Flutter & ML : Train Tensorflow Lite models for Flutter Apps

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Hamza Asif

4:50:31

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1. Introduction

1. Introduction.mp4

02:45

2. Course Curriculum.mp4

02:15

2. Machine Learning & Deep Learning

1. Machine Learning Introduction.mp4

03:25

2. Supervised Machine Learning Regression & Classification.mp4

04:42

3. Unsupervised Machine Learning & Reinforcement Learning.mp4

03:21

4. Deep Learning and regression models training.mp4

13:04

5. Basic Deep Learning Concepts.mp4

06:13

3. Python Programming Language

1. Google Colab Introduction.mp4

04:58

2.1 Python.zip

2. Python Introduction & data types.mp4

08:34

3. Python Lists.mp4

06:03

4. Python dictionary & tuples.mp4

03:40

5. Python loops & conditional statements.mp4

03:58

6. File handling in Python.mp4

04:21

4. Data Science Libraries

1.1 DataScience Libraries.zip

1. Numpy Introduction.mp4

05:22

2. Numpy Operations.mp4

04:33

3. Numpy Functions.mp4

04:41

4. Pandas Introduction.mp4

03:29

5. Loading CSV in pandas.mp4

03:13

6. Handling Missing values in dataset with pandas.mp4

03:41

7. Matplotlib & charts in python.mp4

03:21

8. Dealing images with Matplotlib.mp4

02:40

5. Tensorflow

1.1 Tensorflow.zip

1. Tensorflow Introduction Variables & Constants.mp4

05:37

2. Shapes & Ranks of Tensors.mp4

05:23

3. Matrix Multiplication & Ragged Tensors.mp4

05:21

4. Tensorflow Operations.mp4

02:06

5. Generating Random Values in Tensorflow.mp4

06:38

6. Tensorflow Checkpoints.mp4

03:29

6. Training a basic regression model

1. Section Introduction.mp4

02:45

2.1 BasicExample.zip

2. Train a simple regression model for Flutter.mp4

09:56

3. Testing model and converting it to a tflite(Tensorflow lite) format.mp4

03:26

4. Model training for flutter overview.mp4

02:00

5. Creating a new flutter project.mp4

07:14

6. Adding libraries and loading regression models in Flutter.mp4

07:18

7. Passing Input to regression model and getting output in Flutter.mp4

07:33

8. Regression Models Integration in Flutter Overview.mp4

01:41

7. Training a Fuel Efficiency Prediction Model

1. Section Introduction.mp4

02:40

2.1 Fuel Efficiency Prediction.zip

2. Getting datasets for training regression models.mp4

04:56

3. Loading dataset in python with pandas.mp4

07:45

4. Handling Missing Values in Dataset.mp4

03:24

5. One Hot Encoding Handling categorical columns.mp4

04:38

6. Training and testing datasets.mp4

04:19

7. Normalization Bringing all columns to a common scale.mp4

02:40

8. Training a fuel efficiency prediction model.mp4

07:04

9. Testing fuel efficiency prediction model and converting it to a tflite format.mp4

04:20

10. Fuel Efficiency Model Training Overview.mp4

04:30

8. Fuel Efficiency Prediction Flutter Application

1. Analyse trained fuel efficiency prediction model.mp4

02:37

2. Set Up Starter Application for Fuel Efficiency Prediction.mp4

03:51

3. What we have done so far.mp4

06:21

4. Loading Tensorflow Lite model in Flutter for fuel efficiency prediction.mp4

03:49

5. Normalizing user inputs in Flutter before passing it to our model.mp4

05:51

6. Passing Input to our model and getting output.mp4

05:14

7. Testing Fuel Efficiency Prediction Flutter Application.mp4

03:02

8. Fuel Efficiency Prediction Flutter Overview.mp4

02:43

9. Training House Price Prediction Model

1. Section Introduction.mp4

01:57

2.1 House Price Prediction.zip

2. Getting house price prediction dataset.mp4

03:45

3. Load dataset for training house price prediction regression model.mp4

07:19

4. Training & evaluating house price prediction model.mp4

06:30

5. Retraining price prediction model.mp4

04:04

10. House Price Prediction Flutter Application

1. Analysing house price prediction tensorflow lite model.mp4

01:28

2. Loading house price prediction model in Flutter.mp4

06:30

3. Passing input to tensorflow lite model and getting output.mp4

07:24

4. Testing house price prediction Flutter Application.mp4

03:04

Description

Train countless Machine Learning Models for Flutter Application | Use Tensorflow Lite models in Flutter | Flutter ML

What You'll Learn?

Train Machine Learning models for Flutter Applications
Integrate Machine Learning models in Flutter for both Android & IOS
Train a machine learning model and build a house price prediction Flutter Application
Train a machine learning model and build a fuel efficiency prediction Flutter Application
Use of Tensorflow Lite models in Flutter
Train Any Prediction Model & use it in Flutter Applications
Data Collection & Preprocessing for ML model training for Flutter Application
Basics of Machine Learning & Deep Learning for training Machine learning Models
Understand the working of artificial neural networks for training machine learning for Flutter
Basic syntax of python programming language to train ML models for Flutter
Use of data science libraries like numpy, pandas and matplotlib
Analysing & using advance regression models in Flutter Applications

Who is this for?

Beginner Flutter Developer who want to build Machine Learning based Flutter Applications

Aspiring Flutter developers eager to add predictive modeling to their skillset

Enthusiasts seeking to bridge the gap between Machine Learning and mobile app development.

Machine Learning Engineers looking to build real world applications with Machine Learning Models

What You Need to Know?

Android studio & Flutter installed in your PC

More details

Description
Do you want to train different Machine Learning models and build smart Android & IOS applications in Flutter then Welcome to this course.

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
to predict the price of the house
to predict the Fuel Efficiency of vehicles
to recommend drug doses for medical conditions
to recommend fertilizer in agriculture
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 regression models in 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 regression 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

Course Highlights:
Training Your First Regression Model:
Harness TensorFlow and Python to create a simple regression model
Convert the model into TFLite format, making it compatible with Flutter
Learn to integrate the regression model into Flutter apps for Android and iOS
Fuel Efficiency Prediction:
Apply your knowledge to a real-world problem by predicting automobile fuel efficiency
Seamlessly integrate the model into a Flutter app for an intuitive fuel efficiency prediction experience
House Price Prediction in Flutter:
Master the art of training regression models on substantial datasets
Utilize the trained model within your Flutter app to predict house prices confidently

The Flutter Advantage: By the end of this course, you'll be equipped to:
Train advanced regression models for accurate predictions
Seamlessly integrate regression models into your Flutter applications
Analyze and use existing regression models effectively within the Flutter ecosystem

Who Should Enroll:
Aspiring Flutter developers eager to add predictive modeling to their skillset
Enthusiasts seeking to bridge the gap between Machine Learning and mobile app development
Data aficionados interested in harnessing the potential of data for real-world applications

Step into the World of Flutter and Predictive Modeling: Join us on this exciting journey and unlock the potential of Flutter and Linear Regression. 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!
Who this course is for:
Beginner Flutter Developer who want to build Machine Learning based Flutter Applications
Aspiring Flutter developers eager to add predictive modeling to their skillset
Enthusiasts seeking to bridge the gap between Machine Learning and mobile app development.
Machine Learning Engineers looking to build real world applications with Machine Learning Models

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Experienced Mobile Developer, specialized in Mobile Machine Learning using Tensorflow lite, ML Kit, and Google cloud vision API. Leading Android Machine learning instructor with over 50,000 students from 150 countries. I am an enthusiastic developer with a strong programming background and possess great app development skills. I have developed a bunch of native and cross-platform apps in the past and satisfied all of my clients. It has been +4 years doing Mobile development and providing support for Android Applications. Empowering mobile Applications using Machine Learning and Computer vision is my core skill.Powering Android Application with ML really fascinates me. So I learned Android development and then Machine Learning. I developed Android applications for several multinational organizations. Now I want to spread the knowledge I have. I'm always thinking about how to make difficult concepts easy to understand, what kind of projects would make a fun tutorial, and how I can help you succeed through my courses.

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