Flutter AI & TensorFlow Lite – Train ML Models & Build Apps

Explore the basics of machine learning, a branch of AI that uses data and algorithms to imitate human learning and make predictions, with dog breeds and salary examples.

Explore the three main types of machine learning—supervised, unsupervised, and reinforcement—while mastering terms like dataset, features, label, and training and testing data, including a salary-prediction example.

Explore supervised machine learning's two main types: regression and classification. Learn how continuous salary prediction uses experience and certifications, while gender prediction uses height and weight as inputs.

Explore unsupervised machine learning using unlabeled data to discover patterns through clustering, then examine reinforcement learning as trial-and-error training for AI agents like self-driving cars and robotic dogs.

Explore how deep learning uses artificial neural networks to extract patterns from data, using input, hidden, and output layers to perform classification and regression tasks.

Learn how a neural network predicts house prices from area and rooms by training with input features, weights, and epochs. Explore feed forward, backpropagation, and testing to build a model.

Explore how neural networks classify images by converting them to 1d arrays, feeding them to the input layer, and training via backpropagation to recognize dog breeds.

Explore core deep learning concepts, including supervised and unsupervised learning, feed forwarding, backpropagation, learning rate, loss function, and dropout to combat overfitting.

Explore Google Colab as a browser-based, zero-configuration Python environment, with code and text cells, GPU access, notebook sharing, and basic operations for training machine learning models in Flutter apps.

Explore Python basics, including variables and data types such as numbers, strings, lists, tuples, and dictionaries, and learn to use Google Colab notebooks for model training.

Explore Python numbers by declaring variables, printing values, deleting with the del keyword, and inspecting types for integer, long, float, and complex numbers with real and imaginary parts.

Explore Python strings: declare with single quotes, print values, index and slice (start to end, end not inclusive), repeat strings, and concatenate with the plus operator.

Explore Python lists, the versatile data type that holds mixed elements in square brackets and supports indexing, slicing, nested lists, and key methods like append and sort.

Learn how Python tuples differ from lists, are enclosed in parentheses, immutable, and accessed by index. Learn how dictionaries use unique keys with curly braces to store and retrieve values.

Explore Python conditional statements and loops, using if, elif, else, while, and for loops, with examples of list and string iteration and user input handling.

Learn how to read and write files in Python using the open function while training a machine learning model, including saving models in tflite or other formats and file modes.

Learn numpy, the Python library for creating and operating on multi-dimensional arrays, including importing as np, building 1-D and 2-D arrays, and inspecting shape, size, and dtype for data preparation.

Explore NumPy arrays by creating zeros, ones, and random arrays, and filling them with a value, and reshaping and flattening to generate dummy data for TensorFlow Lite models in Flutter.

Explore NumPy unary and binary operators, compute min, max, and sum, and perform element-wise operations plus dot products between arrays.

This lecture covers matrix multiplication in NumPy using dot or matmul on arrays treated as matrices, and sorting two-dimensional arrays with NumPy sort.

Explore pandas for data manipulation in Python, learn to create data frames from lists and dictionaries, and perform basic table operations for machine learning workflows in Colab.

Load a csv into a pandas data frame using read_csv, specify read mode and file path, upload the NBA.csv file, and inspect the 458-row, 9-column dataset for future model training.

Identify missing values in a dataset with pandas using isnull, fill them with a value or drop them with dropna, and check the data frame shape.

Master Matplotlib for visualizing data in Python with 2D plots, including line, bar, and scatter charts, using plt in Google Colab examples.

Load and visualize images in a Python notebook using matplotlib, view images with imshow, and inspect image data as NumPy arrays to understand shapes and values for ML workflows.

Learn how TensorFlow and TensorFlow Lite enable machine learning on mobile, web, and desktop, and declare variables and constants with tf.variable and tf.constant, inspecting shapes, dtypes, and updates.

Learn how to inspect and manipulate tensors in TensorFlow Lite, including shaping and reshaping a 2x3 tensor, querying rank, indexing elements, converting to NumPy, and performing element-wise operations.

Explore matrix multiplication in TensorFlow using tf dot matmul with a transposed second matrix. Learn to change tensor data types and create ragged tensors for variable row lengths.

Explore how to compute means in TensorFlow Lite using tf.reduce_mean on scalar and multi-element tensors, including axis-based mean for 2d tensors.

Generate random tensors in TensorFlow with tf.random.normal and tf.random.uniform by shaping, mean, and standard deviation, then add and concatenate tensors and locate min and max indices with argmax and argmin.

Save and restore model training progress with TensorFlow checkpoints by storing thousands of variable values and restoring them to resume training.

Explore TensorFlow Lite, an on-device machine learning library that runs models offline on mobile, highlighting converter and interpreter benefits like latency, privacy, and power efficiency on Android and iOS.

Train a simple linear regression model on a dataset where output equals two times input minus one, and integrate it into a Flutter app to predict outputs.

Train your first machine learning model with TensorFlow and Keras, convert it to TensorFlow Lite for mobile apps, and test a simple regression with y = 2x - 1.

Test the trained model with inputs like 100 and 20 to confirm outputs near 199 and 39, then convert to TensorFlow Lite and save as linear.tflite for Flutter.

Describe training a simple flutter app model: create a dataset, build a one-layer one-neuron network, set optimizer and loss, train with epochs, test, and convert to tflite.

Install the flutter sdk on Mac, set up a development folder, and add flutter to your path, then verify installation with flutter version to complete the environment setup.

Install Android Studio to build Flutter apps by downloading from developer.android.com/studio and installing the Flutter plugin.

Install and set up Xcode on your Mac via the App Store to run Flutter apps on iOS and Mac devices, then configure command line tools in Xcode preferences.

Create a Flutter project in Android Studio, select Kotlin for Android and Swift for iOS, and run the default counter app on iOS simulator and Android emulator.

Learn to create and run an Android emulator on macOS, download Android 14 for a Pixel 7, configure orientation, launch the emulator, and install the starter application.

Install flutter on windows by downloading the flutter sdk, extracting it, and adding its bin path to system environment variables. Then run flutter doctor to verify installation from anywhere.

Install android studio, set up flutter with the dart and flutter plugins, run flutter doctor, and install android sdk command line tools to accept licenses.

Create an Android virtual device in Android Studio's Virtual Device Manager, choose a device such as Pixel 7 Pro, download Android 14, finish, and launch the emulator to test apps.

Create a new flutter project and build a regression app using the model y = 2x - 1, with a text field and a button to update a text widget.

Add the Tflite flutter package to your flutter project and declare an assets folder with your model. Load the TensorFlow Lite interpreter in init state to run a regression model.

Learn how to pass user input from a text field to a regression model in a Flutter app using TensorFlow Lite, run inference, and display the prediction.

Learn how to load and run a simple regression model in Flutter using the TensorFlow Lite Flutter package, including asset setup, pubspec.yaml, interpreter creation, and single-value input/output inference.

train a regression model to predict fuel efficiency from automotive data, including data collection and preprocessing with normalization and one hot encoding, then convert to tflite for flutter integration.

Explore training a real-world regression model to predict car fuel efficiency (mpg) using features like cylinders, displacement, horsepower, and weight, sourcing the auto mpg dataset from Kaggle or course resources.

Load a csv into a pandas data frame to train a fuel efficiency model, handling missing values and selecting relevant columns. Inspect the data with the tail to verify results.

Identify and handle missing values in the dataset before training by checking per-column counts with isna and sum, then fill with a value or drop incomplete rows with dropna.

Apply one hot encoding to convert the origin categorical column into binary USA, Europe, and Japan columns, removing the original column, to improve model accuracy during training.

Split the dataset into 80/20 training and testing sets, then describe column statistics for normalization; separate the miles per gallon label from features to prepare for model training.

Apply normalization to datasets by bringing numeric features to a common scale, dividing values by their max, so area and rooms train without one dominating the model.

Learn to normalize data by subtracting each column's mean and dividing by its standard deviation, applying the operation to all columns in training and test sets using stored train statistics.

Normalize the training and testing data and build a TensorFlow Keras sequential model for Flutter fuel efficiency. Train with 64-neuron hidden layers, monitor MSE and MAE, and run 1000 epochs.

Test and evaluate a fuel efficiency model with a 20% test split, assess loss with mean absolute error and mean square error, then convert the model to tflite for Flutter.

Train a real-world regression model on a Kaggle dataset in Colab, clean data, one-hot encode region, split 80/20, normalize, train and evaluate, then convert to TFLite for mobile use.