Advanced Machine Learning
Master cutting-edge ML techniques including deep learning, reinforcement learning, and specialized applications for those seeking expert-level proficiency.
Course Overview
About This Course
Advanced Machine Learning is our most sophisticated course, designed for professionals who already have a strong foundation in ML and are looking to master cutting-edge techniques and specialized applications. This intensive program will take your skills to expert level, preparing you for the most complex challenges in the field.
Over 12 weeks, you'll dive deep into advanced topics including deep learning architectures, reinforcement learning, natural language processing, and computer vision. The curriculum includes both theoretical depth and practical implementation, with a focus on developing systems that solve complex, real-world problems.
The course features four specialization tracks, allowing you to develop expert-level knowledge in a specific domain while maintaining a broad understanding of advanced ML concepts. With guidance from experienced researchers and industry leaders, you'll complete a sophisticated capstone project that demonstrates your expertise.
What You'll Learn
- Advanced deep learning architectures
- Reinforcement learning algorithms
- NLP and transformer models
- Computer vision techniques
- ML systems design & optimization
Career Outcomes
- Senior ML Engineer
- ML Research Scientist
- AI Technical Lead
- ML Solution Architect
- AI Consultant
Who Should Attend
- Experienced ML practitioners
- Data scientists seeking specialization
- Graduates of our Practical ML course
- Software engineers with ML expertise
- AI/ML team leads and architects
Course Structure
Advanced Sessions
Specialization Tracks
Research Project
Expert Mentors
This intensive course combines theoretical depth with practical implementation. Core modules cover foundational advanced concepts, while specialization tracks allow you to develop expertise in a specific area. Classes include lectures from ML researchers, hands-on implementation sessions, paper discussions, and project work. The final four weeks are dedicated to a substantial capstone project in your chosen specialization.
Course Curriculum
Our 12-week curriculum provides comprehensive coverage of advanced machine learning topics with theoretical depth and practical implementation experience.
Module 1: Deep Learning Foundations
Weeks 1-2- Advanced neural network architectures
- Optimization techniques for deep learning
- Convolutional and recurrent architectures
- Attention mechanisms and transformers
- Deep learning frameworks: PyTorch and TensorFlow
- GPU optimization and distributed training
Module 2: Advanced Computer Vision
Week 3- State-of-the-art CNN architectures
- Object detection and segmentation
- Visual recognition and understanding
- Generative models for images (GANs, VAEs)
- Video analysis and action recognition
Module 3: Natural Language Processing
Week 4- Modern transformer models (BERT, GPT, T5)
- Transfer learning for NLP
- Text generation and language modeling
- Question answering and dialogue systems
- Cross-lingual models and machine translation
Module 4: Reinforcement Learning
Week 5- Markov decision processes
- Policy gradient methods
- Deep Q-learning and its variants
- Actor-critic methods
- Multi-agent reinforcement learning
- RL in continuous action spaces
Module 5: Advanced ML Systems & MLOps
Week 6- ML system architecture and design patterns
- Model serving and deployment at scale
- ML pipelines and workflow orchestration
- Model monitoring and management
- ML system testing and reliability
- ML infrastructure and DevOps practices
Module 6: Research Methodologies & Current Advances
Week 7-8- Reading and analyzing research papers
- State-of-the-art ML techniques
- Designing ML experiments
- Current research trends and open problems
- Specialization track preparation
- Capstone project planning and proposal development
Module 7: Specialization & Capstone Project
Weeks 9-12- Focused study in chosen specialization track
- Expert mentorship in specialization area
- Implementation of advanced capstone project
- Weekly project reviews and feedback
- Technical documentation and paper development
- Final project presentation to industry partners
Specialization Tracks
In the final four weeks of the course, you'll select one of four specialization tracks to develop deep expertise in a specific domain of machine learning.
Computer Vision & Image Processing
Develop expertise in advanced computer vision techniques, from object detection and segmentation to generative models for image synthesis and video analysis.
Potential Capstone Projects:
- Multi-stage object detection for satellite imagery
- Medical image segmentation for diagnostic support
- Video-based human activity recognition system
Track Mentor:
Dr. Mikhael Podorski, Computer Vision Researcher
Natural Language Processing
Master advanced NLP techniques using transformer architectures, learning to build sophisticated language understanding and generation systems.
Potential Capstone Projects:
- Financial document analysis and summarization
- Multilingual customer support chatbot
- Legal contract analysis and risk identification
Track Mentor:
Dr. Larissa Viskovitch, NLP Specialist with 10+ years experience
Reinforcement Learning & Decision Systems
Specialize in reinforcement learning for complex decision-making systems, from game AI to industrial optimization and control.
Potential Capstone Projects:
- Adaptive trading strategy using RL
- Multi-agent system for supply chain optimization
- RL-based resource allocation for cloud computing
Track Mentor:
Kazimir Chekhovsky, RL Research Engineer at DeepMind
MLOps & Production ML Systems
Focus on designing, deploying and maintaining robust machine learning systems in production environments at scale.
Potential Capstone Projects:
- Real-time ML pipeline for anomaly detection
- Continuous learning system with human feedback
- Multi-tenant ML serving infrastructure
Track Mentor:
Miroslav Leoridze, Lead MLOps Engineer at Microsoft Azure
Specialization Selection Process
At the end of Week 8, you'll select your specialization track based on your interests and career goals. Each track includes:
- Dedicated specialized curriculum and mentorship
- Advanced workshops with industry practitioners
- Guided capstone project development
- Specialized technical resources and tools
Your specialization track will be noted on your certificate and will help position you for specific roles in the ML ecosystem.
Meet Your Instructors
Arkady Vekshilev
MLOps Specialist
Senior ML Engineer at Amazon with expertise in designing and deploying ML systems at scale.
Dr. Christov Laurenski
Computer Vision Expert
Former Computer Vision researcher at OpenAI with multiple publications on generative models.
Elennova Yasimirska
RL Researcher
Specializes in multi-agent reinforcement learning with industry applications in finance and robotics.
Our course also features guest lectures from leading ML researchers and practitioners from organizations like Google AI, NVIDIA, and European research institutions.
Prerequisites & Requirements
Technical Prerequisites
- Advanced Python programming skills including object-oriented design
- Experience with machine learning libraries (scikit-learn, TensorFlow or PyTorch)
- Strong understanding of machine learning algorithms and concepts
- Familiarity with Linux command line and Git
Knowledge Prerequisites
- Advanced statistics and probability theory
- Linear algebra and calculus (gradients, derivatives)
- Understanding of deep learning fundamentals
- Experience implementing ML solutions for real problems
Equipment Requirements
- High-performance laptop with minimum 16GB RAM, 8-core CPU, and 100GB free storage
- Dedicated GPU recommended (access to cloud GPU resources will be provided)
- Reliable high-speed internet connection for video sessions and cloud computing
Application Process
Due to the advanced nature of this course, admission is selective and requires an application process:
- Submit your application with CV/resume and background information
- Complete a technical assessment to demonstrate ML knowledge
- Short interview with course instructors (for selected candidates)
- Admission decision within one week of completing all steps
We strongly recommend completing our Practical Machine Learning course or equivalent before applying, though exceptional candidates with demonstrated experience may be admitted directly.
Apply for Advanced ML Course
Course Details
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Duration:
12 weeks, August 5 - October 28, 2025
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Investment:
€1,490 (includes materials, specialized tools, cloud resources, and expert certification)
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Schedule:
Tuesdays & Thursdays, 6:00-9:00 PM
+ Saturday workshops (bi-weekly) -
Application Deadline:
July 15, 2025 (Early applications recommended)
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Payment Plans:
Full payment, two or three installments available
Advanced Machine Learning Education in Cyprus
As artificial intelligence continues to transform industries globally, Cyprus is emerging as a strategic location for advanced AI expertise in the Mediterranean region. The demand for specialists with sophisticated machine learning knowledge has grown exponentially, as organizations seek to implement cutting-edge AI solutions that provide competitive advantages in an increasingly data-driven economy.
NexaLearn's Advanced Machine Learning course represents the highest level of ML education available in Cyprus, designed specifically for professionals who are ready to develop expert-level knowledge in specialized domains such as deep learning, reinforcement learning, natural language processing, and computer vision. Unlike introductory courses that provide broad fundamentals, this advanced curriculum explores the theoretical underpinnings and practical implementations of state-of-the-art techniques that define the current frontiers of AI research and application.
What distinguishes this course is its specialization tracks, which allow practitioners to develop deep expertise aligned with their professional aspirations and industry needs. With Cyprus's growing sectors in finance, shipping, tourism, and technology, there is particular demand for ML specialists who can apply advanced techniques to solve complex problems specific to these domains. The course's project-based approach ensures that participants develop not only theoretical knowledge but also the practical implementation skills required to deploy sophisticated ML systems in production environments.
The instructional team includes internationally recognized researchers and industry practitioners who bring both academic rigor and real-world experience to the classroom. This combination is essential for advanced ML education, where the translation from cutting-edge research to practical application requires nuanced understanding and experience. Through mentorship from these experts, students develop the critical thinking and creative problem-solving abilities needed to innovate in this rapidly evolving field.
For professionals in Cyprus looking to position themselves at the forefront of artificial intelligence, advanced machine learning expertise represents one of the most valuable and transferable skill sets in today's technology landscape. Whether pursuing research-oriented roles, specialized engineering positions, or technical leadership opportunities, graduates of this program are equipped to drive AI innovation both within Cyprus and in the broader European and Mediterranean markets.