Ios & Ml : Train Tensorflow Lite Models For Ios Swift Apps

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

What you'll learn

Train Machine Learning models for IOS Swift Applications

Integrate Machine Learning models in IOS with SwiftUI

Use of Tensorflow Lite models in IOS Swift App

Analysing & using advance regression models in IOS Swift Applications

Train a machine learning model and build a house price prediction IOS Application

Train a machine learning model and build a fuel efficiency prediction IOS Swift Application

Train Any Prediction Model & use it in IOS Swift Applications

Data Collection & Preprocessing for ML model training for IOS Swift Application

Basics of Machine Learning & Deep Learning for training Machine learning Models for smart IOS App Development

Understand the working of artificial neural networks for training machine learning for IOS Swift Apps

Basic syntax of python programming language to train ML models for IOS Swift Applications

Use of data science libraries like numpy, pandas and matplotlib

Requirements

XCode Installed on your MAC

Description

Do you want to train different Machine Learning models and build smart IOS applications 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 houseto predict the Fuel Efficiency of vehiclesto recommend drug doses for medical conditionsto recommend fertilizer in agriculture to suggest exercises for improvement in player performanceand so on. So Inside this course, you will learn to train your custom machine learning models in Tensorflow lite and build smart IOS Swift applications.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 IOS app development. Whether you're a seasoned IOS developer or new to the scene, this course has something valuable to offer youCourse 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 Tensorflow Lite  models for IOS Applications.The IOS-ML Fusion: After grasping the core concepts, we'll bridge the gap between IOS 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 trainingUnlocking 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.Course Highlights:Training Your First Regression Model:Use TensorFlow and Python to create a simple regression modelConvert the model into TFLite format, making it compatible with IOS SwiftLearn to integrate the TFLite model into IOS Swift appsFuel Efficiency Prediction in IOS:Apply your knowledge to a real-world problem by predicting automobile fuel efficiencySeamlessly integrate the model into a IOS Swift app for an intuitive fuel efficiency prediction experienceHouse Price Prediction in IOS:Master the art of training regression models on substantial datasetsUtilize the trained model within your IOS app to predict house prices confidentlyThe IOS Advantage: By the end of this course, you'll be equipped to:Train advanced regression models for accurate predictionsSeamlessly integrate ML models into your IOS Swift applicationsAnalyze and use existing tflite models effectively within the IOS Swift ecosystemWho Should Enroll:Aspiring IOS developers eager to add predictive modeling to their skillsetBeginner IOS Swift developer with very little knowledge of mobile app development Intermediate IOS Swift developer wanted to build a powerful Machine Learning-based application in IOS SwiftExperienced IOS Swift developers wanted to use Machine Learning models inside their IOS applications.Enthusiasts seeking to bridge the gap between Machine Learning and IOS app developmentStep into the World of IOS and Predictive Modeling: Join us on this exciting journey and unlock the potential of IOS and Machine Learning. By the end of the course, you'll be ready to develop IOS applications that not only look great but also make informed, data-driven decisions.Enroll now and embrace the fusion of IOS and Machine Learning

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Machine Learning & Deep Learning for IOS Swift

Lecture 2 Machine Learning Introduction

Lecture 3 Supervised Machine Learning: Regression & Classification

Lecture 4 Unsupervised Machine Learning & Reinforcement Learning

Lecture 5 Deep Learning and regression models training

Lecture 6 Basic Deep Learning Concepts

Section 3: Python Programming Language for IOS Swift

Lecture 7 Google Colab Introduction

Lecture 8 Python Introduction & data types

Lecture 9 Python Lists

Lecture 10 Python dictionary & tuples

Lecture 11 Python loops & conditional statements

Lecture 12 File handling in Python

Section 4: Data Science Libraries for IOS

Lecture 13 Numpy Introduction

Lecture 14 Numpy Operations

Lecture 15 Numpy Functions

Lecture 16 Pandas Introduction

Lecture 17 Loading CSV in pandas

Lecture 18 Handling Missing values in dataset with pandas

Lecture 19 Matplotlib & charts in python

Lecture 20 Dealing images with Matplotlib

Section 5: Tensorflow & Tensorflow Lite for IOS Swift

Lecture 21 Tensorflow Introduction | Variables & Constants

Lecture 22 Shapes & Ranks of Tensors

Lecture 23 Matrix Multiplication & Ragged Tensors

Lecture 24 Tensorflow Operations

Lecture 25 Generating Random Values in Tensorflow

Lecture 26 Tensorflow Checkpoints

Section 6: Training a basic regression model for IOS Swift

Lecture 27 Section Introduction

Lecture 28 Train a simple regression model for IOS Swift

Lecture 29 Testing model and converting it to a tflite(Tensorflow lite) format for IOS

Lecture 30 Model training for IOS Swift app development overview

Lecture 31 Creating a new IOS SwiftUI project and the GUI of Swift Application

Lecture 32 Adding Tensorflow Lite Models in IOS Swift Application

Lecture 33 Loading Tensorflow Lite Models in IOS Swift Application

Lecture 34 Preparing Input for Tensorflow Lite Models and Passing it in IOS Swift App

Lecture 35 Getting Output from Tensorflow Lite model and showing it on IOS Swift App

Lecture 36 Tensorflow Lite Models Integration in IOS Swift App Overview

Section 7: Training a Fuel Efficiency Prediction Model for IOS Swift Application

Lecture 37 Section Introduction

Lecture 38 Getting datasets for training regression models for IOS

Lecture 39 Loading dataset in python with pandas

Lecture 40 Handling Missing Values in Dataset

Lecture 41 One Hot Encoding: Handling categorical columns

Lecture 42 Training and testing datasets

Lecture 43 Normalization: Bringing all columns to a common scale

Lecture 44 Training a fuel efficiency prediction model for IOS Swift Application

Lecture 45 Testing fuel efficiency prediction model and converting it to a tflite format

Lecture 46 Fuel Efficiency Model Training Overview

Section 8: Fuel Efficiency Prediction IOS Swift Application

Lecture 47 Setup Starter IOS Application for Fuel Efficiency Prediction

Lecture 48 GUI of Fuel Efficiency Prediction IOS Application

Lecture 49 Adding Tensorflow Lite Library in IOS Swift Application

Lecture 50 Loading Fuel Efficiency Prediction tflite model in IOS Swift Application

Lecture 51 Preparing Input for Tensorflow Lite Model

Lecture 52 Passing input to tflite model and getting output in IOS Swift Application

Lecture 53 Normalizing Input for Tensorflow Lite Models in IOS Swift Application

Lecture 54 Important things to remember while using Tensorflow Lite Models in IOS Apps

Section 9: Training House Price Prediction Model for IOS

Lecture 55 Section Introduction

Lecture 56 Getting house price prediction dataset

Lecture 57 Load dataset for training house price prediction tflite model for IOS

Lecture 58 Training & evaluating house price prediction model for IOS

Lecture 59 Retraining price prediction model

Section 10: House Price Prediction IOS Application

Lecture 60 Setting Up House Price Prediction IOS Swift Application

Lecture 61 GUI of House Price Prediction IOS Swift Application With SwiftUI

Lecture 62 Adding Tensorflow Lite Library in IOS Swift Application

Lecture 63 Loading Tensorflow Lite Model in IOS Swift Application

Lecture 64 Passing Input to Tensorflow Lite Model and Get prediction for House Price

Lecture 65 House Price Prediction Application Testing

Beginner IOS Developer who want to build Machine Learning based IOS Applications,Intermediate IOS developers eager to add Machine Learning to their skillset,IOS experts seeking to bridge the gap between Machine Learning and Mobile App Development