AI and Machine Learning for Mobile Developers Course Overview

AI and Machine Learning for Mobile Developers Course Overview

Unlock the potential of AI and Machine Learning with our comprehensive course designed for mobile developers. This course will guide you through the foundational concepts of AI and ML, their significance in mobile development, and the setup of your development environment on Android and iOS platforms. Learn to preprocess data, build and evaluate machine learning models, and implement them using frameworks like TensorFlow Lite and Core ML. Through hands-on sessions, develop practical applications such as image recognition and text classification apps. Gain insights into advanced techniques, deploy and maintain AI models effectively, and explore future trends and ethical considerations. This course equips you with the skills to innovate and excel in mobile AI development.

This is a Rare Course and it can be take up to 3 weeks to arrange the training.

Purchase This Course

Fee On Request

  • Live Online Training (Duration : 40 Hours)
  • Per Participant
  • Guaranteed-to-Run (GTR)
  • date-img
  • date-img

♱ Excluding VAT/GST

Classroom Training price is on request

You can request classroom training in any city on any date by Requesting More Information

  • Live Online Training (Duration : 40 Hours)
  • Per Participant

♱ Excluding VAT/GST

Classroom Training price is on request

You can request classroom training in any city on any date by Requesting More Information

Request More Information

Email:  WhatsApp:

Koenig's Unique Offerings

images-1-1

1-on-1 Training

Schedule personalized sessions based upon your availability.

images-1-1

Customized Training

Tailor your learning experience. Dive deeper in topics of greater interest to you.

happinessGuaranteed_icon

Happiness Guaranteed

Experience exceptional training with the confidence of our Happiness Guarantee, ensuring your satisfaction or a full refund.

images-1-1

Destination Training

Learning without limits. Create custom courses that fit your exact needs, from blended topics to brand-new content.

images-1-1

Fly-Me-A-Trainer (FMAT)

Flexible on-site learning for larger groups. Fly an expert to your location anywhere in the world.

Course Prerequisites

Prerequisites for AI and Machine Learning for Mobile Developers Course

To ensure a successful learning experience, we recommend that participants meet the following minimum prerequisites before undertaking the AI and Machine Learning for Mobile Developers course:


  • Basic Understanding of Mobile Development:


    • Familiarity with mobile development platforms (Android and/or iOS).
    • Experience with development tools such as Android Studio or Xcode.
  • Programming Knowledge:


    • Proficiency in a programming language commonly used in mobile development (e.g., Java, Kotlin for Android or Swift for iOS).
    • Basic knowledge of Python is advantageous as it is widely used in AI and machine learning.
  • Mathematical Foundation:


    • Basic understanding of high school mathematics, including algebra.
    • Familiarity with basic concepts of statistics and probability can be helpful.
  • Prior Exposure to Data Handling:


    • Basic understanding of data types, sources, and collection methods.
    • Familiarity with concepts such as data cleaning, normalization, and transformation is beneficial.
  • Interest in AI and Machine Learning:


    • A keen interest in understanding and applying AI and machine learning concepts.

These prerequisites aim to prepare you adequately to make the most of the course content and practical applications. If you meet


Target Audience for AI and Machine Learning for Mobile Developers

1. Introduction: This comprehensive course equips mobile developers with the skills to integrate AI and machine learning into their apps, targeting those keen on staying at the forefront of technological advancements.


2. Job Roles and Audience:


  • Mobile App Developers
  • Software Engineers
  • Data Scientists specialized in mobile platforms
  • AI/ML Enthusiasts with a focus on mobile applications
  • Android Developers
  • iOS Developers
  • Machine Learning Engineers
  • Technical Leads overseeing mobile development projects
  • CTOs and Tech Managers looking to integrate AI in mobile solutions
  • App Development Consultants
  • Product Managers in tech sectors
  • Computer Science Students with an interest in mobile development and AI
  • Startup Founders aiming to innovate mobile app functionalities with AI
  • Technology Trainers and Educators


Learning Objectives - What you will Learn in this AI and Machine Learning for Mobile Developers?

Brief Introduction

The "AI and Machine Learning for Mobile Developers" course by Koenig Solutions aims to equip developers with the necessary skills and knowledge to integrate AI and machine learning into mobile applications. The course covers essential concepts, practical implementations, and the latest trends in the field.

Learning Objectives and Outcomes

  • Overview of AI and Machine Learning

    • Gain a fundamental understanding of AI and machine learning principles.
    • Learn the importance and various applications in mobile development.
  • Development Environment Setup

    • Set up development tools including Android Studio and Xcode.
    • Install required libraries and frameworks like TensorFlow Lite and Core ML.
  • Machine Learning Basics

    • Understand different data types, sources, and collection methods.
    • Learn data preprocessing techniques: cleaning, normalization, and transformation.
    • Introduction to feature engineering.
  • Building Machine Learning Models

    • Select appropriate algorithms for different tasks such as classification and regression.
    • Train and test machine learning models.
    • Evaluate model performance using specific metrics and validation techniques.
  • Mobile AI Frameworks

    • Gain an overview of TensorFlow Lite and Core ML.
    • Compare different mobile AI frameworks for better understanding.
  • Implementing Models in Mobile Apps

    • Convert

Target Audience for AI and Machine Learning for Mobile Developers

1. Introduction: This comprehensive course equips mobile developers with the skills to integrate AI and machine learning into their apps, targeting those keen on staying at the forefront of technological advancements.


2. Job Roles and Audience:


  • Mobile App Developers
  • Software Engineers
  • Data Scientists specialized in mobile platforms
  • AI/ML Enthusiasts with a focus on mobile applications
  • Android Developers
  • iOS Developers
  • Machine Learning Engineers
  • Technical Leads overseeing mobile development projects
  • CTOs and Tech Managers looking to integrate AI in mobile solutions
  • App Development Consultants
  • Product Managers in tech sectors
  • Computer Science Students with an interest in mobile development and AI
  • Startup Founders aiming to innovate mobile app functionalities with AI
  • Technology Trainers and Educators


Learning Objectives - What you will Learn in this AI and Machine Learning for Mobile Developers?

Brief Introduction

The "AI and Machine Learning for Mobile Developers" course by Koenig Solutions aims to equip developers with the necessary skills and knowledge to integrate AI and machine learning into mobile applications. The course covers essential concepts, practical implementations, and the latest trends in the field.

Learning Objectives and Outcomes

  • Overview of AI and Machine Learning

    • Gain a fundamental understanding of AI and machine learning principles.
    • Learn the importance and various applications in mobile development.
  • Development Environment Setup

    • Set up development tools including Android Studio and Xcode.
    • Install required libraries and frameworks like TensorFlow Lite and Core ML.
  • Machine Learning Basics

    • Understand different data types, sources, and collection methods.
    • Learn data preprocessing techniques: cleaning, normalization, and transformation.
    • Introduction to feature engineering.
  • Building Machine Learning Models

    • Select appropriate algorithms for different tasks such as classification and regression.
    • Train and test machine learning models.
    • Evaluate model performance using specific metrics and validation techniques.
  • Mobile AI Frameworks

    • Gain an overview of TensorFlow Lite and Core ML.
    • Compare different mobile AI frameworks for better understanding.
  • Implementing Models in Mobile Apps

    • Convert