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IOS & ML: Train Machine Learning models for IOS SwiftUI Apps

سرفصل های دوره

Use Tensorflow Lite models in IOS with SwiftUI | Train Machine Learning Models for IOS Swift Applications | SwiftUI


1. Introduction
  • 1. Introduction

  • 2. Machine Learning & Deep Learning for IOS Swift
  • 1. Machine Learning Introduction
  • 2. Supervised Machine Learning Regression & Classification
  • 3. Unsupervised Machine Learning & Reinforcement Learning
  • 4. Deep Learning and regression models training
  • 5. Basic Deep Learning Concepts

  • 3. Python Programming Language for IOS Swift
  • 1. Google Colab Introduction
  • 2.1 Python.zip
  • 2. Python Introduction & data types
  • 3. Python Lists
  • 4. Python dictionary & tuples
  • 5. Python loops & conditional statements
  • 6. File handling in Python

  • 4. Data Science Libraries for IOS
  • 1.1 DataScience Libraries.zip
  • 1. Numpy Introduction
  • 2. Numpy Operations
  • 3. Numpy Functions
  • 4. Pandas Introduction
  • 5. Loading CSV in pandas
  • 6. Handling Missing values in dataset with pandas
  • 7. Matplotlib & charts in python
  • 8. Dealing images with Matplotlib

  • 5. Tensorflow & Tensorflow Lite for IOS Swift
  • 1.1 Tensorflow.zip
  • 1. Tensorflow Introduction Variables & Constants
  • 2. Shapes & Ranks of Tensors
  • 3. Matrix Multiplication & Ragged Tensors
  • 4. Tensorflow Operations
  • 5. Generating Random Values in Tensorflow
  • 6. Tensorflow Checkpoints

  • 6. Training a basic regression model for IOS Swift
  • 1. Section Introduction
  • 2.1 Basic Example.zip
  • 2. Train a simple regression model for IOS Swift
  • 3. Testing model and converting it to a tflite(Tensorflow lite) format for IOS
  • 4. Model training for IOS Swift app development overview
  • 5. Creating a new IOS SwiftUI project and the GUI of Swift Application
  • 6. Adding Tensorflow Lite Models in IOS Swift Application
  • 7. Loading Tensorflow Lite Models in IOS Swift Application
  • 8. Preparing Input for Tensorflow Lite Models and Passing it in IOS Swift App
  • 9. Getting Output from Tensorflow Lite model and showing it on IOS Swift App
  • 10. Tensorflow Lite Models Integration in IOS Swift App Overview

  • 7. Training a Fuel Efficiency Prediction Model for IOS Swift Application
  • 1. Section Introduction
  • 2.1 FuelEfficiencyPrediction.zip
  • 2. Getting datasets for training regression models for IOS
  • 3. Loading dataset in python with pandas
  • 4. Handling Missing Values in Dataset
  • 5. One Hot Encoding Handling categorical columns
  • 6. Training and testing datasets
  • 7. Normalization Bringing all columns to a common scale
  • 8. Training a fuel efficiency prediction model for IOS Swift Application
  • 9. Testing fuel efficiency prediction model and converting it to a tflite format
  • 10. Fuel Efficiency Model Training Overview

  • 8. Fuel Efficiency Prediction IOS Swift Application
  • 1. Setup Starter IOS Application for Fuel Efficiency Prediction
  • 2. GUI of Fuel Efficiency Prediction IOS Application
  • 3. Adding Tensorflow Lite Library in IOS Swift Application
  • 4. Loading Fuel Efficiency Prediction tflite model in IOS Swift Application
  • 5. Preparing Input for Tensorflow Lite Model
  • 6. Passing input to tflite model and getting output in IOS Swift Application
  • 7. Normalizing Input for Tensorflow Lite Models in IOS Swift Application
  • 8. Important things to remember while using Tensorflow Lite Models in IOS Apps

  • 9. Training House Price Prediction Model for IOS
  • 1. Section Introduction
  • 2.1 HousePricePrediction.zip
  • 2. Getting house price prediction dataset
  • 3. Load dataset for training house price prediction tflite model for IOS
  • 4. Training & evaluating house price prediction model for IOS
  • 5. Retraining price prediction model

  • 10. House Price Prediction IOS Application
  • 1. Setting Up House Price Prediction IOS Swift Application
  • 2. GUI of House Price Prediction IOS Swift Application With SwiftUI
  • 3. Adding Tensorflow Lite Library in IOS Swift Application
  • 4. Loading Tensorflow Lite Model in IOS Swift Application
  • 5. Passing Input to Tensorflow Lite Model and Get prediction for House Price
  • 6. House Price Prediction Application Testing
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