Learn how to harness the power of artificial intelligence to transform your workflow with hands-on training in ChatGPT and Microsoft Copilot. This course will teach you how to generate content, streamline tasks, and integrate AI into business communication, writing, analysis, and productivity tools.
Harness the creative power of AI with this hands-on workshop. Explore the cutting-edge world of artificial intelligence and learn how to generate text, ideas, and more with the innovative technology of ChatGPT.
Leverage the power of cutting-edge AI tools and features in Photoshop, Illustrator, InDesign, and other Adobe apps in this hands-on class.
Learn how to use generative AI to improve your video and motion projects. Create visual and audio assets, integrate AI tools into your daily workflow, and learn how copyright and data confidentiality work when using AI content.
Learn how to incorporate AI in your marketing strategy with this in-depth course. Explore how to use AI-driven tools to help your SEO, SEM, social media marketing, analytics tracking, paid advertising, and more.
Master AI technologies for business, marketing, design, data analytics, and more in this cutting-edge certificate program. In this program, you'll learn how to optimize Excel workflows, create stunning video content, create captivating graphics, analyze data efficiently, boost workplace productivity, and more with the power of generative AI.
Learn Python, SQL, automation, and machine learning to become a Data Scientist. Gain Python programming, data analysis, SQL querying, and predictive modeling skills. Perfect for beginners, this program prepares you for entry-level data science and Python engineering roles. Unlock high-paying job opportunities in the field of data science.
Gain practical skills in analyzing datasets, building predictive models, and creating AI-powered applications using Python and industry-leading tools.
Create, style, and enhance web apps with AI capabilities. In this advanced Python course, you'll develop a powerful sentiment analysis web application with Flask and the OpenAI API integration and learn how to take your coding projects to the next level with AI.
Artificial intelligence (AI) is the field of computer science dedicated to creating machines and systems that can perform tasks requiring human-like intelligence. These tasks range from simple ones, such as recognizing speech or images, to more complex functions like decision-making, problem-solving, and learning from experience. AI systems achieve this by processing large amounts of data, identifying patterns, and making predictions or decisions with minimal human intervention. AI can be broadly categorized into two types: narrow AI, designed for specific tasks like virtual assistants or recommendation systems, and general AI, a more advanced and theoretical form of AI capable of performing any intellectual task that a human can do.
The history of AI dates back to the mid-20th century, with the formal inception of the field occurring at the 1956 Dartmouth Conference, where the term “artificial intelligence” was coined by John McCarthy. Early AI research focused on symbolic reasoning and problem-solving, with pioneers like Alan Turing laying the groundwork for machine intelligence. However, progress was slow due to limited computing power, leading to periods of stagnation known as “AI winters.” Despite these setbacks, the field revived in the late 20th and early 21st centuries, thanks to advances in computing power, data availability, and machine learning algorithms. Today, AI is a rapidly growing field with widespread applications in industries ranging from healthcare and finance to entertainment and public service.
With AI training, you can explore various innovative projects that leverage the power of machine learning, data analysis, and automation to solve complex problems. For instance, you could create predictive models that analyze large datasets to forecast trends in areas like finance, retail, or healthcare. These models can help businesses optimize inventory management, anticipate customer needs, or even predict the likelihood of certain medical conditions based on patient data. AI training also equips you to work on projects involving natural language processing (NLP), enabling you to develop intelligent chatbots, language translation tools, or sentiment analysis systems that can understand and generate human language.
Another exciting application of AI training is in computer vision, where you can develop systems capable of interpreting visual data from the world around you. This could involve creating AI-driven solutions for facial recognition, autonomous vehicles, or medical imaging, where AI helps detect anomalies in X-rays or MRIs with precision that rivals human experts. Additionally, AI training allows you to delve into robotics and automation, where you can design systems that perform complex tasks autonomously, such as drones for environmental monitoring or robots for precision manufacturing.
Beyond these technical applications, AI training enables you to contribute to socially impactful projects such as AI for social good. You could work on initiatives that use AI to tackle environmental challenges, improve public health, or enhance education. For example, AI can be used to analyze climate data to predict natural disasters, develop personalized learning platforms for students, or optimize the distribution of resources in humanitarian efforts. These examples illustrate the vast potential of AI training, offering opportunities to create meaningful solutions across various domains, whether for personal projects, research, or industry innovation.
In an AI class, you’ll learn about machine learning algorithms, which are the core tools used to create AI models. These algorithms enable systems to identify patterns in data and make predictions or decisions based on that data. Understanding different types of machine learning algorithms, such as supervised learning, unsupervised learning, and reinforcement learning, is crucial because they form the foundation of AI applications. You’ll need to learn these algorithms to build and refine AI models, allowing you to tackle a wide range of tasks, from classification and regression to more complex challenges like game playing or autonomous driving.
Data preprocessing is another critical skill you’ll acquire in an AI class. This involves cleaning, transforming, and organizing raw data into a format that can be used effectively by machine learning algorithms. Since AI systems rely on large datasets to learn and improve, mastering data preprocessing is essential for ensuring that the data fed into your models is accurate, relevant, and free from noise. Properly preprocessed data leads to better model performance and more reliable outcomes, making this skill indispensable in the development of AI systems.
Programming, particularly in languages like Python, is a fundamental skill in AI. Python is widely used in the AI community due to its simplicity, readability, and the extensive libraries available for machine learning, data analysis, and deep learning. In an AI class, you’ll learn to write code that implements AI algorithms, manipulates data, and builds models. Programming skills are necessary because they allow you to experiment with AI concepts, develop custom solutions, and deploy AI models into real-world applications, making your work both practical and impactful.
Model evaluation and validation are key components of AI development that you’ll explore in an AI class. These skills involve assessing the performance of AI models using various metrics and techniques, such as cross-validation, to ensure they generalize well to new, unseen data. Learning how to properly evaluate and validate models is crucial because it helps you avoid common pitfalls like overfitting, where a model performs well on training data but poorly on real-world data. By mastering these techniques, you can build AI systems that are both accurate and reliable.
An AI class will also help you develop problem-solving and critical thinking skills, which are essential soft skills in AI. Working with AI involves tackling complex, often ambiguous challenges that require innovative solutions. You’ll need to break down problems, analyze various approaches, and think critically about the trade-offs between different AI techniques. This skill is relevant not only in designing and optimizing AI models but also in deciding when and how to apply AI solutions to real-world problems. Cultivating strong problem-solving abilities enables you to approach AI projects with confidence and creativity.
Learning AI can be challenging, but the difficulty largely depends on your background and how deeply you want to delve into the field. For those with a foundation in programming, mathematics, and data analysis, becoming familiar with basic AI concepts and tools is attainable within a few months to a year of consistent study. This level of understanding allows you to experiment with simple AI projects and grasp the fundamentals of machine learning and data processing. However, mastering AI and becoming an expert is significantly more demanding. It requires in-depth knowledge of complex algorithms, extensive hands-on experience, and the ability to apply AI to solve real-world problems. Achieving this level of expertise typically takes several years of dedicated learning and practice, along with continuous adaptation to the rapidly evolving AI landscape.
The most challenging parts of learning AI often involve mastering the underlying mathematics and complex algorithms that drive AI systems. Concepts like linear algebra, calculus, probability, and statistics are fundamental to understanding how AI models work, but they can be daunting for those without a strong mathematical background. Additionally, the breadth of AI can be overwhelming, with various subfields such as machine learning, natural language processing, and computer vision, each requiring a deep dive into specialized techniques. Another challenge lies in developing the practical skills needed to implement AI solutions effectively, including tuning models, handling large datasets, and managing computational resources. Finally, keeping pace with the rapidly evolving AI landscape, where new techniques and tools are constantly emerging, demands continuous learning and adaptation, which can be time-consuming and intellectually demanding.
Becoming comfortable experimenting with AI typically takes a few months to a year, depending on your programming, mathematics, and data analysis background. During this time, you can learn the basics of machine learning, data preprocessing, and using AI frameworks like TensorFlow or PyTorch, allowing you to build simple models and explore AI projects. However, reaching a professional level in AI—where you can tackle complex problems, design innovative solutions, and apply AI across various industries—usually requires several years of dedicated study and practice. This includes mastering advanced algorithms, gaining extensive hands-on experience, and staying current with the latest developments in the rapidly evolving field of AI.
When deciding whether to learn AI in person or online, it’s important to understand the key differences between in-person learning, live online learning, and asynchronous online learning. Each format has its own advantages and challenges, depending on your learning style, schedule, and personal preferences.
In-person learning offers the benefit of direct interaction with instructors and peers, which can be especially helpful for understanding complex AI concepts. The classroom environment encourages immediate feedback, collaboration, and hands-on support, making it easier to stay engaged and motivated. Being physically present in a structured setting can also help establish a routine and improve focus. However, in-person learning requires a fixed location and schedule, which may not be convenient for everyone—especially those with busy lifestyles or limited access to training centers.
Live online learning provides a flexible alternative while still maintaining structure and real-time interaction. Students can attend sessions from anywhere, ask questions, collaborate with classmates, and receive instant feedback from instructors. This format works well for learners who want guidance and accountability but need the convenience of remote access. The main drawback is that it still requires adherence to a set schedule, and it may lack some of the immediacy and connection of a physical classroom.
Asynchronous online learning offers the most flexibility, allowing students to learn AI at their own pace and on their own schedule. This option is ideal for those balancing school, work, or other responsibilities. However, the lack of real-time interaction can make it harder to get quick feedback or collaborate with others, and it requires strong self-discipline to stay on track without scheduled class times.
Ultimately, the best way to learn AI depends on your individual needs. In-person and live online learning are well-suited for students who benefit from structure and real-time interaction, while asynchronous learning is best for those who need maximum flexibility and are comfortable with self-directed study.
When learning AI, it’s essential to build complementary skills that strengthen your ability to apply AI effectively across different use cases. A solid foundation in programming—especially in languages like Python and R—is critical, as these are widely used in AI development. Understanding data science is equally important because AI systems rely heavily on data. This includes skills in data analysis, data visualization, and working with large datasets, all of which are necessary for training, evaluating, and optimizing AI models. Familiarity with database management and big data technologies such as SQL, Hadoop, and Spark can further enhance your ability to handle the large volumes of data often required for AI projects.
A strong grasp of mathematics and statistics is also vital. Core concepts such as linear algebra, calculus, probability, and statistics form the foundation of many machine learning algorithms and AI models. Knowledge in these areas helps you understand how models work, why they behave the way they do, and how to fine-tune them for better performance. In addition, learning cloud computing platforms like AWS or Azure can be highly beneficial, as they provide the scalable infrastructure needed to train, deploy, and manage AI models in real-world environments.
Finally, developing domain-specific knowledge in the field where you plan to apply AI—such as finance, healthcare, marketing, or manufacturing—can significantly improve your effectiveness. Understanding industry-specific challenges, regulations, and workflows allows you to design AI solutions that are practical, relevant, and impactful. By combining technical skills with domain expertise, you’ll be better equipped to create AI systems that solve real problems and deliver meaningful results.
In Phoenix, AI is increasingly being integrated across several key industries that drive the region’s economy and public services. Known for its growing tech sector, advanced manufacturing base, healthcare systems, and expanding education network, the city is leveraging AI to improve efficiency, modernize operations, and enhance community outcomes. Major employers and organizations such as Intel, Honeywell, Banner Health, and Arizona State University are helping position Phoenix as a rising center for applied AI. The region is also seeing increased AI adoption within nonprofit and public service organizations, where technology supports data-driven decision-making and civic engagement.
In Phoenix, AI is increasingly being integrated across several key industries that drive the region’s economy and public services. Known for its growing tech sector, advanced manufacturing base, healthcare systems, and expanding education network, the city is leveraging AI to improve efficiency, modernize operations, and enhance community outcomes. Major employers and organizations such as Intel, Honeywell, Banner Health, and Arizona State University are helping position Phoenix as a rising center for applied AI. The region is also seeing increased AI adoption within nonprofit and public service organizations, where technology supports data-driven decision-making and civic engagement.
Healthcare is a cornerstone of Phoenix’s economy, supported by organizations such as Banner Health, Mayo Clinic Arizona, Dignity Health, and HonorHealth. These institutions use AI to improve diagnostic accuracy, personalize treatment plans, and streamline administrative processes. AI-driven medical imaging tools help radiologists detect conditions like cancer earlier, while predictive analytics supports chronic disease management and patient outcome forecasting. With healthcare being one of the region’s largest employers, AI is helping improve both patient care and operational efficiency.
Phoenix’s education sector is increasingly adopting AI to enhance learning experiences and institutional effectiveness. Arizona State University (ASU), Maricopa Community Colleges, and other local institutions use AI-powered platforms to deliver personalized learning, track student progress, and identify learners who may need additional support. AI is also used in administrative planning, enrollment forecasting, and curriculum development. As student populations grow and diversify, AI helps educators better meet individual needs and improve academic outcomes.
Phoenix has a vibrant nonprofit and public service ecosystem, including organizations such as Valley of the Sun United Way, St. Vincent de Paul Phoenix, and the City of Phoenix government. AI is increasingly used to analyze community data, identify service gaps, and allocate resources more effectively. Public agencies leverage AI to analyze public feedback, improve transportation planning, and support emergency response efforts. As social equity and civic engagement remain priorities in Phoenix, AI is helping nonprofits and public institutions extend their reach and maximize their impact.
Generative AI Certificate—Noble Desktop
The Generative AI Certificate program led by Noble Desktop offers hands-on training from expert instructors, available in person in NYC or live online (including for learners in the Phoenix area). This comprehensive program is tailored for professionals who want to apply AI across fields such as business, marketing, design, and data analytics. You’ll learn how to optimize workflows in Excel, create compelling video content, design engaging graphics, analyze data efficiently, and boost workplace productivity using generative AI—all without any prior coding experience. With small class sizes, free retakes, setup assistance, and one-on-one mentoring, this program equips non-programmers with practical tools to drive innovation and efficiency in their roles.
AI & Data Science Certificate—Noble Desktop
Noble Desktop’s AI & Data Science Certificate program provides hands-on instruction from industry experts and is offered in person in NYC or live online for students in the Phoenix area. Designed for beginners and career changers, the program builds essential skills in Python, SQL, automation, AI integration, and advanced Python techniques. This immersive curriculum prepares students for careers in data science or AI-focused roles by emphasizing real-world applications and project-based learning. The program includes 174 hours of interactive instruction, one-on-one mentoring, and a professional project portfolio, along with added benefits such as free retakes, setup assistance, and flexible payment plans to support long-term success.
Noble Desktop offers comprehensive corporate digital skills training designed to upskill or reskill your workforce with the latest tools and techniques in AI, data, business, design, and coding. Whether your team needs to enhance its capabilities in AI-driven data analysis, streamline business operations, or develop cutting-edge marketing and design strategies, Noble provides flexible training options to meet your organization’s needs. Training sessions can be conducted onsite at your location, providing a hands-on learning experience in a familiar environment, or live online, allowing your team to participate from anywhere in the world. This flexibility ensures that your employees receive the training they need, tailored to your company’s schedule and specific goals.
In addition to custom training programs, Noble Desktop also offers the option to purchase discounted bulk vouchers for attendance in its regular public enrollment courses. This is an excellent option for companies looking to provide ongoing education for their teams without the need to coordinate specific training sessions. By investing in your team’s AI skills, you empower them to drive innovation and efficiency within your organization. To learn more about how Noble Desktop can support your corporate training needs or to set up a customized training program, contact corporate@nobledesktop.com.
Phoenix professionals have a lot of opportunities when it comes to expanding their knowledge in fields like tech, design, business, and data. Anchored by major businesses in downtown Phoenix and the surrounding areas, such as Tempe, Mesa, and Scottsdale, the city benefits greatly from a diverse economy that spans industries ranging from local government and education to tech and finance, all while maintaining strong connections to the global market. In virtually all Phoenix industries, the reliance on artificial intelligence (AI) tools is growing rapidly. It's no surprise that AI and machine learning experts who can power applications, create automated chatbots, and perform predictive analysis are in high demand. Through Noble Desktop’s network of professional instructors, students in Phoenix can access high-quality training that helps them meet the industry standard. With decades of combined experience, these instructors go beyond the basics and blend practical guidance with real-world insights. This helps students not only acquire technical skills but also the confidence to make it in their professional careers.
Dr. Deanne Larson is a data science and analytics expert with 20 years of experience and counting. She has worked as an advisor for Fortune 500 companies and FinTech organizations through her firm, Larson & Associates LLC. Throughout her career, she has been drawn to work with AI-driven strategies and initiatives, machine learning implementation, and customer data platforms. Additionally, Dr. Deanne has worked on fraud detection models and managed global personalization platforms that have served hundreds of millions of customers. She is also a knowledgeable instructor, having worked for City University of Seattle, where she mentored doctoral students and developed curricula in data and business analytics. Dr. Deanne currently teaches courses in subjects ranging from data and project management to AI and Python.
After 34 years of federal service, Geno Sexton retired in 2012 and now works as a Phoenix-based project management instructor. Throughout these decades, Geno worked for eight years in the Senior Executive Service, culminating as Deputy Chief Human Capital Officer and HR Director at the U.S. Department of Labor, where he led 120 HR professionals serving 18,000 employees. He was named Deputy CHCO of the Year in 2011 and served as the first HR Officer at the new DHS Headquarters. In his distinguished career, Geno has made a real impact and currently teaches project management courses. He is a Vietnam-era Army veteran, holds a Bachelor’s degree in Journalism and Mass Communications and a Master’s degree in Government from New Mexico State University, and is a proud bilingual and ESL-certified instructor.