Practical Machine Learning
Bridging the gap between theory and application with real-world case studies and hands-on implementation experience.
Course Overview
About This Course
Practical Machine Learning is our intermediate-level course designed for professionals who understand ML fundamentals and are ready to apply these concepts to solve real-world business problems. This course bridges the gap between theoretical knowledge and practical implementation.
Over 10 weeks, you'll work through a series of industry-relevant case studies and projects, gaining hands-on experience with the entire ML pipeline from data preparation and model selection to deployment and monitoring. You'll learn how to overcome common challenges faced in real-world ML applications and develop the problem-solving skills needed for successful implementation.
By the end of this course, you'll have built multiple end-to-end ML solutions and gained the confidence to tackle complex ML problems in your professional work. This course is ideal for those looking to take their ML skills to the next level and apply them effectively in business contexts.
What You'll Learn
- End-to-end ML project implementation
- Advanced data preprocessing
- Feature engineering techniques
- Model optimization and tuning
- ML pipeline development
Career Outcomes
- Machine Learning Engineer
- Data Scientist
- ML Solutions Architect
- Technical Product Manager
- AI Implementation Consultant
Who Should Attend
- Data professionals seeking ML skills
- ML Basics graduates
- Software engineers transitioning to ML
- Business analysts with ML knowledge
- Tech managers implementing ML solutions
Course Structure
Hands-on Sessions
Case Studies
Industry Projects
Industry Mentors
This course follows a project-based learning approach, with each module built around real-world case studies and practical implementation. Classes include a mix of guided workshops, collaborative project sessions, and industry expert discussions. You'll spend approximately 60% of your time on hands-on work and 40% on conceptual understanding and strategy.
Course Curriculum
Our 10-week curriculum focuses on the practical application of machine learning to solve real business problems across various industries.
Module 1: The ML Project Lifecycle
Week 1- End-to-end ML project anatomy and workflow
- Problem framing and ML solution design
- Setting up development environments and workflows
- Version control for data science projects
- Case Study: Retail demand forecasting project setup
Module 2: Advanced Data Processing & Feature Engineering
Weeks 2-3- Working with large, complex, and messy datasets
- Automated feature selection and extraction techniques
- Handling categorical variables and text data
- Time series feature engineering
- Feature scaling and transformation strategies
- Project: Customer churn prediction feature engineering
Module 3: Model Selection & Optimization
Weeks 4-5- Systematic approach to model selection
- Advanced ensemble methods
- Hyperparameter tuning strategies
- Cross-validation in complex scenarios
- Handling class imbalance and rare events
- Project: Credit risk modeling optimization
Module 4: ML for Specific Domains
Weeks 6-7- Time series forecasting techniques
- Recommendation systems implementation
- Customer segmentation and clustering
- Anomaly detection systems
- Natural language processing applications
- Projects: Customer recommendation system & Fraud detection
Module 5: ML Model Deployment & Monitoring
Week 8- ML model deployment strategies
- Creating APIs for ML models
- Model performance monitoring
- Detecting and handling concept drift
- Model versioning and updates
- Project: Deploying a prediction API
Module 6: ML in Production & Business Integration
Week 9- ML model testing and quality assurance
- Integrating ML models with business systems
- Model explainability and interpretability
- Ethical considerations and bias detection
- Building ML documentation for stakeholders
- Industry Case Studies: ML implementation challenges and solutions
Module 7: Capstone Project
Week 10- End-to-end industry project implementation
- Working with industry mentors
- Project presentation and technical documentation
- Code review and feedback
- Building a professional ML portfolio
- Course completion ceremony and networking event
Industry Projects
Throughout the course, you'll work on five real-world projects that simulate the challenges faced by ML professionals in various industries. These projects are designed to build your portfolio and demonstrate your ability to solve complex business problems.
Retail Demand Forecasting
Develop a time series forecasting system for a retail chain to predict product demand based on historical sales data, promotions, and external factors like seasonality and holidays.
Customer Churn Prediction
Build a machine learning system to identify customers at risk of cancellation for a telecommunications company, with feature engineering focused on behavior patterns and service usage.
Credit Risk Modeling
Create a loan default prediction model for a financial institution, addressing class imbalance and regulatory requirements while optimizing both precision and recall metrics.
Product Recommendation System
Implement a recommendation engine for an e-commerce platform using collaborative filtering and content-based approaches, with A/B testing framework to evaluate recommendation quality.
Fraud Detection System (Capstone Project)
Design and deploy an end-to-end fraud detection system for online transactions that balances false positives with fraud prevention. This comprehensive capstone project includes real-time scoring, model monitoring, and a visualization dashboard for business users.
Project Mentorship
Each project includes guidance from industry mentors who provide feedback on your approach, implementation, and results. These professionals bring real-world expertise from companies like Oracle, Microsoft, and local Cypriot technology firms.
Your final capstone project will become a showcase piece for your portfolio and can be presented to potential employers during our end-of-course networking event.
Meet Your Instructors
Our courses also feature guest lecturers from industry partners who share real-world case studies and practical insights from their ML implementation experience.
Prerequisites & Requirements
Technical Prerequisites
- Intermediate Python programming skills (functions, classes, libraries)
- Familiarity with pandas, NumPy, and scikit-learn
- Understanding of basic machine learning concepts and algorithms
- Working knowledge of SQL for data retrieval
Knowledge Prerequisites
- Intermediate statistics (hypothesis testing, probability distributions)
- Basic understanding of model evaluation metrics
- Experience with data manipulation and cleaning
- Business acumen to understand problem contexts
Equipment Requirements
- Laptop with at least 16GB RAM and 50GB free storage space
- Operating system: Windows 10+, macOS 10.15+, or Linux (Ubuntu 18.04+ recommended)
- Reliable internet connection for live sessions and cloud computing tasks
Recommended Preparation
To get the most out of this course, we recommend:
- Complete our Machine Learning Basics course or equivalent
- Refresh your Python skills with a focus on pandas and scikit-learn
- Review our pre-course materials which include key ML concepts and Python refreshers
Not sure if your skills meet our prerequisites? Take our free assessment or schedule a call with our admissions team for personalized guidance.
Apply for Practical ML Course
Course Details
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Duration:
10 weeks, July 1 - September 9, 2025
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Investment:
€990 (includes all materials, projects, and certificate)
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Schedule:
Mondays & Wednesdays, 6:30-8:30 PM
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Application Deadline:
June 15, 2025 (Early bird discount until May 31)
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Payment Plans:
Full payment, two or three installments available
Practical Machine Learning Education in Cyprus
The business landscape in Cyprus is increasingly driven by data-informed decision making, with organizations across financial services, hospitality, shipping, and technology sectors seeking professionals who can bridge the gap between theoretical machine learning knowledge and practical implementation. NexaLearn's Practical Machine Learning course addresses this specific need in the Cypriot market.
While theoretical understanding of machine learning is valuable, the ability to apply these concepts to solve real business problems is what truly drives innovation and creates competitive advantage. Our intermediate-level course is specifically designed for professionals who have grasped the fundamentals and are ready to develop the hands-on skills needed to implement ML solutions in real-world scenarios.
Cyprus's position as a growing technology hub connecting Europe, Asia, and Africa presents unique opportunities for machine learning professionals who can demonstrate practical expertise. The island's diverse economy—with strengths in financial services, tourism, shipping, and emerging technology—provides rich contexts for applied machine learning, from predictive analytics and customer segmentation to fraud detection and operational optimization.
What sets NexaLearn's approach apart is our focus on industry-relevant projects guided by experienced practitioners. Students work with actual datasets and business scenarios encountered in Cypriot industries, ensuring that the skills developed are directly applicable to local market needs. This practical orientation, combined with mentorship from industry experts, prepares graduates to make an immediate impact in their organizations.
As Cyprus continues to develop its digital economy, professionals with practical machine learning expertise are positioned to lead the technological transformation across sectors. NexaLearn's Practical Machine Learning course offers a structured pathway to develop these in-demand skills, supporting both individual career advancement and the broader development of Cyprus's data science ecosystem.