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| Section 01: Intro to Course | |||
| What is Machine Learning? | |||
| Basics of Machine Learning | |||
| Installing Anaconda / Python Environment | |||
| Downloading / Setting Up Atom and Plugins | |||
| Section 02: Python Basics | |||
| Variables in Python | |||
| Functions, Conditionals, and Loops in Python | |||
| Arrays and Tuples in Python | |||
| Importing Modules in Python | |||
| Section 03: Building a Classification Model | |||
| What is scikit-learn? Why use it? | |||
| Installing scikit-learn and scipy with Anaconda | |||
| Intro to the Iris Dataset | |||
| Datasets: Features and Labels Explained | |||
| Loading the Iris Dataset / Examining and Preparing Data | |||
| Creating / Training a KNeighborsClassifier | |||
| Testing Prediction Accuracy with Test Data | |||
| Building Our Own KNeighborsClassifie | |||
| Section 04: Building a Convolutional Neural Network | |||
| What is Keras? Why use it? | |||
| What is a Convolutional Neural Network (CNN)? | |||
| Installing Keras with Anaconda | |||
| Preparing Dataset for a CNN | |||
| Building / Visualizing a CNN using Sequential: Part 1 | |||
| Building / Visualizing a CNN using Sequential: Part 2 | |||
| Training CNN / Evaluating Accuracy / Saving to Disk | |||
| Switching Python Environments / Converting to Core ML Model | |||
| Section 05: Building a Handwriting Recognition App | |||
| Intro to App-Handwriting | |||
| Building Interface / Wiring Up | |||
| Drawing on Screen | |||
| Importing Core ML Model / Reading Metadata | |||
| Utilizing Core ML / Vision to Make Prediction | |||
| Handling / Displaying Prediction Results | |||
| Section 06: Core ML Basics | |||
| Intro to App -Core ML Photo Analysis | |||
| What is Machine Learning? | |||
| What is Core ML? | |||
| Creating X code Project | |||
| Building Image VC in Interface Builder / Wiring Up | |||
| Creating Image Cell | |||
| Creating Food Items Helper File | |||
| Creating Custom 3×3 Grid UI Collection View Flow Layout | |||
| Choosing, Downloading, Importing Core ML Model | |||
| Passing Images through Core ML Model | |||
| Handling Core ML Prediction Results | |||
| Challenge –Core ML Photo Analysis | |||