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Curriculum

Overview:

Welcome to “Machine Learning for Apps”! This course is tailored for app developers seeking to integrate machine learning capabilities into their applications. Machine learning has revolutionized app development, enabling apps to offer personalized experiences, predictive features, and intelligent functionalities. In this course, you’ll learn how to leverage machine learning algorithms and techniques to enhance your apps, making them smarter, more efficient, and more user-friendly.

Interactive video lectures by industry experts
Instant e-certificate and hard copy dispatch by next working day
Fully online, interactive course with Professional voice-over
Developed by qualified first aid professionals
Self paced learning and laptop, tablet, smartphone friendly
24/7 Learning Assistance
Discounts on bulk purchases

Main Course Features:

Comprehensive coverage of machine learning concepts and algorithms relevant to app development
Hands-on projects and coding exercises focusing on integrating machine learning models into apps
Exploration of popular machine learning libraries and frameworks suitable for app development
Guidance on data preprocessing, model training, and deployment in the context of app development
Real-world examples and case studies demonstrating machine learning applications in various types of apps
Access to resources and tools for building and testing machine learning-powered apps
Supportive online community for collaboration and assistance throughout the course
Regular assessments and feedback to track progress and reinforce learning

Who Should Take This Course:

App developers interested in incorporating machine learning features into their apps
Software engineers looking to expand their skill set to include machine learning for app development
Students and professionals aiming to enhance their app development skills with machine learning capabilities

Learning Outcomes:

Understand fundamental machine learning concepts and techniques relevant to app development
Integrate machine learning models seamlessly into mobile and web applications
Utilize popular machine learning libraries and frameworks such as TensorFlow or PyTorch for app development
Implement predictive features, recommendation systems, and other intelligent functionalities in apps
Optimize machine learning models for performance and efficiency in app environments
Debug and troubleshoot machine learning integration issues in apps effectively
Develop a portfolio of machine learning-powered apps showcasing proficiency in app development and machine learning integration
Stay updated with the latest advancements and trends in machine learning for app development.

Certification

Once you’ve successfully completed your course, you will immediately be sent a digital certificate. Also, you can have your printed certificate delivered by post (shipping cost £3.99). All of our courses are fully accredited, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in your chosen field. Our certifications have no expiry dates, although we do recommend that you renew them every 12 months.

Assessment

At the end of the Course, there will be an online assessment, which you will need to pass to complete the course. Answers are marked instantly and automatically, allowing you to know straight away whether you have passed. If you haven’t, there’s no limit on the number of times you can take the final exam. All this is included in the one-time fee you paid for the course itself.

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 â€“Core ML Photo Analysis	00:01:00