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Overview

Machine learning is the future. Start building intelligent apps and make the most of this groundbreaking framework, with this complete training guide.

This Machine Learning for Apps course will teach you how to take advantage of machine learning to code like a pro and build incredible apps that can make predictions. It will also teach you the fundamentals of Python for complete beginners. Designed by industry experts, it covers best practices for managing projects and the core concepts for creating your own ML model.

By the end of this course, you will be able to build an amazing handwriting recognition app and convolutional neural network from scratch, and will have an in-depth understanding of the core ML basics. This course is ideal for those with a basic understanding of iOS development.

Course design

The course is delivered through our online learning platform, accessible through any internet-connected device. There are no formal deadlines or teaching schedules, meaning you are free to study the course at your own pace.

You are taught through a combination of

  • Video lessons
  • Online study materials

Will I receive a certificate of completion?

Upon successful completion, you will qualify for the UK and internationally-recognised CPD accredited certification. You can choose to make your achievement formal by obtaining your PDF Certificate at the cost of £9 and Hard Copy Certificate for £15.

Why study this course

It doesn’t matter if you are an aspiring professional or absolute beginner; this course will enhance your expertise and boost your CV with critical skills and an accredited certification attesting to your knowledge.

The Machine Learning for Apps is fully available to anyone, and no previous qualifications are needed to enrol. All One Education needs to know is that you are eager to learn and are over 16.

Course Curriculum

Section 01: Intro to Course
What is Machine Learning? 00:08:00
Basics of Machine Learning 00:07:00
Installing Anaconda / Python Environment 00:07:00
Downloading / Setting Up Atom and Plugins 00:09:00
Section 02: Python Basics
Variables in Python 00:08:00
Functions, Conditionals, and Loops in Python 00:10:00
Arrays and Tuples in Python 00:14:00
Importing Modules in Python 00:05:00
Section 03: Building a Classification Model
What is scikit-learn? Why use it? 00:04:00
Installing scikit-learn and scipy with Anaconda 00:03:00
Intro to the Iris Dataset 00:03:00
Datasets: Features and Labels Explained 00:08:00
Loading the Iris Dataset / Examining and Preparing Data 00:09:00
Creating / Training a KNeighborsClassifier 00:10:00
Testing Prediction Accuracy with Test Data 00:12:00
Building Our Own KNeighborsClassifie 00:18:00
Section 04: Building a Convolutional Neural Network
What is Keras? Why use it? 00:08:00
What is a Convolutional Neural Network (CNN)? 00:27:00
Installing Keras with Anaconda 00:05:00
Preparing Dataset for a CNN 00:18:00
Building / Visualizing a CNN using Sequential: Part 1 00:14:00
Building / Visualizing a CNN using Sequential: Part 2 00:20:00
Training CNN / Evaluating Accuracy / Saving to Disk 00:18:00
Switching Python Environments / Converting to Core ML Model 00:14:00
Section 05: Building a Handwriting Recognition App
Intro to App-Handwriting 00:03:00
Building Interface / Wiring Up 00:12:00
Drawing on Screen 00:21:00
Importing Core ML Model / Reading Metadata 00:05:00
Utilizing Core ML / Vision to Make Prediction 00:18:00
Handling / Displaying Prediction Results 00:15:00
Section 06: Core ML Basics
Intro to App -Core ML Photo Analysis 00:04:00
What is Machine Learning? 00:08:00
What is Core ML? 00:05:00
Creating X code Project 00:03:00
Building Image VC in Interface Builder / Wiring Up 00:08:00
Creating Image Cell 00:08:00
Creating Food Items Helper File 00:07:00
Creating Custom 3×3 Grid UI Collection View Flow Layout 00:09:00
Choosing, Downloading, Importing Core ML Model 00:05:00
Passing Images through Core ML Model 00:12:00
Handling Core ML Prediction Results 00:10:00
Challenge –Core ML Photo Analysis 00:01:00
Assignment
Assignment – Machine Learning for Apps 00:00:00

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