A Certified Machine Learning Specialist understands how and where machine learning and deep learning technology and techniques are best utilized to produce business value. Machine learning algorithms and system design principles are of second nature to the Certified Machine Learning Specialist, who further possesses in-depth knowledge of deep learning techniques, as well as supervised, semisupervised and unsupervised machine learning processing models and approaches. A Certified Machine Learning Specialist further has an understanding of how machine learning relates to and can be utilized together with data science and artificial intelligence.
A Certified Machine Learning Specialist has demonstrated proficiency in machine learning methods, models and algorithms and can design scalable machine learning systems capable of solving complex business problems.
Some working experience with Business Intelligence and Data Analysis
Engineers, developers, business analysts, data analysts, data science professionals, BI, database and data warehouse professionals, and anyone who is keen to learn more in-depth about Machine Learning and the real applications of ML for business intelligence on this day and age.
Module 1: Fundamental Machine Learning
This module provides an easy-to-understand overview of machine learning for anyone interested in how it works, what it can and cannot do and how it is commonly utilized in support of business goals. The course covers common algorithm types and further explains how machine learning systems work behind the scenes. The base course materials are accompanied by an informational supplement covering a range of common algorithms and practices.
The following primary topics are covered:
- Machine Learning Business and Technology Drivers, as well as 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
This module delves into the many 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.
Primary topics covered:
- 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 Modeling
- 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 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.
For instructor-led delivery of this lab course, the Certified Trainer works closely with participants to ensure that all exercises are carried out completely and accurately. Attendees can voluntarily have exercises reviewed and graded as part of the class completion.
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