class AI Education accessible to everyone. The mission is to develop and advance AI. The future lies in building AI and its applications, not just using, admiring, or discussing about AI. It's time to unlock the full potential of AI and shape the world with innovations!

AI & Computer Science School aims to equip you with the skills and knowledge necessary to succeed in today's technology industry. We offer a wide range of cutting-edge computer science courses that cover a range of subjects, including Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Computer Vision (CV), Data Science (DS), Programming, and Databases. Our course material is designed to accommodate students of all levels, from beginner to advanced.

By taking our courses, you will gain a deeper understanding of the latest technologies and commercial applications of computing practices. You'll learn how to solve complex problems and develop your technical and transferable skills through hands-on exercises and project-based learning. Our courses are taught by experienced industry professionals who are passionate about teaching and dedicated to helping you succeed.

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Reviews

4.5

174 ratings on Udemy

Select a bar to show only those reviews.Select the bar again to show every rating.

By Andre Vartore on 7/23/2024

Course provides a well grounded entry point into the field of Machine Leaning. Amazing teaching style complex topics are explained in a very well way so anyone can easily understand and grab.

By Ali Zain Javed on 5/8/2024

I found this course to be informative and well-structured. The instructor did a great job of breaking down the concepts of deep learning for image classification into manageable chunks.

By Jansen on 2/20/2024

Deep learning concepts are covered from basics pretrained models to transfer learning with python. I am more clear how deep learning works as demonstrated with examples.

By Ross Tayler on 12/7/2023

Deep learning concepts are covered in such a understandable way that make sense. This course covers starts fromm basics deep learning to transfer learning.

By Germán Alonso on 10/25/2023

Many code options are left uncommented or partially commented. Explanations of why some things are done are explained in a very superficial way in many cases. It is well structured but shallow. If you are looking to get started with pytorch and machine vision, it is a decent course to start with but not much more.

By Michelle on 10/4/2023

According to my learning experience, this course covers theoretical concepts starting from basics of deep learning to practical implementation of the the deep learning models.

By Francesco on 5/21/2023

Great explanations of the deep learning concepts demonstrated by building and training the deep learning models.

By Giancarlo Bazzi on 8/6/2022

Just a skim on the topic, but fast and informative.

By I B Karthikeyan on 7/15/2022

Was expecting more detailed explanation for each steps.. But otherwise it was a good guided project.

By Temitayo Adeniran on 5/16/2022

Good course for anyone wanting to jump into the field of deep learning with python. The instructor explains the details very well. I surely recommend it.

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Overall Score : 90 / 100