Mastering Image Classification With Deep Learning
Published 1/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 570.66 MB | Duration: 2h 18m
Published 1/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 570.66 MB | Duration: 2h 18m
Unlocking Computer Vision: Train Deep Learning Models for Image Classification with Python, Keras and PyTorch
What you'll learn
Master both classical Machine Learning and cutting-edge Deep Learning approaches for state-of-the-art image classification
Design and train advanced CNN architectures including VGG-16, ResNet50, and EfficientNet from scratch
Build end-to-end image classification pipelines from data preprocessing to deployment
Deploy five portfolio-ready Computer Vision projects using Google Colab, PyTorch, and Keras
Master both 1D and 2D Convolutional Neural Networks for image and time series analysis
Master the complete toolkit needed for roles in Data Science and Machine Learning Engineering
Requirements
Basic Programming skills in Python
Description
Master Computer Vision: From Fundamentals to State-of-the-Art Deep LearningTransform your career with cutting-edge Computer Vision skills that top companies are actively seeking. This comprehensive, project-driven course takes you from core concepts to advanced implementations used by industry leaders like Google, Meta, and OpenAI.Why This Course Is DifferentUnlike theoretical courses, you'll build real-world systems from day one. Master the exact tools and techniques used in production environments while building a portfolio that showcases your expertise to potential employers.Your Learning JourneyFoundation ModuleMaster the building blocks of Computer Vision:Transform raw images into powerful feature representationsImplement essential convolution operations used by tech giantsBuild classical ML models (SVM, KNN, Decision Trees) that still power many production systemsDeep Learning MasteryDive into architectures that power today's most advanced AI systems:Master CNNs through hands-on implementationDeploy industry-standard models: VGG-16, ResNet50, InceptionV3, EfficientNetLearn optimization techniques used by top AI researchersReal-World Projects PortfolioBuild five production-grade projects that demonstrate your expertise:Deploy a Deep Learning Model on Google Colab's GPU infrastructureImplement Transfer Learning for lightning-fast model development in KerasCreate a production-ready Image Classifier using PyTorchMaster Time Series Classification with Conv1DBuild advanced image classification systems with 2D Convolutional LayersWho Should Take This CoursePerfect for:Data Scientists seeking to specialize in Computer VisionMachine Learning Engineers expanding their deep learning toolkitOCR Engineers advancing their technical capabilitiesOCR Specialists moving into advanced computer visionSoftware Engineers transitioning to AI developmentTech enthusiasts ready to master professional Computer Vision skillsWhat You'll MasterDesign and deploy production-ready image classification systemsImplement advanced deep learning models using Keras and PyTorchOptimize model performance using transfer learningBuild end-to-end computer vision pipelinesDeploy models in real-world environmentsYour TransformationBy course completion, you'll have:A professional portfolio of five advanced Computer Vision projectsMastery of tools used by leading tech companiesThe ability to build and deploy production-grade AI systemsSkills that command top salaries in the AI industry
Overview
Section 1: Course Onboarding: Your Guide to Success
Lecture 1 Course Starter - How to approach the course
Section 2: Laying the Foundation: Environment Configuration
Lecture 2 Using Pycharm for Coding
Lecture 3 Using Jupyter Notebook and Shortcuts
Lecture 4 Using Google Colab
Section 3: Vision Zero: Fundamentals of Image Classification
Lecture 5 Image Basics
Lecture 6 Image Classification Overview
Lecture 7 Image Classification Pipeline
Lecture 8 Understanding Pixel values and Image representation
Lecture 9 Reading and Exploring Images
Lecture 10 Convolution - Kernel and Stride
Lecture 11 Convolution - Feature Map
Section 4: Traditional Machine Learning Classical Approaches: SVM, KNN & Decision Trees
Lecture 12 Understanding Support Vector Machines (SVM) for Classification
Lecture 13 Decision Trees for Classification
Lecture 14 Understanding K-Nearest Neighbors (KNN)
Section 5: Unleashing the Power: Deep Learning Architectures for Vision
Lecture 15 Exploring Convolutional Neural Networks (CNN)
Lecture 16 The VGG-16 Architecture: A Closer Look
Lecture 17 ResNet50: Revolutionizing Image Classification
Lecture 18 The InceptionV3 Architecture: A Deep Dive
Lecture 19 Unlocking Efficiency: Deep Learning with EfficientNet
Section 6: From Theory to Practice: Real-World Image Classification Projects
Lecture 20 Project 1 - Training a Deep Learning Model using Google Colab
Lecture 21 Project 2 - Transfer Learning Image Classifier using Keras
Lecture 22 Project 3 - Image Classification Model using PyTorch
Lecture 23 Project 4 - Time Series Classification using Conv1D
Lecture 24 Project 5 - CNN based Image Classification Model using 2D Convolutional layers i
Beginners to Computer Vision,OCR Engineer,OCR Specialist,Machine Learning Professionals,Anyone looking to become more employable as a Computer Vision Expert