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Certificate of completion Machine Learning

Machine Learning

15-hour self-paced eLearning course, certificate of completion issued by Edkore (INFOSEC CENTER). Live trainer-led or in-house delivery available on request.

eLearning · 15 hours Level: Intermediate Certificate of completion Romanian / English
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About this Course

Machine learning has become one of the most influential technologies in modern organisations: from predictive analytics and intelligent automation to recommendation systems and advanced decision support, it lets organisations extract value from data and transform how they operate.

Successful adoption, however, takes more than technical implementation. Professionals must understand how models are developed, evaluated, governed and deployed responsibly, while being aware of challenges around data quality, bias, explainability, security and compliance.

What You Will Learn

  • The core concepts, terminology and methodologies of machine learning
  • The difference between supervised, unsupervised and reinforcement learning
  • How machine learning models are trained, validated and improved
  • The importance of data quality, governance and responsible AI practices
  • Identifying practical machine learning opportunities across the organisation

Course Structure 6 modules

Introduction & Foundations of Machine Learning
  • ML, artificial intelligence and data science
  • Categories: supervised, unsupervised, semi-supervised, reinforcement
  • Concepts: training data, features, bias, variance, regularisation
  • Applications, benefits and limits in organisations
Supervised Learning Algorithms & Predictive Modelling
  • Classification and regression in practice
  • Linear/logistic regression, trees, Random Forests, SVM, KNN
  • Preparing and structuring datasets
  • Hyperparameter tuning and validation
Unsupervised Learning & Pattern Discovery
  • Clustering techniques: K-Means, DBSCAN
  • Anomaly and outlier detection
  • Customer segmentation and behavioural analysis
  • Interpreting and validating results
Neural Networks, Deep Learning & Reinforcement Learning
  • Neurons, layers, activation functions, backpropagation
  • Architectures: DNNs, CNNs, RNNs
  • Foundations of reinforcement learning
  • Computer vision, NLP, recommendation systems
Governance, Security & Responsible Adoption
  • Data quality and lifecycle management
  • Cybersecurity for ML systems and data pipelines
  • Bias, fairness, explainability and transparency
  • Ethical, legal and regulatory considerations
Course Closing & Consolidation
  • Structured review across all modules
  • Algorithm families and governance principles
  • Preparation for the final assessment

Who It Is For

Professionals, managers, analysts, technical specialists, innovation teams and decision-makers seeking to understand and apply machine learning within organisational environments.

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