At Matza Education, each training course is designed to offer practical and relevant knowledge, connecting theory and application in real-life scenarios. Our aim is to prepare professionals for the challenges of the market, strengthening technical and strategic skills in different areas of technology and management.
By taking part in one of our programs, you will have access to up-to-date content, experienced instructors and a results-oriented methodology. Regardless of the format - face-to-face or online - we aim to create a dynamic, accessible and high-impact learning experience.
More than just a course, each training is an opportunity for professional and personal development, helping you to gain certifications, expand your skills and stand out in an increasingly competitive market.
Important: you must confirm the e-mail you received after registering to validate your participation.
Enterprise, corporate, or SMB business professionals in non-technical roles.
Roles include but are not limited to: business analysts, IT managers, project managers, product managers.
For senior VPs and above, Data Driven Transformation with Google Cloud is more suitable.
- Gain a thorough understanding of how ML can be used to improve business processes and create new value.
- Explore common machine learning use cases implemented by businesses.
- Identify the requirements to carry out an ML project from assessing feasibility, to data preparation, to model training, to evaluation, to deployment.
- Define data characteristics and biases that affect quality of ML models.
- Recognize key considerations for managing ML projects including data strategy, governance, and project teams.
- Pitch a custom ML use case that can meaningfully impact your business.
- No prior technical knowledge is required.
- Savvy about your own business and objectives.
- Recommended: completing the Business Transformation with Google Cloud course.
2 days - 16 hours
- The course includes presentations, demonstrations, and immersive activities.
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Module 1: Introduction
- Overview: what is machine learning?
- Key terms: Artificial intelligence, machine learning, and deep learning.
- Real-world examples of machine learning.
- Overview: five phases in a machine learning project.
- Phase 1: Assess the ML use case for specificity and difficulty.
- Brainstorm a minimum of three custom ML use cases.
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Module 2: What is Machine Learning?
- Common ML problem types.
- Standard algorithms.
- Data characteristics.
- Predictive insights and decisions.
- More real-life ML use cases.
- Why ML now.
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Module 3: Employing ML
- Features and labels.
- Building labeled data sets.
- Training an ML model.
- Evaluating an ML model.
- General best practices.
- Human bias and ML fairness.
- Part 1: custom ML use case proposal.
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Module 4: Discovering ML Use Cases
- Replacing rules with machine learning.
- Automating business processes with machine learning.
- Understanding unstructured data with machine learning.
- Personalizing applications with machine learning.
- Creative use cases with machine learning.
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Module 5: How to Be Successful at ML
- Key considerations.
- Formulating a data strategy.
- Developing governance around uses of machine learning.
- Building successful machine learning teams.
- Creating a culture of innovation.
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Module 6: Summary
- Summary, presentations, feedback form.