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.
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.
|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 â€“Core ML Photo Analysis||00:01:00|
|Assignment – Machine Learning for Apps||00:00:00|
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