Mastering Image Classification With Deep Learning

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