Best AI Classes Near Me: Find In-Person & Virtual Training

Learn More About AI Courses

Artificial Intelligence (AI) is a field of computer science focused on creating and training computer programs and algorithms that can 'learn' and operate without the need for human intervention. While this technology is often associated with science fiction or, more recently, various chatbots, AI covers an expansive subset of recent developments in computer science. For example, machine learning algorithms, which in the past were mostly used to offer automated recommendations on platforms like Amazon or Netflix, are a subset of AI, and their capacity to 'learn' from large datasets is rapidly increasing.

AI also encompasses large language models, neural networks, and natural language processing algorithms, each of which has different functions. With the rise of OpenAI’s ChatGPT platform and the competing models coming from companies like Meta and Alphabet, learning how to use AI can be a long-term benefit to individuals in almost any field.

What Can You Do with AI Training?

One of the main reasons to learn AI-related skills is that the technology is moving so quickly and developing in so many unique ways that it is hard to predict what you’ll be able to do with it. Artificial Intelligence applications are in the early days of their public release, and they are already miles ahead of what was even imagined to be possible a few years ago. Even in their current state, models like ChatGPT-4o and Dalle-3 can do things that ChatGPT-3 and Dalle-2 were incapable of in a fraction of the time.

Professionals in almost any field can utilize AI applications, particularly LLMs and NLPs, to handle tasks ranging from paraphrasing, summarizing, rewriting, or even creating copy and content, writing snippets of code and overseeing basic debugging tasks, brainstorming pitch ideas, summarizing a report for easier digestion, expanding on a report to get a better look at data or any of hundreds of other tasks (and more APIs and modules are being trained as we speak). In addition, AI can also help with some of the more mundane aspects of the workday. LLMs, for example, can write boilerplate forms and emails or create rough outlines of an agenda meeting by summarizing the notes.

Non-professionals can also use AI daily for many of the same tasks. Writing emails, creating announcements and sending out alerts, even simple creative tasks like brainstorming ideas or proofreading essays, can be handled or improved by AI. The tool can help you learn languages, study for your weekly trivia event, or just experiment with the applications as they become more complex.

What Will I Learn in an AI Course?

Since AI courses tend to focus primarily on how the tools are used in specific fields, what you’ll learn largely depends on the field you are working in. For example, in an AI for graphic design course, you’ll spend time working with Adobe Photoshop’s Firefly AI, while an AI for data analysis course may have you working with more programming-focused GPTs.

Writing Prompts

The most common skill you’ll learn across AI courses is how to write a prompt for a generative AI to interpret and respond to. This sounds easy, but it can be a difficult skill to learn since you can’t assume that the AI has any of the contextual or peripheral information it needs to assist you. This can be done with complex prompts, setting rules for the AI to follow on all prompts (such as feeding it a company-style guide), or just through trial and error. Since most commercially available AI platforms use a limited token system, you want to get your prompts in order and correct, so as not to waste time and energy.

Detecting Biases

It can be difficult at first, but AI models will always reflect the data that they are trained on, even if that data is imperfect. Since human beings are the ones feeding the AI their data, they are prone to feed it bad data, and on occasion, AI (particularly large language models) will become recursive and start prioritizing data that affirms something it was already taught. This can lead to the AI making mistakes based on its own internal assumptions that it is correct. In order to avoid this issue, you’ll want to understand how biases form and how to identify if an AI you are working with has habituations that you want to either curb or be vigilant for.

Proofing and Editing

Commercially available AI models, particularly LLMs, are infamous for their habit of inventing information when they can’t adequately call up relevant information. While this has been mitigated in more recent models, it is still the case that, without sufficient information, LLMs will fill in the blanks with real-sounding information. This means that you’ll need to have a good eye for these kinds of things, and you’ll need to learn how to edit your work properly. In visual AI courses, this also involves checking for significant artifacts and knowing how to modify the assets created or altered by the AI.

Limitations of the Applications

It is also important to learn the limitations of an AI application or LLM to avoid making obvious mistakes or using assets or content created by generative models without considering their limitations. For example, AI models aren’t particularly good at drawing hands or teeth, so if you are working with Firefly, you will want to understand that you won’t really be able to modify some elements of the human form. Likewise, the knowledge of an LMM is fairly cursory, so it is limited in how technical and focused the information it provides can be. In these classes, you’ll learn how to work around these limitations and how to make use of the AI even in contexts where you butt up against those limitations.

AI Integration

Almost no project you work on will be completely handled by AI. One of the most important goals is going to be learning how to integrate the AI algorithm into your projects so that you can get the most out of it with the most efficiency. This means learning how to utilize the AI as another tool or application, like Photoshop (which now has a native AI feature), rather than as something completely disconnected from the other tools that you regularly use in your projects. AI skills are inherently complementary, so all your lessons in an AI course will focus on the specifics of your project and field, since integrating AI into a data science project will significantly differ from integrating it into a graphic design or animation project.

How Hard is It to Learn AI?

The difficulty of learning AI largely depends on how much you want to integrate AI into your work. If you are only looking to use the AI as a tool to help with things like brainstorming (or even mundane tasks like writing emails), you can learn how to write a good prompt and navigate a GPT interface with relative ease. If you want to create a complex, automated system in which an AI writes short descriptions of 10,000 pages of archived text or a complicated system in which the AI handles the bulk of the programming tasks on a software project,

What Are the Most Challenging Parts of Learning AI?

The most challenging aspect of learning how to use AI is understanding what it can and cannot do to help with your project. The last thing you want to do is spend several hours trying to get an AI prompt to accomplish a task that you yourself could have done in two hours. Likewise, you don’t want to turn to AI without knowing what work you’ll need to do to the project after the AI has taken care of its part of the work. This will vary from field to field, but learning how to find the correct level of involvement for AI as a productivity tool is essential to avoid falling into either extreme.

How Long Does It Take to Learn AI?

You can learn to use AI relatively quickly if you are only planning to use the tool for basic tasks, automating simple processes, or other light work. This will still require some training (or a lot of trial and error experimentation), but you won’t need weeks of training to become adept at utilizing AI for simple automation and generative tasks. However, if you are looking to learn more advanced techniques and tricks, you’ll want to spend more time in training programs getting support on the ever-evolving world of AI applications.

Should I Learn AI in Person or Online?

After you’ve chosen to learn AI skills (in whatever field seems most relevant to you), you’ll need to decide whether you want to enroll in an in-person class or an online one. Over the last few years, online training has advanced by leaps and bounds, so no matter which option you choose, you’ll still be able to get a high-quality education working with experienced instructors. However, there are still meaningful differences between the two kinds of courses that you’ll want to consider.

Learning in-person at a dedicated training center offers students a wide range of different benefits, including being able to work directly with your instructors and peers. This can really help ensure that students stay focused and on top of their lessons, and this is one of the major reasons that students tend to prefer in-person training. Students also benefit from getting immediate feedback on their work, and they will likely be able to save some money by not needing to pay for expensive subscriptions to AI platforms and providers. The drawback to these courses is that, because they are held in-person, you can only enroll in classes that you can physically attend, which can limit your options, especially if you don’t live in a big city. You will also need to factor in commuting issues and the added cost of traveling to your classes.

Learning online can help eliminate some logistical concerns for students who still want live training but can’t (or cannot) attend in-person. These classes are held in live, digital classrooms, and students will still be able to work directly with their instructor and get feedback on their work in real time. They can also learn how to take advantage of AI tools from the comfort of their own home office, which can be especially useful for anyone who is a remote worker or a freelancer. The drawback to these classes is that there is still a distance between you and your instructor who you’ll have to navigate. In addition, most AI platforms require users to subscribe to receive tokens, and there are limitations to their usage, so learning from home may require you to strike a more careful balance between using your allotment in class and using them outside of class (be it for homework or personal usage).

Can I Learn AI Free Online?

If you are just looking for lessons on writing simple prompts and editing AI-produced content, you can likely learn the basics for free online. In addition, many new software applications with integrated AI support offer basic lessons in the capabilities and limitations of these features, either in the application or on the publisher’s website. These lessons can help you learn simple prompting skills and will help you build a base of knowledge for the future.

If you are looking to learn more than just how to write simple prompts, or you want to learn those skills without endless trial and error, you will want to enroll in a live training program. These classes aim to provide students with all of the tools they need to become professionals in their fields and, increasingly, learning how to utilize AI is a key component of that skill set.

What Should I Learn Alongside AI?

AI is an interesting case because it is a skill you learn to complement your other, pre-existing skills. You can’t do much with AI in a graphic design context, for example, if you don’t know anything about graphic design already. Thus, if you are looking for skills to complement your AI training, you should think about what you primarily are going to be using the AI to accomplish and how you can learn other skills aimed at accomplishing those goals such as programming languages for aspiring Data Scientists or Adobe Creative Cloud applications for Graphic Designers.

Industries That Use AI

Despite how quickly it is developing, publicly available AI technology is still largely in its infancy, meaning a lot of different industries are experimenting with ways to integrate and utilize these technologies. Because this is in such flux, two or three years from now, this may look incredibly different, but it is worth considering who is using this tech now and why.

Data Science and Analytics

Given that artificial intelligence is built on top of data analytics infrastructure, it is no surprise that one of the key industries utilizing AI also involves big data. Any professional working with large amounts of data can take advantage of AI applications designed to read, organize and interpret this data. Even applications like ChatGPT are becoming more adept at reading large datasets and you can easily find ways to speed up these processes. In addition, learning how to work with AI from a user perspective can help data scientists and machine learning engineers develop better, more robust models.

Retail

The retail industry has become so streamlined that AI is starting to play a major role in everything from identifying proper supply chains to predicting consumer attitudes based on long-term historic data. It is also used to track efficiency, build better internal management systems, and handle simple advertising tasks, like creating the scaffold of a direct mailer campaign. Learning how to use AI tools can help small businesses find clients and customers more efficiently, and it can be used to check over large datasets of consumer information to find correlations that a human user might miss.

Design and Animation

While purely generative AI is incredibly limited in its scope (you aren’t going to become an artist by feeding prompts to Dalle-3), the tools being developed alongside generative AI are particularly useful for artists and graphic designers. This includes tools for digital animators that handle the process of tracking the movement of lips, eyes and other subtle bodily movements, video game developers utilizing generative tools to create procedurally generated content that learns from players, and traditional artists using tools like Firefly to assist in making highly detailed touch-ups to existing images.

Finance and Investment

The finance and investment sector is always looking for new tools to help make consistent RoIs on investments and acquisitions, and AI is no exception. If you work in finance, learning how to use AI applications can be useful for reading and interpreting large datasets, analyzing data to make more informed decisions based on historical precedents, and attempting to filter out the bias inherent in human data analysis. These AI tools can complement other FinTech skills to help make personal and professional investors better at making the right decisions for long-term stability.

AI Classes Near Me

Noble Desktop offers a wide range of different courses for students looking to learn how to utilize AI as part of their professional lives. These classes focus on specific use cases for AI in various fields, so students are encouraged to enroll in the focused course that is most relevant to their needs. None of these classes have additional coding requirements, meaning students won’t need to learn extra coding skills (though some classes have pre-requisite courses that cover coding), but all of the classes do have certain other requirements (varying from class to class).

If you are looking to make use of generative AI while customizing it to fit the needs of your business or enterprise, you can consider enrolling in the Data Science and AI Certificate program. In this course, you'll get training in the finer points of data science techniques, using programming languages like Python to write and train AI and LMM algorithms that can be customized to fit the needs of your current project. In addition, everyone enrolled in the course will receive free admission to the Python for AI: Create AI Apps with Flask & OpenAI course, where you'll learn how to write your own applications and GPTs using Python and OpenAI. This course provides students with a well-rounded and practical AI and Data Science skill set, ensuring that you leave the course ready to start using and creating your own AI tools.

Students who are just looking to learn the basics of utilizing generative AI can enroll in the introductory Generative AI with ChatGPT course. This class focuses on teaching students the basics of using generative AI, from how to construct prompts for the model to the limitations of ChatGPT to the process of writing standardized rules for the chatbot to follow each time it is utilized. This course is largely field agnostic, so it can be useful to enroll in regardless of what kind of AI training you are hoping to pursue.

For students with more specific goals, you can enroll in one of Noble’s focused AI training programs. For example, in the AI for Graphic Design course, students will learn how tools like Adobe Firefly can be used to streamline and improve various graphic design tasks. This course treats AI as another tool in an artist's toolkit rather than a replacement for an artist's hard work and training.

Similarly, there is an AI for Marketing course that focuses on how generative AI can be utilized to improve marketing efforts, track consumer attitudes and build robust marketing plans. It can also handle some of the more rote and repetitive parts of the task such as constructing and filling out formulaic emails. There are also options for AI for Data Analytics, Workplace Productivity, Excel, and Video and Motion Graphics, all of which provide students with focused training on the finer points of utilizing AI in a variety of professional and creative contexts.

AI Corporate Training

Training in the use of AI is also a particularly valuable skill for teams looking to become more productive in their regular tasks. This can include everything from workplace productivity training to learning specific use cases for AI in their specific fields. All of Noble’s AI training courses can be delivered as in-person or live online corporate training events. You will have the option to run a class in your office or in a private digital classroom and a live instructor will guide your team members through all of the major uses of generative AI in the field of your choosing. This training can be customized to fit the needs of your team members and the training can be as comprehensive or introductory as you prefer. If you aren’t interested in a training session for your whole team but still want to offer them the chance to improve their AI skills, you can purchase group vouchers for any of Noble’s open enrollment classes. If you are interested in learning more or scheduling a training event, you can email corporate@nobledesktop.com and an associate can help you right away.

AI Topics

Machine Learning

Machine learning, a subset of artificial intelligence, is vital in tech careers, powering systems to learn from data and refine their algorithms for increased efficiency. It's applied in predictive analytics, automated customer services, and smart system design, shaping roles like Data Scientists and Machine Learning Engineers. These professionals use both supervised and unsupervised learning to extract insights and innovate in areas like intelligent technology, significantly advancing AI's impact in the digital world.

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Data Science

In the realm of artificial intelligence, data science is a distinct subset, centering on deriving insights and patterns from both structured and unstructured data through techniques like statistical analysis and machine learning. This specialization has given rise to tech careers such as Data Analysts and AISstrategists, who apply these skills in big data interpretation, algorithmic development, and AI-driven decision-making, thus playing a pivotal role in the practical application of AI in diverse industries.

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Data Analytics

With artificial intelligence (AI), data analytics plays a crucial role, focusing on interpreting raw data to uncover trends, guided by AI's advanced algorithms for accuracy. This aspect of AI is foundational for tech careers, enabling roles like Business Intelligence Analysts to harness AI in transforming complex data into actionable insights and informed decision-making. Specialists in data analytics are central to the AI ecosystem, turning large amounts of data into strategic assets across different sectors, from enhancing business operations to refining customer interactions.

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Software Engineering

Software engineering involves the design and development of AI-driven applications and systems, integrating AI algorithms to create intelligent, responsive software. This synergy has evolved software engineering roles, introducing AI-focused positions where professionals develop and maintain systems that learn, adapt, and interact, bringing a new dimension to software solutions across industries.

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FinTech

FinTech stands out by applying AI algorithms to financial services, enhancing areas such as automated trading, fraud detection, and personalized banking services. AI's influence in FinTech has created specialized career paths where experts leverage AI tools to revolutionize financial transactions and services, making them more efficient, secure, and user-centric.

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Cybersecurity

AI's role in cybersecurity is to augment threat detection and response mechanisms, using machine learning and pattern recognition to anticipate and mitigate cyber threats. This integration has led to the emergence of AI-enhanced cybersecurity roles, where professionals utilize AI to develop more advanced, proactive security strategies, staying ahead in the evolving landscape of cyber threats.

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Python

Python, with its simplicity and robust library, is a preferred language for AI development and is used extensively in implementing machine learning models and AI algorithms. This has made Python programming a highly sought-after skill in the AI sector, with professionals using it to build, test, and deploy AI applications, making it a valuable tool in a developer's toolkit.

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Finance

Finance integrates AI to enhance data analysis, risk assessment, and algorithmic trading, making financial processes more accurate and efficient. This convergence has led to the creation of specialized finance roles where professionals use AI to drive financial forecasting, portfolio management, and personalized investment strategies, transforming traditional finance practices into innovative, data-driven operations.

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Web Development

Web development has evolved to incorporate AI technologies, enabling the creation of more intuitive, user-responsive websites and applications. This integration allows Web Developers to implement AI-driven features like personalized content, predictive search, and automated customer support, fostering a new generation of adaptive web solutions. The synergy between AI and web development has enhanced the user experience and opened up new career avenues where developers innovate at the intersection of AI technology and web design.

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Digital Marketing

Digital marketing harnesses AI to personalize marketing strategies, analyze consumer behavior, and optimize advertising campaigns. AI's processing of datasets and predicting trends allows Digital Marketers to create highly targeted campaigns and content, revolutionizing how businesses engage with their audience. This fusion of AI and marketing has expanded career opportunities, where professionals leverage AI tools for data-driven insights, enhancing customer experiences and driving marketing innovation.

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AI Classes & Bootcamps

Learn More About AI Courses

What Can You Do with AI Training?

What Will I Learn in an AI Course?

Writing Prompts

Detecting Biases

Proofing and Editing

Limitations of the Applications

AI Integration

How Hard is It to Learn AI?

What Are the Most Challenging Parts of Learning AI?

How Long Does It Take to Learn AI?

Should I Learn AI in Person or Online?

Can I Learn AI Free Online?

What Should I Learn Alongside AI?

Industries That Use AI

Data Science and Analytics

Retail

Design and Animation

Finance and Investment

AI Classes Near Me

AI Corporate Training

AI Corporate & Onsite Training Noble Desktop

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Finance

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