Training: AI en Machine Learning voor Beleidsmakers en Leiders
Data Visualisatie
13 uur
Engels (US)

Training: AI en Machine Learning voor Beleidsmakers en Leiders

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Productinformatie

Deze training biedt kennis van de belangrijkste concepten en praktijken op het gebied van kunstmatige intelligentie (AI) en machine learning (ML). Deze training combineert vier essentiële trainingen: Fundamenten van AI en ML, Een AI/ML-gegevensstrategie ontwikkelen, Data visualiseren voor impact en Cloud computing en MLOps in AI/ML. Aan het einde van de training wordt je kennis getoetst in een examen.

Fundamenten van AI en ML

In deze uitgebreide training ontdek je de kracht van machine learning (ML) en de toepassingen ervan in verschillende industrieën. Leer hoe je patronen kunt ontdekken, nauwkeurige voorspellingen kunt doen en inzichten kunt verkrijgen uit grote datasets. Verdiep je in clustering, classificatie, regressie en geavanceerde methoden van data science, zoals tekstmining, grafiekanalyse, anomaliedetectie, associatieregels en neurale netwerken.

Een AI/ML-gegevensstrategie ontwikkelen

Leer hoe je de kracht van data-analyse kunt benutten om betere zakelijke beslissingen te nemen in verschillende industrieën. Deze training biedt inzichten in het aanpassen van data-analyse aan jouw organisatie. Verken het maturity model voor data-analyse en vergelijk beschrijvende, diagnostische, voorspellende en AI-gerelateerde vormen van data-analyse. Bouw een AI-gedreven team met toegewijde datateams en begrijp de betekenis van ethiek met betrekking tot gegevens in deze context.

Data visualiseren voor impact

Bouw een data-gedreven cultuur door effectieve datavisualisatie onder de knie te krijgen. Creëer toegankelijke en begrijpelijke grafische representaties die teams in staat stellen patronen en trends te herkennen en geïnformeerde beslissingen te nemen. Leer de fundamentele principes, beste praktijken en geavanceerde technieken voor het ontwerpen van overtuigende visuals met behulp van contrast en positie.

Cloud computing en MLOps in AI/ML

Verken het raakvlak tussen technologie en besluitvorming. Ontdek de kracht van cloud computing in AI, inclusief de voordelen, uitdagingen en implementatiestrategieën. Leer over MLOps en de transformerende impact op machine learning en AI-ontwikkeling. Krijg inzicht in ML-pipelines, hun belang, beste praktijken en testmethodologieën.

Inhoud van de training

AI en Machine Learning voor Beleidsmakers en Leiders

13 uur

Fundamentals of AI & ML: Foundational Data Science Methods

Data science methods are used across several industries to deliver value to businesses. Machine learning (ML) is a data science method that uses prediction algorithms that find patterns in massive amounts of data, allowing machines to predict future results and make decisions with minimal human intervention. Through this course, learn foundational methods for using machine learning. Examine what machine learning is, how it is categorized, and common machine learning challenges. Next, learn about common types of machine learning tasks, such as clustering, classification, and regression. Finally, explore the types of regression, including simple and multiple linear regression. Upon completion, you'll be able to define machine learning and methods for using it.

Fundamentals of AI & ML: Advanced Data Science Methods

In data science, many statistical and analytical techniques can be used to pull meaningful insights from data. Additionally, some advanced data science methods rely on other foundation data science methods, such as the case of text mining. Through this course, learn about advanced data science methods and their use cases. Explore advanced machine learning (ML) methods such as text mining and graph analysis and their uses. Next, learn about the anomaly and novelty detection processes. Finally, examine association rule mining and neural networks and their use cases across industries. After course completion, you'll be able to outline advanced methods for data science.

Fundamentals of AI & ML: Introduction to Artificial Intelligence

Artificial intelligence (AI) provides cutting-edge tools to help organizations predict behaviors, identify key patterns, and drive decision-making in a world that is increasingly made up of data. In this course, you will explore the full definition of AI, how it works, and when it can be used. You will identify the types of data tools and technologies AI uses to operate. Next, you will discover a framework for using the AI life cycle and data science process. Finally, you'll consider what you need to keep in mind as you implement AI techniques in your organization. Upon completion of this course, you'll be familiar with common concepts and use cases of artificial intelligence (AI) and be able to outline strategies for each part of the AI life cycle.

Developing an AI/ML Data Strategy: The Data Analytics Maturity Model

Data analytics is used across various industries to help companies make better-informed business decisions. Data analysts capture, process, and organize data in addition to establishing the best way to present that data. Through this course, learn about the uses and benefits of data analytics and the tools to leverage it. Examine the data analytics maturity model and compare the descriptive, diagnostic, predictive, and AI types of data analytics. Next, discover how data analytics can be used across teams and the benefits it offers. Finally, discover the different types of tools designed for data storage, cleaning, visualization, analysis, and collaboration. Upon completion, you'll be able to outline what data analytics is and list common data science tools.

Developing an AI/ML Data Strategy: Building an AI-powered Workforce

Building a successful data team is a key part of a data strategy. To build a proper data team, it's important to know how they are structured and the roles of each member. Through this course, learn how to build an AI-powered workforce with a data team. Discover the need for an AI-powered workforce and three main structure types of a data team. Next, learn how to determine which strategy is preferable for a data team. Finally, explore the roles of data team members, how to evaluate an organization's strategy, and how to move an organization toward a data-driven culture. After course completion, you'll be able to outline the functions and best practices for a data team.

Developing an AI/ML Data Strategy: Data Analytics & Data Ethics

Growing fields of data analytics and artificial intelligence (AI) provide many benefits to individuals and society, but also raise ethical concerns regarding privacy, transparency, and bias. How can organizations collect, store, and use data ethically, and what ethical safeguards must be maintained? Through this course, learn about data ethics and its importance in AI. Explore the concept of data ethics and a manager's role and responsibility to maintain ethical standards on their team. Next, discover the key principles and considerations for data ethics in AI. Finally, learn about data ethics frameworks that are used across a variety of industries. After course completion, you'll be able to identify the importance of data ethics and its concerns and best practices.

Visualizing Data for Impact: Introduction to Data Visualization

Using data visualizations effectively and correctly is a part of building a data-driven culture in your team. Data visualization creates accessible, understandable, and effective graphic representations of data to help teams understand the patterns and trends in their data and make data-driven decisions. In this course, you will learn about the fundamentals of data visualization, why it is important, and how data visualizations can be useful to your team. You will also explore different types of data visualizations, their use cases, and how to interpret them. Finally, you will discover how to select appropriate tools and visualizations. Upon completion of this course, you'll be able to define the fundamental concepts, types, and uses of data visualization.

Visualizing Data for Impact: Visual Design Theory

Visual designs play an important role in the presentation of data. Understanding and implementing visual design principles can help you build data visualizations that effectively communicate the message and make an impact on the target audience. Through this course, learn visual design principles and how to apply them to data visualizations. Explore elements and best practices for designing compelling visuals. Next, learn how to design effective visuals using contrast and position, as well as sizing and grouping visualization items. Finally, discover how to arrange items, use legends, address data set gaps, and use color for visualizations. After course completion, you'll be able to outline and apply visual design best practices to visualize data.

Visualizing Data for Impact: Analyzing Misleading Visualizations

One of the challenges of data visualization is recognizing and avoiding misleading visuals. These and other common mistakes make data visualization less effective and can lead to incorrect conclusions. Through this course, learn about misleading statistics and visual distortions. Examine some common data visualization mistakes, including data overload, interchanging charts, and the use of color, as well as how to recognize and correct them. Next, explore examples of deceiving statistics, visual distortions, and graphs and how to avoid being misleading. Finally, learn about omitting data, improper extraction, and correlating causation. After course completion, you'll be able to avoid mistakes when visualizing your data.

Visualizing Data for Impact: Data Storytelling

Data storytelling lets you set up and reveal key results quickly and in an organized fashion. It is a great way to make findings impactful and meaningful for an audience. Through this course, learn about data storytelling and how it can help elevate your data visualizations and create impactful narratives for an audience. Explore the theory and purpose behind data storytelling and how to contextualize and refine insight. Next, discover how to engage with an audience and put together an outline. Finally, learn how to plot data points to a storyboard and format a story for delivery. Upon completion, you'll be able to outline elements of data storytelling and apply them when presenting data.

Cloud Computing and MLOps: Cloud and AI

Cloud computing is the on-demand delivery of computing services over the Internet. It enables scalable artificial intelligence (AI) and other advantages such as increased speed, scalability, and reduced cost. Through this course, learn about the role of cloud computing in AI. Explore the benefits and challenges of cloud computing, how to implement a cloud AI strategy, and the elements of the cloud computing architecture. Next, discover the importance of AI as a Service (AIaaS), the role of AI tools in data management and governance, and best practices for AI cloud security. Finally, learn about key cloud technologies for AI and emerging trends for cloud computing and AI. After course completion, you'll be able to outline elements of cloud computing in AI.

Cloud Computing and MLOps: Introduction to MLOps

The term MLOps is a combination of machine learning (ML) and DevOps. Used across several industries, MLOps is a valuable method for developing and testing machine learning and artificial intelligence (AI) solutions. Through this course, learn the basics of MLOps. Explore the elements of XOps, MLOps, and DataOps and their uses. Next, examine the importance of version control in machine learning and learn about version control types and tools. Finally, discover the roles and responsibilities of humans in ML pipeline automation and investigate ethical considerations and best practices for MLOps. By the end of this course, you be able to define MLOps and recognize its uses.

Cloud Computing and MLOps: ML Pipelines

ML pipelines help organizations improve the standards of machine learning (ML) models, improve their business strategy, and reduce redundant work and miscommunication. They consist of a series of ML workflow steps performed in a connected and automated/semi-automated way. Through this course, learn the basics of ML pipelines. Discover the uses and benefits of ML pipelines and the characteristics of manual and automated pipelines. Next, explore best practices for building pipelines and the three types of environments in the MLOps process. Finally, examine the importance of CI/CD in ML, the purpose of ML pipeline testing, and ML pipeline testing tools and frameworks. Upon completion, you'll be able to define ML pipelines and their benefits.

Final Exam: AI and ML for Decision-makers

Final Exam: AI and ML for Decision-makers will test your knowledge and application of the topics presented throughout the AI and ML for Decision-makers journey.

Kenmerken

Docent inbegrepen
Bereidt voor op officieel examen
Engels (US)
13 uur
Data Visualisatie
180 dagen online toegang
HBO

Meer informatie

Doelgroep Projectmanager, Manager, Data-analist
Voorkennis

Basiskennis van AI en ML is vereist.

Resultaat

Na afronding van deze training:

  • Heb je een grondig begrip van data science-methoden en hun toepassing in verschillende industrieën.
  • Heb je kennis gemaakt met geavanceerde data science-technieken zoals tekstmining en grafiekanalyse.
  • Weet je hoe je strategieën kunt opstellen voor elke fase van de AI-levenscyclus.
  • Heb je kennis van ethische kwesties en beste praktijken met betrekking tot data.
  • Begrijp je de principes van visueel ontwerp en beste praktijken voor effectieve datavisualisatie.

Positieve reacties van cursisten

Training: Leidinggeven aan de AI transformatie

Nuttige training. Het bestelproces verliep vlot, ik kon direct beginnen.

- Mike van Manen

Onbeperkt Leren Abonnement

Onbeperkt Leren aangeschaft omdat je veel waar voor je geld krijgt. Ik gebruik het nog maar kort, maar eerste indruk is goed.

- Floor van Dijk

Hoe gaat het te werk?

1

Training bestellen

Nadat je de training hebt besteld krijg je bevestiging per e-mail.

2

Toegang leerplatform

In de e-mail staat een link waarmee je toegang krijgt tot ons leerplatform.

3

Direct beginnen

Je kunt direct van start. Studeer vanaf nu waar en wanneer jij wilt.

4

Training afronden

Rond de training succesvol af en ontvang van ons een certificaat!

Veelgestelde vragen

Veelgestelde vragen

Op welke manieren kan ik betalen?

Je kunt bij ons betalen met iDEAL, PayPal, Creditcard, Bancontact en op factuur. Betaal je op factuur, dan kun je met de training starten zodra de betaling binnen is.

Hoe lang heb ik toegang tot de training?

Dit verschilt per training, maar meestal 180 dagen. Je kunt dit vinden onder het kopje ‘Kenmerken’.

Waar kan ik terecht als ik vragen heb?

Je kunt onze Learning & Development collega’s tijdens kantoortijden altijd bereiken via support@aitrainingscentrum.nl of telefonisch via 026-8402941.

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