Android & Linear Regression: Train ML models for Android

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Hamza Asif

4:48:57

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1. Introduction

1. Introduction.mp4

03:11

2. Machine Learning & Deep Learning Introduction

1. What is Machine Learning.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 A simple overview

1. Google Colab.mp4

04:58

2. Python Introduction & its datatypes.mp4

08:34

3. Lists in Python.mp4

06:03

4. Dictionary and Tuples in Python.mp4

03:40

5. Loops and Conditional Statements in Python.mp4

03:58

6. File Handling In Python.mp4

04:21

4. Data Science Libraries Numpy, Pandas, Matplotlib

1. Numpy Library.mp4

05:22

2. Operations in Numpy.mp4

04:41

3. Functions in Numpy.mp4

04:33

4. Pandas library.mp4

03:29

5. Loading CSV Files in Pandas.mp4

03:13

6. Handling missing values in Pandas dataset.mp4

03:41

7. Matplotlib library.mp4

03:21

8. Images in Matplotlib.mp4

02:40

5. Tensorflow and Tensorflow Lite

1. Tensorflow Variables & Constants.mp4

05:37

2. Tensorflow Shapes & Ranks of Tensors.mp4

05:23

3. Ragged Tesnors & Matrix Multiplication in Tensorflow.mp4

05:21

4. Tensorflow Operations.mp4

02:06

5. Random Values in Tensorflow.mp4

06:38

6. Tensorflow Checkpoints Save ML models.mp4

03:29

6. Train a simple Regression Model and build Android Application

1. Section Introduction.mp4

05:22

2. Training a simple regression model for mobile devices.mp4

09:56

3. Model Testing and Conversion into Tensorflow Lite.mp4

03:26

4. Tensorflow Lite Model Training Overview.mp4

02:00

5. Analysing trained tflite model.mp4

02:28

6. Creating a new Android Studio Project and GUI of Application.mp4

08:28

7. Adding Tensorflow Lite Library In Android & Loading Tensorflow Lite Model.mp4

07:45

8. Passing Input to Tensorflow Lite Model in Android and Getting Output.mp4

05:23

9. Using basic tflite regression model in Android overview.mp4

02:08

7. Fuel Efficiency Prediction Training an advance regression model

1. Section Introduction.mp4

02:31

2. Data Collection Finding Fuel Efficiency Prediction Dataset.mp4

04:56

3. Loading Dataset in Python for Model Training.mp4

07:45

4. Handling missing Values in Fuel Efficiency Prediction Dataset.mp4

03:24

5. Handling Categorical Columns in Dataset for Model Training.mp4

04:38

6. Dataset Normalization.mp4

02:40

7. Training Fuel Efficiency Prediction Model in Tensorflow.mp4

07:04

8. Testing Trained Model and converting it to Tensorflow Lite Model.mp4

04:20

9. Training Fuel Efficiency Prediction Model Overview.mp4

04:30

8. Fuel Efficiency Prediction Android Application

1. Setting up Android Application for fuel efficiency prediction.mp4

02:52

2. Starter Application Overview.mp4

05:14

3. Loading Tensorflow Lite models in Android.mp4

05:06

4. Data Normalization in Android.mp4

06:28

5. Passing input to Tensorflow Lite model in Android and getting output.mp4

04:56

6. Testing fuel efficiency prediction android application.mp4

01:42

7. Fuel Efficiency Prediction Android App Overview.mp4

02:40

9. Training a house price prediction Model

1. Section Introduction.mp4

01:57

2. Getting dataset for training house price prediction model.mp4

03:45

3. Loading dataset for training tflite model.mp4

07:19

4. Training & Evaluating house price prediction model.mp4

06:30

5. Retraining House Price Prediction Model.mp4

04:04

10. Building House Price Prediction Android Application

1. Setting Up Android Studio Project.mp4

02:29

2. What we have done so far.mp4

06:02

3. Data Normalization in Android.mp4

06:31

4. Passing Input to house price prediction model in Android.mp4

04:42

5. Testing house price prediction Android Application.mp4

02:52

Description

Train regression models for Android | Use regression models in Android | Tensorflow Lite models integration in Android

What You'll Learn?

Train linear regression models for Android Applications
Integrate regression models in Android Applications
Use of Tensorflow Lite models in Android
Train Any Prediction Model & use it in Android Applications
Data Collection & Preprocessing for model training
Basics of Machine Learning & Deep Learning
Understand the working of artificial neural networks for model training
Basic syntax of python programming language
Use of data science libraries like numpy, pandas and matplotlib
Analysing & using advance regression models in Android Applications

Who is this for?

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

Aspiring Android 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 installed in your PC

More details

Description
Welcome to the exciting world of Android and Linear Regression! 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 Android app development. Whether you're a seasoned Android 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 delve into the world of deep learning and artificial neural networks, which will serve as the foundation for training our regression models in Android.

The Android-ML Fusion: After grasping the core concepts, we'll bridge the gap between Android 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 Android

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 Android
Learn to integrate the regression model into Android apps
Fuel Efficiency Prediction:
Apply your knowledge to a real-world problem by predicting automobile fuel efficiency
Seamlessly integrate the model into an Android app for an intuitive fuel efficiency prediction experience
House Price Prediction in Android:
Master the art of training regression models on substantial datasets
Utilize the trained model within your Android app to predict house prices confidently

The Android 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 Android applications
Analyze and use existing regression models effectively within the Android ecosystem

Who Should Enroll:
Aspiring Android 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 Android and Predictive Modeling: Join us on this exciting journey and unlock the potential of Android and Linear Regression. By the end of the course, you'll be ready to develop Android applications that not only look great but also make informed, data-driven decisions.
Enroll now and embrace the fusion of Android and predictive modeling!
Who this course is for:
Beginner Android Developer who want to build Machine Learning based Android Applications
Aspiring Android 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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