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Machine Learning for Apps
01-Intro to Course
0 What is Machine Learning- (7:46)
1 Basics of Machine Learning (6:34)
2 Installing Anaconda - Python Environment (3.0 - 2.7) (7:25)
3 Downloading - Setting Up Atom & Plugins (9:02)
02-Python Basics
4 Variables in Python (8:24)
5 Functions, Conditionals, & Loops in Python (9:50)
6 Arrays & Tuples in Python (13:52)
7 Importing Modules in Python (5:22)
03-Building a Classification Model
8 What is scikit-learn- Why use it- (3:52)
9 Installing scikit-learn & scipy with Anaconda (3:28)
10 Intro to the Iris Dataset (3:28)
11 Datasets- Features & Labels Explained (7:39)
12 Loading the Iris Dataset - Examining & Preparing Data (9:27)
13 Creating - Training a KNeighborsClassifier (9:42)
14 Testing Prediction Accuracy with Test Data (12:08)
15 Building Our Own KNeighborsClassifier (18:00)
04-Building a Convolutional Neural Network
16 What is Keras- Why use it- (8:01)
17 What is a Convolutional Neural Network (CNN)- (26:30)
18 Installing Keras with Anaconda (4:38)
19 Preparing Dataset for a CNN (17:38)
20 Building - Visualizing a CNN using Sequential- Part 1 (14:07)
21 Building - Visualizing a CNN using Sequential- Part 2 (19:40)
22 Training CNN - Evaluating Accuracy - Saving to Disk (17:53)
23 Switching Python Environments - Converting to Core ML Model (13:39)
05-Building a Handwriting Recognition App
24 Intro to App – Handwriting (2:56)
25 Building Interface - Wiring Up (11:42)
26 Drawing On Screen (21:01)
27 Importing Core ML Model - Reading Metadata (5:16)
28 Utilizing Core ML - Vision to Make Prediction (17:31)
29 Handling - Displaying Prediction Results (15:13)
06-Core ML Basics
30 Intro to App – Core ML Photo Analysis (4:25)
31 What is Machine Learning- (7:46)
32 What is Core ML- (5:03)
33 Creating Xcode Project (2:43)
34 Building ImageVC in Interface Builder - Wiring Up (7:40)
35 Creating ImageCell & Subclass - Wiring Up (8:13)
36 Creating FoodItems Helper File (7:02)
37 Creating Custom 3x3 Grid UICollectionViewFlowLayout (9:12)
38 Choosing, Downloading, Importing Core ML Model (5:18)
39 Passing Images Through Core ML Model (12:18)
40 Handling Core ML Prediction Results (9:42)
41 Challenge – Core ML Photo Analysis (1:15)
11 Datasets- Features & Labels Explained
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