The Certified Machine Learning Specialist course provides a fundamental overview of how machine learning works, what it can and cannot do and how it is commonly utilized in support of business goals. This course covers multiple algorithms, methods and models of contemporary machine learning practices to explore how a range of different business problems can be solved by utilizing and combining proven machine learning techniques. Finally, the course labs provide participants with a series of exercises and problems designed to test how they apply their knowledge. Completing these labs will highlight areas for improvement and demonstrate proficiency in machine learning systems and techniques, as they are applied to solve real-world problems.
As an authorized partner of Arcitura, TechnoEdge Learning uses the official curriculum to prepare for the Machine Learning Certification exam. Completion of the Certified Machine Learning course and passing the Machine Learning Specialist Certification Exam (ML90.01) proves proficiency in best practices in machine learning, allowing employers to feel confident that the participant is able to apply the knowledge learned in the course.
The course includes a 12-month subscription to the following digital course materials:
- Video lessons (Modules 1 and 2 )
- Workbook (Modules 1, 2 and 3)
- Exam preparation guide with sample questions (Modules 1, 2 and 3)
- Machine learning algorithms and practices reference supplement (Module 1)
- Mind map poster (Modules 1, 2 and 3)
- Symbol legend poster (Modules 1 )
- Flashcards with sample questions (Modules 1, 2 and 3)
- Patterns and Mechanisms Poster (Module 2)
- Machine Learning Specialist Certification Exam (ML90.01) Voucher
Arcitura Certified Machine Learning Specialist Course Outline
Module 1: Fundamental Machine Learning
- Machine learning business and technology drivers
- Machine learning benefits and challenges
- Machine learning usage scenarios
- Datasets, structured, unstructured and semi-structured data
- Models, algorithms, model training and learning
- How machine learning works
- Collecting and pre-processing training data
- Algorithm and model selection
- Training models and deploy trained models
- Machine learning algorithms and practices
- Supervised learning, classification, decision tree
- Regression, ensemble methods, dimension reduction
- Unsupervised learning and clustering
- Semi-supervised and reinforcement learning
- Machine learning best practices
- How machine learning systems work
- Common machine learning mechanisms
- How mechanisms are used in model training
- Machine learning and deep learning, artificial intelligence (AI)
Module 2: Advanced Machine Learning
- Data exploration patterns
- Central tendency computation, variability computation
- Associativity computation, graphical summary computation
- Data reduction patterns
- Feature selection, feature extraction
- Data wrangling patterns
- Feature imputation, feature encoding
- Feature discretization, feature standardization
- Supervised learning patterns
- Numerical prediction, category prediction
- Unsupervised learning patterns
- Category discovery, pattern discovery
- Model evaluation patterns, baseline modelling
- Training performance evaluation, prediction performance evaluation
- Model optimization patterns
- Ensemble learning, frequent model retraining
- Lightweight model implementation, incremental model learning
Module 3: Machine Learning Lab
This course module presents participants with a series of exercises and problems that are designed to test their ability to apply their knowledge of topics covered in previous courses. Completing this lab will help highlight areas that require further attention and will further prove proficiency in machine learning systems and techniques, as they are applied and combined to solve real-world problems.
Who Should Take This Course
- Entry-level Machine Learning Engineers
- Associate Data Scientists
- Junior Data Scientists
- Operational Data Scientists
