English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 7 Hours | 1.32 GB
Start building more intelligent apps with Machine Learning. Take advantage of this new foundational framework!
Welcome to the most comprehensive course on Core ML, one of Apples hot new features for iOS 11. The goal with Machine Learning is to mimic the human mind. It can be used to identify things like objects or images, make predictions and even analyze and identify speech.
Dive in and learn the core concepts of machine learning and start building apps that can think! In this course you going to learn everything you need to know to start building more intelligent apps and your own ML Models.
Core ML is the first step if you want to start building apps with AI. Machine Learning opens an entirely new world to opportunities that will take your apps to the next level.
Here are some of the things you’ll be able to do after taking this course:
- Learn to code how the PROs code – not just copy and paste
- Build Real Projects – You’ll get to build projects that help you retain what you’ve learned
- Build awesome apps that can make predictions
- Build amazing apps that can classify human handwriting
WHAT YOU WILL LEARN:
- Learn about the foundation of Machine Learning and Core ML
- Learn foundational python
- Build a classification model allow your apps to make predictions
- Build a neural network for your app that can classify human writing
- Learn core ML concepts so you can build your own ML Model
- Utilize the power of Machine Learning and AI for use in iOS apps
- Learn how to pass in images to Apples pre trained model – MobileNet
Table of Contents
Intro to Course
1 What is Machine Learning
2 Basics of Machine Learning
3 Installing Anaconda Python Environment
4 Downloading Setting Up Atom Plugins
Python Basics
5 Variables in Python
6 Functions Conditionals Loops in Python
7 Arrays Tuples in Python
8 Importing Modules in Python
Building a Classification Model
9 What is scikit-learn Why use it
10 Installing scikit-learn scipy with Anaconda
11 Intro to the Iris Dataset
12 Datasets Features Labels Explained
13 Loading the Iris Dataset Examining Preparing Data
14 Creating Training a KNeighborsClassifier
15 Testing Prediction Accuracy with Test Data
16 Building Our Own KNeighborsClassifier
Building a Convolutional Neural Network
17 What is Keras Why use it
18 What is a Convolutional Neural Network (CNN)
19 Installing Keras with Anaconda
20 Preparing Dataset for a CNN
21 Building Visualizing a CNN using Sequential Part 1
22 Building Visualizing a CNN using Sequential Part 2
23 Training CNN Evaluating Accuracy Saving to Disk
24 Switching Python Environments Converting to Core ML Model
Building a Handwriting Recognition App
25 Intro to App Handwriting
26 Building Interface Wiring Up
27 Drawing On Screen
28 Importing Core ML Model Reading Metadata
29 Utilizing Core ML Vision to Make Prediction
30 Handling Displaying Prediction Results
Core ML Basics
31 Intro to App Core ML Photo Analysis
32 What is Machine Learning
33 What is Core ML
34 Creating Xcode Project
35 Building ImageVC in Interface Builder Wiring Up
36 Creating ImageCell Subclass Wiring Up
37 Creating FoodItems Helper File
38 Creating Custom 3×3 Grid UICollectionViewFlowLayout
39 Choosing Downloading Importing Core ML Model
40 Passing Images Through Core ML Model
41 Handling Core ML Prediction Results
42 Challenge Core ML Photo Analysis
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