Ios & Ml Train Machine Learning Models For Ios Swift Apps

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

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