1 -Introduction (18.87 MB)
1 -Section Introduction (9.44 MB)
10 -Testing fuel efficiency prediction model and converting it to a tflite format (45.93 MB)
11 -Fuel Efficiency Model Training Overview (50.83 MB)
2 -Getting datasets for training regression models for Flutter (49.43 MB)
3 -Loading dataset in python with pandas (78.13 MB)
4 -Handling Missing Values in Dataset (33.31 MB)
5 -One Hot Encoding Handling categorical columns (46.66 MB)
6 -Training and testing datasets (48.15 MB)
7 -Normalization Introduction (11.91 MB)
8 -Normalization Bringing all columns to a common scale (27.97 MB)
9 -Training a fuel efficiency prediction model for Flutter (73.39 MB)
1 -Analyse trained fuel efficiency prediction model for Flutter (16.22 MB)
2 -Setup Starter Flutter Application for Fuel Efficiency Prediction (44.36 MB)
3 -What we have done so far (86.64 MB)
4 -Loading Tensorflow Lite model in Flutter for fuel efficiency prediction (47.36 MB)
5 -Normalizing user inputs in Flutter before passing it to our model (70.2 MB)
6 -Passing Input to our model and getting output in Flutter Application (66.58 MB)
7 -Testing Fuel Efficiency Prediction Flutter Application (32.44 MB)
8 -Fuel Efficiency Prediction Flutter Overview (38.34 MB)
1 -Section Introduction (6.75 MB)
2 -Getting house price prediction dataset (39.51 MB)
3 -Load dataset for training house price prediction regression model for Flutter (83.13 MB)
4 -Training & evaluating house price prediction model for Flutter (78.16 MB)
5 -Retraining price prediction model (46.89 MB)
1 -Analysing house price prediction tensorflow lite model (9.46 MB)
2 -Loading house price prediction model in Flutter (82.11 MB)
3 -Passing input to tensorflow lite model and getting output (95.27 MB)
4 -Testing house price prediction Flutter Application (35.94 MB)
1 -Image Classification Introduction (20.74 MB)
10 -Attaching Metadata With a Trained Tensorflow Lite Model (76.52 MB)
2 -Object Detection Introduction (42.2 MB)
3 -Section Introduction (5.59 MB)
4 -Dataset Collection Introduction (13.29 MB)
5 -Downloading Dataset for Training our first Image Classification Model (56.51 MB)
6 -Training Our First Custom Image Classification Model for Flutter (39.69 MB)
7 -Testing Our Own Trained Model and Converting it to tflite format (33.01 MB)
8 -Hyperparameter tuning Improving Model Accuracy (29.48 MB)
9 -Google Colab Introduction (46.78 MB)
1 -Section Introduction (8.92 MB)
2 -Uploading dataset on drive for model training (26.69 MB)
3 -Uploading model training code and importing libraries in Colab (49.36 MB)
4 -Loading dataset in python and doing train, test & split (53.35 MB)
5 -Training Image Classification Model using Transfer Learning (82.19 MB)
6 -Testing Model and converting it to tflite format (18.74 MB)
7 -Hyperparameter tuning Improving model accuracy (29.99 MB)
8 -Training EfficientNet Model using transfer learning for Flutter (43.14 MB)
9 -Training Custom Models for Flutter Applications Overview (14.17 MB)
1 -Creating a new Flutter Project and building GUI of ImagePicker Application (76.08 MB)
2 -Adding Libraries and doing Android & IOS configuration in Flutter (47.79 MB)
3 -Choosing Images From Gallery in Flutter (65.58 MB)
4 -Capturing Images using Camera in Flutter (26.04 MB)
5 -Testing ImagePicker Flutter Application (4.23 MB)
6 -ImagePicker in Flutter Overview (18.73 MB)
1 -Section Introduction (18.52 MB)
2 -Setting up Image Classification with Images Flutter Project (70.11 MB)
3 -Adding libraries and setting up Android & IOS (81.04 MB)
4 -Performing Image Classification in Flutter With Images (100.82 MB)
5 -Using our custom trained image classification models in Flutter with Images (131.81 MB)
6 -Using our trained MobileNet model in Flutter (23.08 MB)
7 -Android Additional Step (12.49 MB)
8 -Image Classification in Flutter with Images Overview (28.13 MB)
1 -Creating a new Flutter project and setting up libraries (59.5 MB)
2 -Displaying live camera footage in Flutter for Android and IOS (92.25 MB)
3 -Realtime Flutter Overview (30.55 MB)
1 -Importing starter application for realtime Image Classification in Flutter (81.97 MB)
2 -Android & IOS configurations for realtime Image Classification with Flutter (41.27 MB)
3 -Converting frames of live camera footage into Inputimage (82.78 MB)
4 -Processing frames of live camera footage in realtime in Flutter (34.76 MB)
5 -Testing Realtime Image Classification Flutter Application (14.38 MB)
6 -Using our own trained Image Classification model in Flutter with live feed (79.76 MB)
7 -Testing Realtime Custom Image Classification Application (24.78 MB)
8 -Realtime Image Classification in Flutter Overview (40.62 MB)
1 -What is Machine Learning (12.06 MB)
2 -Supervised Machine Learning (14.33 MB)
3 -Regression and Classification (8.92 MB)
4 -Unsupervised Machine Learning & Reinforcement Learning (18.36 MB)
5 -Deep Learning and Neural Network Introduction (21.1 MB)
6 -Neural Network Example (44.82 MB)
7 -Working of Neural Networks for Image Classification (20.8 MB)
8 -Basic Deep Learning Concepts (19.08 MB)
1 -Section Introduction (27.63 MB)
2 -Setting Up Object Detection in Flutter Project (57.86 MB)
3 -Making Configurations for Android & IOS to perform Object Detection in Flutter (81.94 MB)
4 -Creating Object Detector in Flutter (57.35 MB)
5 -Performing Object Detection In Flutter With images (62.35 MB)
6 -Drawing Rectangles Around Detected Objects in Flutter (110.11 MB)
7 -How Rectangles are being Drawn Around Detected Objects (76.99 MB)
8 -Using Custom Image Classifier With Object Detector In Flutter (99.62 MB)
9 -Testing Custom Object Detection Flutter Application (23.55 MB)
1 -Setting up realtime object detection flutter application (26.34 MB)
2 -Displaying live camera footage in Flutter and getting frames (93.17 MB)
3 -Performing object detection in flutter live camera footage (47.28 MB)
4 -Drawing rectangles around detected objects in realtime (67.97 MB)
5 -Testing Realtime Object Detection Application (25.48 MB)
6 -Using custom image classification model with object detection model (85.23 MB)
7 -Testing realtime object detection application with our custom models (17.51 MB)
8 -Object Detector Painter (73.57 MB)
9 -Realtime Object Detection in Flutter Overview (36.98 MB)
1 -Google Colab Introduction (58.51 MB)
2 -Python Introduction & data types (31.57 MB)
3 -Python Numbers (21.06 MB)
4 -Python Strings (24 MB)
5 -Python Lists (59.21 MB)
6 -Python dictionary & tuples (28.42 MB)
7 -Python loops & conditional statements (40.03 MB)
8 -File handling in Python (39.23 MB)
1 -Numpy Introduction (47.87 MB)
2 -Numpy Functions and Generating Random Values (29.73 MB)
3 -Numpy Operators (19.41 MB)
4 -Matrix Multiplications and Sorting in Numpy (19.3 MB)
5 -Pandas Introduction (37.77 MB)
6 -Loading CSV in pandas (21.25 MB)
7 -Handling Missing values in dataset with pandas (38.17 MB)
8 -Matplotlib & charts in python (32.21 MB)
9 -Dealing images with Matplotlib (28.3 MB)
1 -Tensorflow Introduction Variables & Constants (55.44 MB)
2 -Shapes & Ranks of Tensors (62.14 MB)
3 -Matrix Multiplication & Ragged Tensors (41.48 MB)
4 -Tensorflow Operations (18.76 MB)
5 -Generating Random Values in Tensorflow (57.01 MB)
6 -Tensorflow Checkpoints (30.08 MB)
7 -Tensorflow Lite Introduction & Advantages (19.41 MB)
1 -Section Introduction (8.28 MB)
2 -Train a simple regression model for Flutter (86.71 MB)
3 -Testing model and converting it to a tflite(Tensorflow lite) format for Flutter (27.32 MB)
4 -Model training for flutter app development overview (17.6 MB)
1 -Install the Flutter SDK (83.85 MB)
2 -Install Android Studio (54.63 MB)
3 -Install and Setup XCode (52.7 MB)
4 -Creating A Flutter Project and Installing in IOS Simulator (48.55 MB)
5 -Install the Android Emulator (34.05 MB)
1 -Installing Flutter on Windows (41.9 MB)
2 -Installing Android Studio (55.47 MB)
3 -Creating Android Virtual Device (18.88 MB)
1 -Creating a new flutter project (92.59 MB)
2 -Adding libraries and loading regression models in Flutter (83.96 MB)
3 -Passing Input to regression model and getting output in Flutter (88 MB)
4 -Regression Models Integration in Flutter Overview (19.64 MB)