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Machine Learning for Apps
01-Intro to Course
0 What is Machine Learning-
1 Basics of Machine Learning
2 Installing Anaconda - Python Environment (3.0 - 2.7)
3 Downloading - Setting Up Atom & Plugins
02-Python Basics
4 Variables in Python
5 Functions, Conditionals, & Loops in Python
6 Arrays & Tuples in Python
7 Importing Modules in Python
03-Building a Classification Model
8 What is scikit-learn- Why use it-
9 Installing scikit-learn & scipy with Anaconda
10 Intro to the Iris Dataset
11 Datasets- Features & Labels Explained
12 Loading the Iris Dataset - Examining & Preparing Data
13 Creating - Training a KNeighborsClassifier
14 Testing Prediction Accuracy with Test Data
15 Building Our Own KNeighborsClassifier
04-Building a Convolutional Neural Network
16 What is Keras- Why use it-
17 What is a Convolutional Neural Network (CNN)-
18 Installing Keras with Anaconda
19 Preparing Dataset for a CNN
20 Building - Visualizing a CNN using Sequential- Part 1
21 Building - Visualizing a CNN using Sequential- Part 2
22 Training CNN - Evaluating Accuracy - Saving to Disk
23 Switching Python Environments - Converting to Core ML Model
05-Building a Handwriting Recognition App
24 Intro to App – Handwriting
25 Building Interface - Wiring Up
26 Drawing On Screen
27 Importing Core ML Model - Reading Metadata
28 Utilizing Core ML - Vision to Make Prediction
29 Handling - Displaying Prediction Results
06-Core ML Basics
30 Intro to App – Core ML Photo Analysis
31 What is Machine Learning-
32 What is Core ML-
33 Creating Xcode Project
34 Building ImageVC in Interface Builder - Wiring Up
35 Creating ImageCell & Subclass - Wiring Up
36 Creating FoodItems Helper File
37 Creating Custom 3x3 Grid UICollectionViewFlowLayout
38 Choosing, Downloading, Importing Core ML Model
39 Passing Images Through Core ML Model
40 Handling Core ML Prediction Results
41 Challenge – Core ML Photo Analysis
7 Importing Modules in Python
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