Train Image Classification Models & Build Flutter Apps 2025

Explore how machine learning uses data and algorithms to imitate human learning, train models with data, recognize patterns, and make predictions like dog breed or salary forecasts.

Explore the three main types of machine learning—supervised, unsupervised, and reinforcement—and learn key terms like dataset, dataset collection, training data, testing data, label, and feature.

Explore supervised learning by contrasting regression and classification: regression predicts continuous values like salary from experience, while classification predicts categorical outcomes like gender from height and weight.

Explore unsupervised machine learning with clustering on unlabeled data to discover patterns, such as customer purchases, and learn reinforcement learning through trial-and-error feedback.

Explore how deep learning uses artificial neural networks and neurons to recognize patterns by training data through input, hidden, and output layers for classification and regression.

Explore how a neural network trains on housing data by feeding area and rooms into an input layer, updating weights via backpropagation across epochs to predict prices.

Explore how neural networks perform image classification by converting 28 by 28 images into a 1D 784-element input array and training with backpropagation to identify dog breeds.

Explore how machine learning finds patterns in data and contrasts supervised and unsupervised learning. Examine deep learning with feed forwarding, back propagation, learning rate, loss, and dropout.

Learn how to collect image data for training an image classification model. Explore existing datasets, web scraping, manual collection, and data augmentation to build datasets with 50–500 images per class.

Find ready-to-use image datasets for training fruit and vegetable classifiers by exploring Kaggle datasets, evaluating train, test, and validation folders, and downloading with sign-in.

Unzip the downloaded archive, inspect the train, test, and validation folders, and create a dataset with apple, banana, watermelon, onion, lemon, and potato, then zip it for training.

Train a custom image classification model with Teachable Machine without coding by simply uploading a dataset and training with a single click, then export to TensorFlow Lite for mobile apps.

Upload your dataset to teachable machine, train an image classification model without coding, and label classes like Apple, Banana, Lemon, Onion, Watermelon, and Potato for mobile use.

Learn to train image classification models using epoch, batch size, and learning rate, test with file or webcam, and export to TensorFlow Lite for mobile apps.

Learn how to attach class labels to a TensorFlow Lite model by embedding metadata using a short Python workflow in Google Colab, enabling a single file for mobile app integration.

Discover Google Colab, a browser-based Python environment with zero configuration that enables training machine learning models for mobile apps using code cells and GPUs.

Learn to attach metadata to a tflite image classification model in google colab, using a notebook, uploading model and label files, installing the tflite library, and downloading a ready-to-use fruits.tflite.

Train custom image classification models with transfer learning, starting from a source model and retraining on a dataset, then test accuracy in Colab and convert to Tflite for Flutter.

Learn to use Google Colab to run Python code in your browser with zero configuration and no software installation, access free GPUs, and manage notebooks with code and text cells.

Upload the 2024 training image classification notebook to Google Colab, install and import libraries with pip and MediaPipe model maker, train and convert to TensorFlow Lite.

Upload dataset.zip to google drive and mount it in colab, then unzip and train. Alternatively, upload the zip directly to colab, mount drive, access my drive, unzip, and start training.

Divide the dataset into training, validation, and testing sets using a dataset object and split functions, allocating 90% to training and 5% each to validation and testing.

Retrain the Efficientnet lite two model via transfer learning, configure hyperparameters and 50 epochs, and monitor training with training and validation loss and accuracy to build a custom image classifier.

Evaluate the trained image classifier on the test set, achieving 91% accuracy with low loss. Export to TensorFlow Lite and download the .tflite file for a mobile Flutter app.

Learn to deploy custom image classification models in Flutter for Android and iOS, using TensorFlow Lite and Google ML Kit for image and live camera labeling.

Set up the Flutter development environment on Mac by downloading the Flutter SDK, extracting it to a development folder, adding Flutter to your path, and verifying installation with flutter version.

Install android studio, download the latest android sdk components, and install the flutter plugin to enable creating flutter projects, concluding with the flutter project button appearing in the IDE.

Install and set up Xcode to run Flutter apps on iOS and macOS, using Android Studio, App Store downloads, and configuring command line tools.

Create a new flutter project in Android Studio, select Kotlin for Android and Swift for iOS, and install the starter counter app on the iOS simulator and Android emulator.

Install and run the Android emulator to test your starter Android app by creating a Pixel 7 emulator with Android 14, downloading the zip, and installing the application.

Learn to install and configure the Flutter SDK on Windows, set up the environment variables, and verify the installation with Flutter doctor to enable Flutter app development.

Install Android Studio, configure flutter with Dart and flutter plugins, restart the IDE, and run flutter doctor to verify tool setup and licenses.

Create an Android Virtual Device in Android Studio to test apps on Windows PC. Use the Virtual Device Manager to choose a device, download Android 14, and launch the emulator.

Create a Flutter image picker app that lets users choose from gallery or capture with camera, then display the selected image for use with machine learning models.

Install the image picker library from pub.dev, add it to pubspec.yaml, and configure iOS permissions; Android requires no extra setup for gallery and camera access in Flutter.

Learn to implement an image picker in Flutter to choose images from the gallery, initialize it in init state, and display the selected image on screen.

Capture images in flutter by long-pressing a button to open the camera, using the image picker with source set to camera, and display the captured image on screen.

Learn how to build a Flutter image picker app using the image picker library to select from the gallery or capture with the camera, and display the chosen image.