PickAClass · AI Applications (Interdisciplinary) · AI for Agriculture (AgriTech)

Automated Pest & Disease Detection

Build and deploy computer vision models to automatically detect and identify pests, weeds, and diseases from images, enabling early intervention and targeted treatments.

Automated Pest & Disease Detection

Python Descriptors: Building Custom Attributes

Python Custom Exceptions for Robust Validation

Inspect Python Objects: Types, Lengths, and Debugging Utilities

Foundations of Pairwise Matching and Binary Classification

JAX and Flax Fundamentals: Loss and Activation Functions

Object-Oriented Python for Distance Metrics in Machine Learning

Distributed Training with JAX and Flax for Image Classification

Python Retry Decorators: Build Robust Error Handling

Support Vector Machines for Classification: A Beginner's Guide

Working with the TMDB API in Python: Fetching TV Show and Cast Data

Elixir Process Graphs: Building Dynamic and Supervised Networks

Handling Imbalanced Datasets in Python Machine Learning

Image Classification in Python with KNN, SVM, and Decision Trees

Python Object State Management: Designing Samples and Transitions

Building GraphCompute Systems with Elixir Supervision Trees

Python API Integration: Fetching TV Show Data with TMDB

Soft-Margin Support Vector Machines for Classification

Building a Student Performance Tracker with Python Iterators

Building Image Data Pipelines in JAX

Python Pickle: Customizing Dynamic Object Serialization

Python Testing with pytest Marks and Test Selection

Designing Real-Time Fraud Detection Systems

Introduction to Support Vector Machines and Support Vectors

Support Vector Machines (SVM) for Beginners