Machine Learning for Apps | Module 4 Summary

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Module 1: Convolutional Neural Network

Module 1: Essential Software

Essential Software - Learning Outcomes

Introduction to the Course

What is Machine Learning?

Machine Learning Basics

Installing Anaconda

Atom and Plugins

Essential Software - Lesson Summary

Module 2: Python Basics

Python Basics - Learning Outcomes

Variables in Python

Python Functions, Conditionals and Loops

Python Arrays and Tuples

Modules in Python

Python Basics - Lesson Summary

Module 3: Classification Modelling

Classification Modelling - Learning Outcomes

About Scikit-Learn

Installing Scikit-Learn

Iris Flower Dataset

Dataset Features and Labels

Preparing Data

Training a Classifier

Testing Prediction Accuracy

Building a Classifier

Classification Modelling - Lesson Summary

Module 4: Convolutional Neural Network

Convolutional Neural Network - Learning Outcomes

Introduction to Keras

Convolutional Neural Networks

Installing Keras and PIP

Preparing the Dataset

Using Sequential

Sequential Process

Training the Data

Core ML Model

Convolutional Neural Network - Lesson Summary

Module 5: Building the App

Building the App - Learning Outcomes

Introduction to the Module

Building the Interface

Drawing On Screen

Core ML Import

Utilizing Core ML

Displaying Prediction Results

Building the App - Lesson Summary

Module 6: Core ML Basics

Core ML Basics - Learning Outcomes

About Core ML

Core ML Photo Analysis

New Xcode Project

Building the ImageVC

ImageCell and Subclass

Food Items Helper File

Custom Grid

Importing Core ML Model

Passing In Images

Handling Prediction Results

Core ML Photo Challenge

Core ML Basics - Lesson Summary

Course assessment

Machine Learning for Apps - Course Assessment

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