AI ML Project Title
Image Classification Using Naive
Project Description
What is this project all about
This project focuses on developing an efficient image classification system using a classical computer vision pipeline. The classification is performed using Support Vector Machines (SVM) after extracting meaningful features from input images using the Histogram of Oriented Gradients (HOG) descriptor.
Scope: Develop an image classification system using HOG for feature extraction and SVM for classification. Focus on efficient, interpretable results using classical computer vision techniques.
Prerequisites
What You Should Know Before Starting
- Intermediate to advanced knowledge of Python or any programming language
- Familiarity with programming logic and fundamental concepts (e.g., loops, functions, arrays)
- General familiarity with how classification or image-based tasks work (optional but helpful)
Tools & Technologies
What Tools & Technologies will be used in this project
- TensorFlow / Keras – For building and training neural networks
- Google Cloud Platform (GCP) – For deploying the final malaria diagnosis model
- Pandas, NumPy – For data handling and numerical computation
- Matplotlib / Seaborn – For data visualization
- Jupyter Notebook / Google Colab / VS Code – As development environments
- OpenCV – For image processing tasks
- scikit-learn – For classical models like SVM and Logistic Regression
- HOG – For image feature extraction
- Support Vector Machine (SVM) – For classification tasks
- Tensors, Gradient Descent – Core concepts in deep learning, aligned with TensorFlow
Learning Objectives
What you will learn in this course
- Understand the difference between image processing and computer vision
- Represent, manipulate, and analyze images as matrices using NumPy and OpenCV
- Extract image features using HOG and build classifiers using SVM and logistic regression
- Grasp core AI concepts such as tensors, gradient descent, and designing ML systems
- Build, train, and evaluate Artificial Neural Networks (ANN) using TensorFlow
- Apply deep learning to solve real-world problems like malaria diagnosis
- Prepare, visualize, and process medical image data for training neural networks
- Deploy a complete deep learning product to Google Cloud Platform (GCP)
LESSON PLAN
🟦 Module 1: Introduction to Computer Vision and Image Processing
- Image Processing vs Computer Vision
- Problems in Computer Vision
- How is an Image Formed
- Digital Images
- Images as Matrix
- Manipulating Pixels
- Color Images
🟦 Module 2: Image Classification
- Introduction
- Histogram of Oriented Gradients (HOG)
- Support Vector Machine (SVM)
- Logistic Regression
🟦 Module 3: Introduction to Artificial Intelligence
- Introduction to AI
- Designing a Machine Learning System
- NumPy
- Introduction to Tensors
- Gradient Descent
🟦 Module 4: Building Artificial Neural Networks (ANN) using TensorFlow
- Understanding Neural Networks
- Building Blocks of Neural Networks
- Bias-Variance Trade-off
- How to Handle Overfitting
🟦 Module 5: Malaria Diagnosis using Deep Learning
- Data Preparation
- Data Visualization
- Data Processing
- Building Neural Network for Diagnosis
- Training
- Testing
- Validation
- Deployment of End-to-End Product to Google Cloud Platform
FAQ
This is not just theory. You’ll build a fully working AI-powered IT support automation system — step-by-step, guided by real professionals. It’s practical, project-based, and portfolio-ready.
If you’ve completed some basic programming (especially through our beginner courses), you’re good to go! If not, start with our foundation courses, then return for this project.
Absolutely. You’ll finish with a deployable IT automation project, integrated with GitHub and real-time logging — a great addition to your resume and GitHub portfolio.
It’s beginner-friendly if you’ve completed basic to intermediate programming. If not, take our intro courses first — they’re designed to prepare you for this exact project.
Just install Python, Node.js, and VS Code — we’ll guide you through setup in the first session. Everything else will be covered during the course.
In just 10 focused hours, you’ll learn email automation, GitHub API, WebSocket, and React integration — tools used by real tech teams today.
Yes, you will receive an industry-recognized certificate.
Yes, we specialize in helping businesses find and hire the right talent. Our HR and recruitment services are tailored to meet your company’s specific hiring needs. Contact us to learn how we can support your hiring process.
Please visit our page : WoCons – Custom AI & CAD Solutions | Bengaluru
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