AI Training Courses: Learn to Build Intelligent Systems

If you're interested in learning how to build intelligent systems, there are many AI training courses available that can help you get started.

These courses cover a range of topics, from the basics of machine learning to advanced techniques in natural language processing and computer vision.

Some courses focus on specific areas of AI, such as robotics or data science, while others provide a more general introduction to the field.

By taking an AI training course, you can gain the skills and knowledge you need to build intelligent systems that can solve real-world problems.

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The course structure of AI training courses is designed to equip you with a solid foundation in artificial intelligence.

You'll start by grasping AI principles and ethics, which is a crucial aspect of AI development.

The course covers statistics fundamentals, which is essential for understanding AI algorithms and applications.

You'll also learn Python programming, machine learning, and deep learning concepts, including advanced deep learning concepts.

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The course highlights include test analytics, which is a valuable skill in the AI industry.

Here's a breakdown of the key topics you'll cover:

These topics will give you a comprehensive understanding of AI and prepare you for a career in the field.

Generative AI is a groundbreaking technology that's changing the world, and you can start using it after just a half-day course.

GenAI Fundamentals is a course that will teach you everything you need to know to get started with this technology. You can learn how to build apps, code with AI, and unlock endless possibilities with a two-day course on Generative AI for Application Developers.

Learning to master generative AI can be a game-changer for your business, and you can achieve success with a one-day course on Making ChatGPT & Generative AI Work for You.

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Artificial Intelligence (AI) is unusual because it's everywhere, from our personal assistants like Siri and Alexa to self-driven cars and chess-playing computers. AI is constantly evolving, with experts predicting it will soon be omnipresent.

The technology is growing rapidly, with the Global AI Market Size expected to grow 37% every year from 2023 to 2030. This growth is driven by its transformative impact on various industries.

AI is being adopted in virtually every facet of applications, with businesses investing heavily in the technology. At H2K Infosys, they provide online AI training led by industry experts, demonstrating the demand for AI professionals.

The demand for AI professionals is not being met due to a lack of skilled workers, with AI jobs seeing an upward trend of over 119%. This is reflected in the high annual median pay package of AI specialists, ranging from $95k – $120k.

Generative AI is revolutionizing the way we build applications, and as a developer, you're likely eager to get in on the action. You can learn to completely reimagine the way you build apps, learn to code with AI, and unlock endless possibilities by taking a two-day course on Generative AI for Application Developers.

With this course, you'll gain the skills to start using this groundbreaking new technology. Generative AI is changing the world, and it's exciting to think about the possibilities it holds. In this course, you'll learn everything you need to know to start using GenAI.

One of the key aspects of generative AI is prompt engineering, which involves crafting perfect prompts that align with your tasks and goals. By taking a one-day course on Prompt Engineering, you'll learn how to get consistent output in less time.

If you're looking to incorporate generative AI into your applications, you'll want to learn how to use text generation models and master prompt creation and fine-tuning. You can do this by taking a course on Text Generation for Applications Using Gen AI Studio.

Here are some key skills you'll want to develop as a generative AI developer:

These skills will help you unlock the full potential of generative AI and create innovative applications that will set you apart from the competition. With the right training and knowledge, you can start building applications that are more intelligent, more intuitive, and more user-friendly.

Training and Evaluation is a crucial aspect of AI training courses. Understanding the concepts of under- and overfitting is essential for building accurate models.

To avoid overfitting, it's common to split your data into train, validation, and test sets. This helps you evaluate your model's performance on unseen data.

Resampling techniques like cross-validation can also be used to improve model generalization.

User interviews with empathy can be a game-changer for any organization. This approach combines human empathy with AI to streamline data-collection processes.

Combining AI with empathy-led interview techniques can significantly enhance your research and design methodologies. Want to learn more about it? Check out the course "User & Stakeholder Interviews Using Human Empathy & AI" for a one-day, instructor-led training.

By leveraging AI, you can amplify the potential of your user research and design methodologies. This course will teach you how to harness the technology that’s changing the digital landscape.

With AI-powered tools, you can collect and analyze data more efficiently, allowing you to focus on what matters most – understanding your users' needs and perspectives.

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Model preparation and evaluation are crucial steps in the machine learning process.

Under- and overfitting can occur, which means the model is either too complex and fits the noise in the training data or too simple and fails to capture the underlying patterns.

A train/validation/test split is essential to prevent overfitting and ensure the model generalizes well to new data.

Resampling techniques, such as cross-validation, can be used to evaluate the model's performance on unseen data.

Here's a breakdown of the train/validation/test split:

By following these best practices, you can ensure your machine learning model is robust and accurate.

As you dive into the world of machine learning and AI, it's essential to understand the key topics that are typically covered in training programs. Machine learning algorithms are a crucial part of this, and you can expect to learn about them in courses that cover topics like machine learning classification, where you'll discover techniques such as Support-Vector-Machines (SVM) and Random Forests (RF).

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Confusion matrices and Receiver Operating Characteristic (ROC) curves are also important concepts in machine learning classification. These tools help you evaluate the performance of your models and make informed decisions about how to improve them.

Deep learning is another area of focus in AI training programs, particularly when it comes to classification tasks. You can expect to learn about binary, multi-label, and multiclass classification, as well as how to implement deep learning models.

Artificial intelligence courses often cover a range of topics, including machine learning, neural networks, natural language processing, computer vision, and robotics. These courses are designed to provide a comprehensive understanding of AI technologies and how to apply them in real-world scenarios.

Here's a breakdown of some of the key topics you can expect to cover in an AI training program:

Learning from technical industry experts and instructors who are passionate about developing curriculum around the latest technology trends is also an essential part of any AI training program. By gaining hands-on experience and practical knowledge, you'll be well-equipped to apply AI concepts in real-world scenarios.

Data parallelism is a technique that allows you to train deep learning models on multiple GPUs. This can significantly speed up the training process.

To train deep learning models using data parallelism, you can use techniques such as data-parallel deep learning training on multiple GPUs. This involves working with deep learning tools, frameworks, and workflows to perform neural network training.

Training models with data parallelism can be complex, but it's a crucial step in building powerful AI models. The outputs generated by these models are only as good as the data you've powered them with.

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You can deploy Deep Learning models and create a REST API to make them accessible to others. This involves learning how to deploy Deep Learning models, which can be done through various platforms.

To test your API, you can use Postman, a tool that allows you to send HTTP requests and inspect the responses. This is an essential step in ensuring that your API works as expected.

Some popular platforms for deploying Deep Learning models include Google Cloud and digitalocean. These platforms provide scalable infrastructure and tools for deploying and managing models.

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To create impactful results, you need to know how to craft prompts that produce them. This is where Prompt Engineering comes in, a skill that can help you achieve consistent output in less time.

You can learn how to craft perfect prompts by taking a one-day course, which will teach you everything you need to know about aligning your prompts with your tasks and goals.

Prompt Engineering is especially important for brands, as it can help you avoid creative conformity and produce captivating visual stories.

Building conversational applications is a fascinating field that's rapidly evolving. You can quickly build and deploy production-quality conversational AI applications with real-time transcription and natural language processing (NLP) capabilities.

To get started, you can leverage the same DLI base environment containers used in the courses, available from the NVIDIA NGC catalog. This will help you build production-quality solutions.

For deployment, you can learn how to deploy Deep Learning models, create a REST API, and test APIs via Postman. You can even deploy via Google Cloud or digitalocean.

Here are some popular platforms for deploying conversational AI applications:

These platforms will help you bring your conversational AI applications to life.

You can prove your subject matter competency with an NVIDIA DLI certificate, which supports professional career growth.

These certificates are offered for select instructor-led workshops and online courses, and you can use them to highlight new skills on LinkedIn, potentially boosting your attractiveness to recruiters and advancing your career.

NVIDIA DLI certificates are available for select instructor-led workshops and online courses.

You can access fully configured, GPU-accelerated servers in the cloud to complete hands-on exercises included in the training.

The same DLI base environment containers used in the courses are available from the NVIDIA NGC catalog, allowing you to build production-quality solutions.

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You can access a wide range of AI courses from top universities and institutions. The courses cover various topics, including machine learning, deep learning, natural language processing, and more.

The courses are designed for beginners, with some specializing in specific areas like computer vision or marketing. For example, the University of Virginia offers a course on Artificial Intelligence in Marketing.

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Some courses are free, while others require a fee. You can find courses that fit your schedule, ranging from 1-4 weeks to 3-6 months.

Here's a selection of popular AI courses:

These courses are a great starting point for anyone looking to learn about AI and its applications. You can choose the one that best fits your needs and schedule.

Earning an NVIDIA Deep Learning Institute certificate can be a great way to demonstrate your subject matter competency and support your professional career growth.

NVIDIA DLI certificates are offered for select instructor-led workshops and online courses, and can be used to highlight your new skills on LinkedIn.

These certificates can potentially boost your attractiveness to recruiters and advance your career, making them a valuable addition to your professional profile.

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The NVIDIA DLI Certificate is a valuable credential that can help demonstrate your expertise in AI technologies. It's offered for select instructor-led workshops and online courses.

To earn an NVIDIA DLI Certificate, you'll need to complete a qualifying course, which can be found on the NVIDIA Deep Learning Institute website. The certificate is designed to validate your skillset and can be used to boost your attractiveness to recruiters and advance your career.

You can use your DLI certificate to highlight your new skills on LinkedIn by following these step-by-step instructions to update your profile and add your certificate to the “Licenses and Certifications” section.

The NVIDIA DLI Certificate is a great way to demonstrate your commitment to mastering AI technologies and can open up opportunities for roles such as AI engineer, data scientist, machine learning specialist, and research scientist.

Here's a breakdown of the benefits of earning an NVIDIA DLI Certificate:

If you're looking to get in touch with NVIDIA for questions on deep learning training, you can contact them for self-paced online courses, live instructor-led workshops, educator programs and teaching kits, and enterprise solutions.

NVIDIA offers various products and platforms, including DGX Systems, DGX A100, DGX Station, EGX Platform, Data Center GPUs, Virtual GPU, NVIDIA DRIVE, NVIDIA Isaac, Jetson, and GeForce RTX.

Their resources include CUDA-X AI, NGC Catalog, Data Analytics, Deep Learning SDKs, Deep Learning Frameworks, Conversational AI - Riva, Recommender Systems, and Industry Frameworks.