Data Science, AI, and ML Training

Web Age's Generative AI course teaches the core concepts and components that power Generative AI. Students explore real-world applications and understand the challenges and ethical considerations this technology may present. This course also covers machine learning and effective prompting techniques.

This Introduction Generative AI (GenAI) training teaches developers how to build intelligent, scalable applications using GenAI and large language models (LLMs).

Our Intermediate Generative AI (Gen AI) training teaches developers advanced techniques like fine-tuning LLMs, Retrieval Augmented Generation (RAG), and Vector Embeddings. Attendees also learn how to integrate LLMs into development pipelines.

Protect your Machine Learning (ML) applications. This ML Security training course teaches developers the specialized secure coding skills needed to protect their ML applications from attacks. Attendees learn how to identify and address potential security vulnerabilities in their applications and how to avoid the security pitfalls of the Python programming language.

This intensive Python for Machine Learning (ML) training course teaches attendees ML concepts, including supervised and unsupervised learning, regression, classification, and clustering. Students learn how to implement ML algorithms in Python, a popular programming language for machine learning.

This Machine Learning (ML) course introduces natural language processing (NLP) and teaches attendees Python Programming basics. Students learn how to use Python to import and manipulate data, perform exploratory data analysis, build machine learning models, and evaluate their performance. The training also covers H2O, a powerful ML platform.

This Prompt Engineering course teaches software developers how to design and develop effective conversational AI applications using sound prompt engineering techniques. Participants learn how to create targeted custom prompts, resulting in more accurate and engaging conversational experiences. This AI course also teaches attendees techniques for designing, refining, and testing prompts.

This Generative AI Prompt Engineering course teaches students how to design and refine prompts for natural language processing (NLP) models. Students learn to select the right inputs, questions, and context to ensure the model generates accurate and relevant outputs. This course also focuses on prompt engineering for generative NLP models.

This ChatGPT training course will teach you the fundamentals of prompt engineering for large language models (LLMs). Attendees learn how to craft effective prompts to guide LLMs in generating the desired output.  Students also learn about different prompting techniques, including zero-shot prompting, few-shot prompting, chain-of-thought prompting, and retrieval-augmented generation.

Modern deep learning challenges leverage increasingly larger datasets and more complex models. As a result, significant computational power is required to train models effectively and efficiently. Learning to distribute data across multiple GPUs during deep learning model training makes possible an incredible wealth of new applications utilizing deep learning. Additionally, the effective use of systems with multiple GPUs reduces training time, allowing for faster application development and much faster iteration cycles. Teams who are able to perform training using multiple GPUs will have an edge, building models trained on more data in shorter periods of time and with greater engineer productivity.

This intensive Data Science and Data Engineering training course teaches architects the theoretical and practical aspects of applying Data Science and Data Engineering methods in real-world scenarios. Students master relevant concepts, terminology, theory, and tools used in this field.  

This Data Science training teaches attendees how to define and scope their data science projects, balancing technical complexities with business requirements for stakeholders. Students learn practical techniques and strategies for identifying challenges, exploring data, creating a project plan, and executing it effectively.

This Data Warehousing course teaches attendees the theories, concepts, domains, techniques, and terminology essential for every business and information technology professional involved in data warehousing. Students also learn best practices for data warehouse development and implementation.

This Python for Data Science training course is ideal for engineers, data scientists, statisticians, and other quantitative professionals looking to hone their Python programming skills. Our experienced instructors will guide you through all the basics, helping you to become a proficient Python programmer.

This Data Science and Machine Learning (ML) with Python training course teaches attendees how to extract meaning from data, empowering you to solve real-world problems, make informed decisions, and uncover hidden patterns. Through hands-on labs and practical exercises, students master essential Python libraries, explore powerful algorithms, and learn to apply real-world best practices to data science and ML.

If you’re an analyst, developer, architect, or technical manager, you will need to use Python in the fields of data science, business analytics, and data logistics. In this intensive 2-day course, we cover both theoretical and practical core concepts of Python and how it applies to these areas.

This course introduces generative artificial intelligence (Gen AI) to software developers interested in using large language models (LLMs) without fine-tuning. After an overview of AI, attendees learn how to plan an AI project, work with Amazon Bedrock, and apply prompt engineering best practices.  In addition, students understand the architecture patterns used to build AI applications with Amazon Bedrock and LangChain.

This Generative AI on AWS course introduces GenAI to software developers interested in using large language models (LLMs) without fine-tuning. After an overview of AI, attendees learn how to plan an AI project, work with Amazon Bedrock, and apply prompt engineering best practices.  In addition, students understand the architecture patterns used to build AI applications with Amazon Bedrock and LangChain.

In this Dialogflow course, master designing customer conversations using Contact Center Artificial Intelligence (CCAI). You’ll use Dialogflow CX to create virtual agents and test them using the simulator. Learn to add functionality to access data from external systems, making virtual agents conversationally dynamic. You'll be introduced to testing methods, connectivity protocols, APIs, environment management, and compliance measures. Learn best practices for integrating conversational solutions with your existing contact center software and implementing solutions securely and at scale.