Flutter & ML : Train Tensorflow Lite models for Flutter Apps
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Do you want to train different Machine Learning models and build smart Android & IOS applications in Flutter then Welcome to this course.
In this course, you will learn to
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train powerful image classification, object detection, and linear regression models in Python from scratch
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Then we will use these models in Flutter to build smart Flutter Apps
Regression is one of the fundamental techniques in Machine Learning which can be used for countless applications. Like you can train Machine Learning models using regression
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to predict the price of the house
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to predict the Fuel Efficiency of vehicles
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to recommend drug doses for medical conditions
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to recommend fertilizer in agriculture
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to suggest exercises for improvement in player performance
and so on. So Inside this course, you will learn to train your custom machine learning models for Flutter and build smart Android & IOS applications in Flutter.
I’m Muhammad Hamza Asif, and in this course, we’ll embark on a journey to combine the power of predictive modeling with the flexibility of Flutter app development. Whether you’re a seasoned Flutter developer or new to the scene, this course has something valuable to offer you
Course Overview: We’ll begin by exploring the basics of Machine Learning and its various types, and then dive into the world of deep learning and artificial neural networks, which will serve as the foundation for training our machine learning models for Flutter.
The Flutter-ML Fusion: After grasping the core concepts, we’ll bridge the gap between Flutter and Machine Learning. To do this, we’ll kickstart our journey with Python programming, a versatile language that will pave the way for our machine learning model training
Unlocking Data’s Power: To prepare and analyze our datasets effectively, we’ll dive into essential data science libraries like NumPy, Pandas, and Matplotlib. These powerful tools will equip you to harness data’s potential for accurate predictions.
Tensorflow for Mobile: Next, we’ll immerse ourselves in the world of TensorFlow, a library that not only supports model training using neural networks but also caters to mobile devices, including Flutter
Regression Models Training
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Training Your First Machine Learning Model:
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Harness TensorFlow and Python to create a simple linear regression model
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Convert the model into TFLite format, making it compatible with Flutter
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Learn to integrate the tflite model into Flutter apps for Android and iOS
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Fuel Efficiency Prediction:
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Apply your knowledge to a real-world problem by predicting automobile fuel efficiency
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Seamlessly integrate the model into a Flutter app for an intuitive fuel efficiency prediction experience
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House Price Prediction in Flutter:
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Master the art of training machine learning models on substantial datasets
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Utilize the trained model within your Flutter app to predict house prices confidently
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Computer Vision Model Training
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Image Classification in Flutter:
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Collect and process dataset for model training
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Train image classification models on custom datasets
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Use image classification models in Flutter with both images and live camera footage
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Object Detection in Flutter
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Use object detection models of ML Kit in Flutter
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Train Custom Image Classification models and use them to classify detected objects
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Perform Object Detection with both Images and Live Camera Footage
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The Flutter Advantage: By the end of this course, you’ll be equipped to:
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Train advanced machine learning models for accurate predictions
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Seamlessly integrate tflite models into your Flutter applications
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Analyze and use existing regression & vision (ML) models effectively within the Flutter ecosystem
Who Should Enroll:
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Aspiring Flutter developers eager to add predictive modeling to their skillset
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Beginner Flutter ( Dart ) developer with very little knowledge of mobile app development in Google Flutter
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Intermediate Flutter ( Dart ) developer wanted to build a powerful Machine Learning-based application in Google Flutter
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Experienced Flutter ( Dart ) developers wanted to use Machine Learning models inside their applications.
Step into the World of Flutter and Predictive Modeling: Join us on this exciting journey and unlock the potential of Flutter and Machine Learning. By the end of the course, you’ll be ready to develop Flutter applications that not only look great but also make informed, data-driven decisions.
Enroll now and embrace the fusion of Flutter and predictive modeling!
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2What is Machine LearningVídeo Aula
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3Supervised Machine LearningVídeo Aula
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4Regression and ClassificationVídeo Aula
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5Unsupervised Machine Learning & Reinforcement LearningVídeo Aula
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6Deep Learning and Neural Network IntroductionVídeo Aula
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7Neural Network ExampleVídeo Aula
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8Working of Neural Networks for Image ClassificationVídeo Aula
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9Basic Deep Learning ConceptsVídeo Aula
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10Google Colab IntroductionVídeo Aula
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11Python Introduction & data typesVídeo Aula
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12Python NumbersVídeo Aula
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13Python StringsVídeo Aula
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14Python ListsVídeo Aula
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15Python dictionary & tuplesVídeo Aula
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16Python loops & conditional statementsVídeo Aula
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17File handling in PythonVídeo Aula
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18Numpy IntroductionVídeo Aula
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19Numpy Functions and Generating Random ValuesVídeo Aula
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20Numpy OperatorsVídeo Aula
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21Matrix Multiplications and Sorting in NumpyVídeo Aula
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22Pandas IntroductionVídeo Aula
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23Loading CSV in pandasVídeo Aula
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24Handling Missing values in dataset with pandasVídeo Aula
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25Matplotlib & charts in pythonVídeo Aula
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26Dealing images with MatplotlibVídeo Aula
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27Tensorflow Introduction | Variables & ConstantsVídeo Aula
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28Shapes & Ranks of TensorsVídeo Aula
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29Matrix Multiplication & Ragged TensorsVídeo Aula
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30Tensorflow OperationsVídeo Aula
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31Generating Random Values in TensorflowVídeo Aula
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32Tensorflow CheckpointsVídeo Aula
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33Tensorflow Lite Introduction & AdvantagesVídeo Aula
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50Section IntroductionVídeo Aula
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51Getting datasets for training regression models for FlutterVídeo Aula
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52Loading dataset in python with pandasVídeo Aula
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53Handling Missing Values in DatasetVídeo Aula
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54One Hot Encoding: Handling categorical columnsVídeo Aula
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55Training and testing datasetsVídeo Aula
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56Normalization IntroductionVídeo Aula
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57Normalization: Bringing all columns to a common scaleVídeo Aula
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58Training a fuel efficiency prediction model for FlutterVídeo Aula
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59Testing fuel efficiency prediction model and converting it to a tflite formatVídeo Aula
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60Fuel Efficiency Model Training OverviewVídeo Aula
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61Analyse trained fuel efficiency prediction model for FlutterVídeo Aula
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62Setup Starter Flutter Application for Fuel Efficiency PredictionVídeo Aula
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63What we have done so farVídeo Aula
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64Loading Tensorflow Lite model in Flutter for fuel efficiency predictionVídeo Aula
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65Normalizing user inputs in Flutter before passing it to our modelVídeo Aula
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66Passing Input to our model and getting output in Flutter ApplicationVídeo Aula
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67Testing Fuel Efficiency Prediction Flutter ApplicationVídeo Aula
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68Fuel Efficiency Prediction Flutter OverviewVídeo Aula
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69Section IntroductionVídeo Aula
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70Getting house price prediction datasetVídeo Aula
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71Load dataset for training house price prediction regression model for FlutterVídeo Aula
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72Training & evaluating house price prediction model for FlutterVídeo Aula
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73Retraining price prediction modelVídeo Aula
