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Machine Learning for Apps


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Modules

Here is the course outline:

1. Course Intro

4 Lessons: (1) What is Machine Learning? (2) Basics of Machine Learning (3) Installing Anaconda - Python Environment (4) Setting up Atom & Plugins

2. Python Basics

4 Lessons: (1) Variables (2) Functions, Conditionals, and Loops (3) Arrays & Tuples (4) Importing Modules

3. Building a Classification Model

8 Lessons: (1) What is scikit-learn? (2) Installing scikit-learn & scipy with Anaconda (3) Intro to the Iris Dataset (4) Datasets - Features & Labels (5) Loading the Iris Dataset - Examining & Preparing Data (6) Creating & Training a KNeighborsClassifier (7) Testing Prediction Accuracy with Test Data (8) Building Our Own KNeighborsClassifier

4. Building a Convolutional Neural Network

8 Lessons: (1) What is Keras and why use it? (2) What is a Convolutional Neural Network? (3) Installing Keras with Anaconda (4) Preparing Datasets for a CNN (5) Building & Visualizing a CNN Using Sequential - Part 1 (6) Building & Visualizing a CNN Using Sequential - Part 2 (7) Training a CNN - Evaluating Accuracy - Saving to Disk (8) Switching Python Environments - Converting to Core ML Model

5. Building a Handwriting Recognition App

6 Lessons: (1) Intro to App - Handwriting (2) Building the Interface - Wiring Up (3) Drawing on Screen (4) Importing Core ML Model - Reading Metadata (5) Utlilizing Core ML Vision to Make Prediction (6) Handling & Displaying Prediction Results

6. Core ML Basics

12 Lessons: (1) Intro to App - Core ML Photo Analysis (2) What is Machine Learning? (3) What is Core ML? (4) Creating Xcode Project (5) Building ImageVC in Interface Builder - Wiring Up (6) Creating ImageCell & Subclass - Wiring Up (7) Creating FoodItems Helper File (8) Creating Custom 3x3 Grid UICollectionViewFlowLayout (9) Choosing, Downloading, Importing Core ML Model (10) Passing Images Through Core ML Model (11) Handling Core ML Prediction Results (12) Challenge - Core ML Photo Analysis

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