EXIN BCS Machine Learning Award - Including Exam
- Código del Curso MACHL-AWARD
- Duración 1 Día
Otros Métodos de Impartición
Salta a:
Método de Impartición
Este curso está disponible en los siguientes formatos:
-
Cerrado
Cerrado
-
Clase de calendario
Aprendizaje tradicional en el aula
-
Aprendizaje Virtual
Aprendizaje virtual
Solicitar este curso en un formato de entrega diferente.
Temario
Parte superiorThe EXIN BCS Machine Learning Award gives you a clear, structured introduction to machine learning—covering key algorithms, data processing, model training, and real-world applications. You’ll learn how to prepare and transform data, understand supervised and unsupervised learning, and get hands-on insights into programming languages and ML frameworks such as Python, TensorFlow, and Scikit-Learn—even if you’re new to AI.
Updated 14/05/2026
Company Events
These events can be delivered exclusively for your company at our locations or yours, specifically for your delegates and your needs. The Company Events can be tailored or standard course deliveries.
Calendario
Parte superiorDirigido a
Parte superior- Software Developers
- Data Analysts
- Data Scientists
- Business Leaders & AI Strategists
- Project Managers
- Product Managers
- Engineers & Technical Consultants
- Individuals with an interest in AI and a background in science, engineering, knowledge engineering, finance, education, or IT services
Objetivos del Curso
Parte superiorWhat will you learn?
- Gain a structured, easy-to-follow introduction to machine learning fundamentals, including supervised, unsupervised, and semi-supervised learning—even if you’re new to AI.
- Understand regression, classification, clustering, and deep learning—the core techniques behind AI-powered decision-making, automation, and predictive analytics.
- Learn how machines recognize patterns, train on data, and improve over time without needing a PhD in statistics.
- Explore Python, TensorFlow, Scikit-Learn, and R—the leading tools for building ML models, even if you have no prior coding experience.
- Learn how to collect, clean, preprocess, and transform data for machine learning—key skills needed to build accurate and reliable AI models.
- From Netflix-style recommendations and chatbots to fraud detection and cybersecurity, understand how machine learning is driving innovation across industries.
- Get a complete picture of how ML models are trained, tested, fine-tuned, and optimized for real-world deployment.
- Understand the biases, legal concerns, and ethical implications of machine learning to ensure responsible AI implementation.
Contenido
Parte superiorIntroduction to Machine Learning
- Definition and Overview
- Applications of Machine Learning
- Role of Learning Agents
- Concept of Deep Learning
- Purpose and Function of Neural Networks
- Integration with Knowledge-Based Systems
- Data Interaction in Machine Learning
Programming in Machine Learning
- Programming Languages for Machine Learning
- Software Tools: Open Source vs. Proprietary
Machine Learning Algorithms
- Mathematical Foundations
- Common Algorithms in Machine Learning
- Types of Learning: Supervised, Unsupervised, and Semi-Supervised
Practical Applications of Machine Learning
- Problem Identification for Machine Learning Solutions
- Data Preparation and Processing
- Training Machine Learning Models
- Testing and Validation of Models
- Evaluation and Reporting of Results to Stakeholders
Pre-requisitos
Parte superior- No formal prerequisites are required; however, a basic understanding of IT and mathematics is recommended
Certificación de Prueba
Parte superiorExam Details
Duration: 30 minutes
Number of Questions: 18 - of which 2 scenario-based questions worth 2 points each (Multiple Choice)
Pass mark: 65%
Open book: No
Electronic equipment allowed: No
Level: Foundation
Languages: English, Portuguese
