Technocation

Menu

Course Name Duration Classes Total Fee Mode of Training Class Timing
APPML Course
2 Months
40
24,000 PKR
Online & Face-to-Face
To be decided mutually with students
APPML Training Certification Course Pakistan

APPML Training Certification Course

In addition to validating your technical skills, APPML Certification can help you advance your expertise. Once APPML Certified, you’ll be eligible for perks that help you show off your achievements and keep learning. Register for exams and claim benefits at Technocation  training.
 

Technocation provides an excellent faculty and qualified developers as there is a remarkable prospect in this field. One can make his/her Career with the help of both APPML Training and establish an identity and get guidance in Rawalpindi.

Therefore, we aim to shape inspiring students with in-depth training to meet the requirements of the IT industry and build substantial grounds in APPML Training by exhibiting students with various projects. Technocation also bestows the Best APPML Training Course in Rawalpindi, Islamabad.

We guide people from every background to change their lives via our career-oriented short-term courses in Rawalpindi. Our evening and online course primarily focus on school, college, university students, and full/part-time employees.

Advantages of Learning APPML

  • Simplified Data Management – Easy data retrieval and display.
  • Reduced Coding Effort – Less manual JavaScript/AJAX needed.
  • Framework-Independent – Works with plain HTML, CSS, and JavaScript.
  • Data-Driven – Efficient data binding to UI elements.
  • Supports JSON – Lightweight and efficient data handling.
  • Easy API Integration – Seamless RESTful API interaction.
  • Dynamic Content – Updates pages without full reloads.
  • Simple Learning Curve – Built on standard web technologies.
 

APPML Training Certification Course Outline

Here’s a comprehensive outline for an Advanced Level Course in Apple Machine Learning (APPML). This course focuses on equipping learners with advanced skills in developing and deploying ML models using Apple frameworks, technologies, and platforms frameworks.

 Module 1:Introduction to Apple Machine Learning Ecosystem

  1. Overview of Core ML
    • Capabilities and applications.
    • Core ML 4: What’s new and advanced features.
  2. Apple ML Ecosystem
    • Integrating Core ML with iOS/macOS apps.
    • Overview of related frameworks (Create ML, Turi Create, and more).

 Module 2:Advanced Core ML Framework

  1. Core ML Model Optimization
    • Techniques for optimizing ML models for mobile deployment.
    • Quantization and pruning for efficiency.
    • Size vs. performance trade-offs.
  2. Custom Layers and Advanced Operations
    • Writing custom layers in Core ML.
    • Handling complex model architectures.
  3. Model Update on Device
    • Implementing on-device model updates using MLUpdateTask.
    • Privacy and security best practices.

 Module 3:Create ML Pro

  1. Custom Dataset Preparation
    • Augmenting datasets for Apple ML tools.
    • Advanced labeling strategies and automated workflows.
  2. Training Custom Models
    • Training custom models for text, vision, and audio domains.
    • Fine-tuning pre-trained models.
    • Evaluating model performance with Create ML tools.
    • Converting models for production use.

 Module 4:Computer Vision

  • Vision Framework
    • Advanced use of Vision framework for image analysis.
    • Custom object detection and segmentation.
  • Real-Time Image Processing
    • Implementing real-time applications with Core ML and Vision.
  • Augmented Reality (AR) Integration
    • Integrating ML models into ARKit.
    • Case studies in AR-based object recognition.

 Module 5:Advanced Natural Language Processing

  • NL Framework Deep Dive
    • Custom tokenization and embedding techniques.
    • Sentiment analysis and text generation pipelines.
  • Speech Framework Integration
    • Building advanced speech-to-text and text-to-speech systems.
    • Incorporating SiriKit with ML models.
  • Transformer Models with Core ML
    • Adapting transformer-based architectures for Apple platforms.

 Module 6:Advanced Audio and Sound Processing

  1. Sound Classification
    • Training and deploying sound classification models.
  2. Advanced Signal Processing
    • Implementing advanced audio features with Core ML.
  3. Integration with AVFoundation
    • Developing applications with synchronized audio and ML features.

 Module 7:Apple Silicon Optimization

  1. Leveraging Apple Silicon (M1/M2+)
  2. Optimizing models for Apple Neural Engine (ANE).
  3. Performance benchmarking on Apple hardware.
  4. GPU and CPU Optimizations
  5. Advanced techniques for resource-efficient ML applications

 Module 8:Privacy-Preserving Machine Learning

  1. Differential Privacy Techniques
    • Implementing privacy-preserving training pipelines.
  2. Federated Learning
    • On-device training and federated learning with Apple frameworks.
    • Students design, develop, and deploy a complex ML-driven application using Apple technologies.

 Module 9: Deployment and Maintenance

  1. Model Conversion and Compatibility
    • Converting models from TensorFlow/PyTorch to Core ML.
    • Ensuring backward compatibility across iOS/macOS versions.
  2. Testing and Debugging ML Apps
    • Advanced debugging techniques with Xcode ML tools.
  3. Continuous Integration/Continuous Deployment (CI/CD)
    • Automating ML model deployment workflows

Experience and Inspiring Trainers:

Our trainers bring their years of industry experience during the course. They are expert and passionate about delivering inspiring training as they know training inside out. They will advise you on all the options to make sure you get the best possible result.
 

Real-time Practice and Projects:

By learning APPML, developers can streamline their web development process while building responsive, data-centric applications with minimal complexity.

 

Prerequisites for APPML Course:

  • Mathematics: Linear Algebra, Calculus, Probability, and Statistics.
  • Programming: Proficiency in Python; libraries like NumPy, Pandas, Scikit-Learn.
  • ML Basics: Supervised/Unsupervised learning, regression, and classification.
  • CS Fundamentals: Algorithms, data structures, and problem-solving.
  • Tools: Familiarity with Jupyter Notebooks; basics of ML frameworks (optional)

Certificate:

Finally completing this training you will receive a course completion certificate along with internship in APPML Training so you can get recognition for your new skills.
 

Course Material:

 Softy Copy notes are briefly included in this course
 

Support and Careers Advice:

In the end our trainers are always ready to help you for any problems or question regarding APPML. We prepare students for facing Interview questions on APPML and help them to build their online resume. Our more than 90% students are placed in good MNCs.

“Our Student Success is Our Mission”​.