Machine Learning for Apps

Course

Online

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Special Emagister price

£ 9 £ 425 VAT inc.

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Description

  • Type

    Course

  • Level

    Intermediate

  • Methodology

    Online

  • Class hours

    7h

  • Duration

    Flexible

  • Start date

    Different dates available

The Machine Learning for Apps course is designed to equip learners with the knowledge and skills to integrate machine learning techniques into mobile and web applications. This course explores the fundamentals of machine learning, including data processing, model selection, algorithm implementation, and practical application within app development environments.

Learners will discover how to design intelligent apps that can make predictions, recognise patterns, automate tasks, and enhance user experiences. The course covers key topics such as supervised and unsupervised learning, natural language processing, recommendation systems, and deploying machine learning models in real-world applications.

Ideal for app developers, software engineers, data enthusiasts, and tech entrepreneurs, this course provides practical insights into using machine learning to improve app functionality and user engagement. By completing Machine Learning for Apps, participants will gain confidence in implementing machine learning models, optimising app performance, and creating intelligent, data-driven applications that stand out in today’s competitive digital landscape.

Important information

Price for Emagister users:

Facilities

Location

Start date

Online

Start date

Different dates availableEnrolment now open

About this course

Understand core machine learning concepts and algorithms.

Process and analyse data for app development.

Implement supervised and unsupervised learning models.

Integrate machine learning features into mobile and web apps.

Apply natural language processing and recommendation systems.

Deploy and optimise machine learning models in real applications.

Enhance app functionality and user experience with intelligent solutions.

The Machine Learning for Apps course is suitable for app developers, software engineers, data scientists, tech entrepreneurs, and anyone interested in creating intelligent applications. It is ideal for professionals looking to enhance their technical skills and integrate machine learning into app development workflows.

Learners will gain practical experience in applying machine learning algorithms to real-world app scenarios, improving functionality, personalisation, and user engagement. This course is also valuable for students, developers, and innovators who want to stay competitive in the rapidly evolving tech industry. By completing this course, participants will be equipped with the knowledge and tools needed to design, implement, and optimise machine learning-driven apps that deliver meaningful results.

There are no formal entry requirements for the Machine Learning for Apps course. It is suitable for learners aged 16 and above who are interested in app development and machine learning. While prior programming knowledge is beneficial, it is not mandatory. Learners are recommended to have good English communication skills, basic numeracy, and IT competence. The course is designed to be professional, accessible, and practical, providing learners with step-by-step guidance to implement machine learning solutions in apps and enhance digital functionality.

Upon successful completion of the Machine Learning for Apps, you will qualify for a UK and internationally recognised professional certification. You may also choose to formalise your achievement by obtaining your PDF Certificate for £9 or a Hardcopy Certificate for £15.

This course stands out for its practical, expert-designed modules that focus on applying machine learning in real-world app development. Learners benefit from flexible, self-paced online learning while gaining hands-on experience with algorithms, data processing, and app integration techniques.

The course emphasises actionable skills that can be applied immediately to create intelligent, personalised applications. Completing this training enhances technical expertise, strengthens professional credibility, and equips learners with the knowledge to innovate in app development. It is particularly valuable for developers, tech entrepreneurs, and professionals seeking to leverage machine learning to improve app performance and user engagement.

Yes. The course is designed for learners with basic programming knowledge or beginners, providing step-by-step guidance to implement machine learning in apps.

Learners will gain skills to integrate machine learning into apps, enhancing functionality, personalisation, and user engagement, making them more competitive in app development roles.

The course is fully online and self-paced. Learners can access materials anytime, from any device, allowing them to study around work, projects, or personal commitments.

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This centre's achievements

2019

All courses are up to date

The average rating is higher than 3.7

More than 50 reviews in the last 12 months

This centre has featured on Emagister for 7 years

Subjects

  • Web
  • Data science
  • Predictive Analytics
  • App Development
  • Machine Learning

Teachers and trainers (1)

One Education

One Education

Course Provider

Course programme

This course offers a practical introduction to **Python and machine learning**, starting with an **introductory overview** and foundational **Python basics**. Learners then progress to **building a classification model, creating a convolutional neural network**, and developing a **handwriting recognition app**, concluding with an introduction to **Core ML**, equipping them with skills to implement machine learning applications effectively.

Course Curriculum

  • Section 01: Intro to Course
  • Section 02: Python Basics
  • Section 03: Building a Classification Model
  • Section 04: Building a Convolutional Neural Network
  • Section 05: Building a Handwriting Recognition App
  • Section 06: Core ML Basics

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Machine Learning for Apps

Special Emagister price

£ 9 £ 425 VAT inc.