Data Science, AI, and ML Training

Master in-demand Data Science, Artificial Intelligence, and Machine Learning courses skills, including using Generative AI, crafting effective prompts, leveraging Nvidia's cutting-edge hardware, and learning Python, the go-to language for data science. Our industry-aligned curriculum, taught by expert instructors, provides an interactive learning experience from beginner to expert.

Leverage the Power of AI to Streamline Processes and Make Predictions

Machine learning (ML) is a subset of Artificial Intelligence (AI) that allows computers to learn from data without any programming intervention. ML algorithms use data to identify patterns and learn from them, making it possible to automate tasks and predict outcomes.

Web Age Solution’s live, instructor-led AI and ML training courses are taught by data science experts who are also seasoned trainers. All courses incorporate real-world techniques and hands-on practice to build AI applications tailored to your group.

For role-specific AI/ML upskilling journeys, see our programs for :

Data Scientists, ML Engineers, Software Engineers

Data Engineers, DevOps/Architects, Analysts, Managers

Machine Learning and Deep Learning Training

Master data-driven prediction with our Machine Learning (ML) & Deep Learning programs. Designed for all levels, our ML training builds a strong foundation in core concepts and delves into advanced deep learning techniques. Unlock text data with Natural Language Processing (NLP), gain proficiency in Python, the go-to language for data science, and explore powerful neural networks and cutting-edge architectures.

Our Top Courses
Prompt Engineering and Copilot Training

Unlock your creative potential using Generative AI with Web Age's Prompt Engineering and Copilot courses. Attendees master the art of crafting and refining prompts in AI models, like OpenAI's GPT and GitHub Copilot, to elicit custom responses. Our courses teach students to use Generative AI to generate text, code, images, code, and more. Learn from industry experts, master advanced prompting techniques, and explore diverse use cases across industries. 

Our Top Courses
Prompt Engineering for Developers: Mastering the Art of Conversational AI
Delivery: On-Site or Instructor-led Virtual

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.

Prompt Engineering: Techniques and Best Practices
Course ID: WA3307
Delivery: On-Site or Instructor-led Virtual

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 such as GPT (Generative Pre-trained Transformer).

ChatGPT Basics
Course ID: WA3403
Delivery: On-Site or Instructor-led Virtual

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.

Nvidia Data Science, AI, and ML Training
NVIDIA is the chip maker that became an AI superpower. With its invention of the GPU in 1999, NVIDIA sparked the growth of the PC gaming market, redefined computer graphics, ignited the era of modern AI, and is fueling industrial digitalization across markets. NVIDIA is now a full-stack computing company with data-center-scale offerings that are reshaping industry.

Our Top Courses
Data Parallelism: How to Train Deep Learning Models on Multiple GPUs
Course ID: NV-DP-GPU
Delivery: On-Site or Instructor-led Virtual

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.

Fundamentals of Accelerated Computing with CUDA Python
Delivery: On-Site or Instructor-led Virtual

This workshop teaches you the fundamental tools and techniques for running GPU-accelerated Python applications using CUDA® GPUs and the Numba compiler. You’ll work though dozens of hands-on coding exercises and, at the end of the training, implement a new workflow to accelerate a fully functional linear algebra program originally designed for CPUs, observing impressive performance gains. After the workshop ends, you’ll have additional resources to help you create new GPU-accelerated applications on your own.
Fundamentals of Accelerated Computing with CUDA C/C++
Delivery: On-Site or Instructor-led Virtual

This workshop teaches the fundamental tools and techniques for accelerating C/C++ applications to run on massively parallel GPUs with CUDA®. You’ll learn how to write code, configure code parallelization with CUDA, optimize memory migration between the CPU and GPU accelerator, and implement the workflow that you’ve learned on a new task—accelerating a fully functional, but CPU-only, particle simulator for observable massive performance gains. At the end of the workshop, you’ll have access to additional resources to create new GPU-accelerated applications on your own.
Data Science Training

Master Data Engineering, Data Science with Python, and more with Web Age's live, instructor-led Data Science training courses. Our Data Science courses are live, hands-on, and taught by data experts with real-world knowledge and extensive teaching experience.

Our Top Courses
Data Science Programming with Python Training

Python is the most popular programming language for Data Science. Our Python for Data Science training courses teach attendees how to code in Python for their data projects and include hands-on labs to help develop real-world skills.

Our Top Courses
AWS Data Science, AI, and ML Training

Whether you're looking to build generative AI applications on AWS, streamline MLOps workflows, or dive deeper into deep learning and machine learning fundamentals, our AWS AI courses teach you the skills to master the AWS cloud for data science success and AI AWS certification.

Our Top Courses
The Machine Learning Pipeline on AWS
Course ID: AWS-ML-PL
Delivery: On-Site or Instructor-led Virtual

This course explores how to the use of the iterative machine learning (ML) process pipeline to solve a real business problem in a project-based learning environment. Students will learn about each phase of the process pipeline from instructor presentations and demonstrations and then apply that knowledge to complete a project solving one of three business problems: fraud detection, recommendation engines, or flight delays. By the end of the course, students will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem. Learners with little to no machine learning experience or knowledge will benefit from this course. Basic knowledge of Statistics will be helpful.
Developing Generative AI Applications on AWS
Delivery: On-Site or Instructor-led Virtual

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.

Developing Generative AI Applications on AWS with Hands-On Labs
Delivery: On-Site or Instructor-led Virtual

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.

Azure Data Science, AI, and ML Training

Master the art of data science, AI, and machine learning on Azure with our comprehensive Azure Data Science courses.  Become certified with our official Microsoft Fabric courses, build robust data pipelines with Azure Data Bricks, and become proficient in data engineering on Azure. Our curriculum also covers foundational courses, incluidng Azure Data Fundamentals (DP-900), ensuring you have a solid understanding of the Azure cloud platform.

Our Top Courses
Migrate SQL Server workloads to Azure SQL Database
Course ID: DP-3001
Delivery: On-Site or Instructor-led Virtual

You'll learn how to assess SQL Server components and compatibility for migration using the Azure SQL Migration Extension and Database Migration Assistant. This training guides you through the process of provisioning and configuring Azure SQL Database resources. You gain hands-on experience in choosing the best migration option to meet business requirements for downtime, handling migration state, and monitoring database migration. Additionally, you'll also learn to perform post-migration tasks like disaster recovery and monitoring for Azure SQL Database. These skills are essential for ensuring a smooth, efficient transition to Azure SQL Database, and maintaining its operation post-migration.
Train and Deploy a Machine Learning Model with Azure Machine Learning
Course ID: DP-3007
Delivery: On-Site or Instructor-led Virtual

To train a machine learning model with Azure Machine Learning, you need to make data available and configure the necessary compute. After training your model and tracking model metrics with MLflow, you can decide to deploy your model to an online endpoint for real-time predictions. Throughout this learning path, you explore how to set up your Azure Machine Learning workspace, after which you train and deploy a machine learning model.
Microsoft Azure Data Fundamentals
Course ID: DP-900T00
Delivery: On-Site or Instructor-led Virtual

In this course, students will gain foundational knowledge of core data concepts and related Microsoft Azure data services. Students will learn about core data concepts such as relational, non-relational, big data, and analytics, and build their foundational knowledge of cloud data services within Microsoft Azure. Students will explore fundamental relational data concepts and relational database services in Azure. They will explore Azure storage for non-relational data and the fundamentals of Azure Cosmos DB. Students will learn about large-scale data warehousing, real-time analytics, and data visualization.

Google Cloud Data Science, AI, and ML Training

Our official Google Cloud training for Data Science, AI, and ML teaches attendees to master machine learning fundamentals, craft data models for insights (LookML),  design engaging customer experiences using Contact Center AI (Dialogflow) , generate text on Gen AI Studio, practice responsible AI, and more - all on Google Cloud's powerful platform.

Our Top Courses
Machine Learning on Google Cloud
Course ID: GCP-ML
Delivery: On-Site or Instructor-led Virtual

This course introduces the artificial intelligence (AI) and machine learning (ML) offerings on Google Cloud that support the data-to-AI lifecycle through AI foundations, AI development, and AI solutions. It explores the technologies, products, and tools available to build an ML model, an ML pipeline, and a generative AI project. You learn how to build AutoML models without writing a single line of code; build BigQuery ML models using SQL, and build Vertex AI custom training jobs by using Keras and TensorFlow. You also explore data preprocessing techniques and feature engineering.
Google Cloud Big Data and Machine Learning Fundamentals
Course ID: GCP-BD-ML
Delivery: On-Site or Instructor-led Virtual

This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.
Customer Experiences with Contact Center AI - Dialogflow CX
Delivery: On-Site or Instructor-led Virtual

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.

Other Data Science, AI, and ML Training
Applied Linear Algebra, Calculus, and Statistics for AI, ML, and Data Science
Course ID: WA3418
Delivery: On-Site or Instructor-led Virtual

Understanding the core concepts of artificial intelligence, machine learning, or data science is impossible without knowing the fundamentals of linear algebra, calculus, statistics, and probability. This training course teaches the essentials in the respective fields of knowledge to prepare the learners to start or advance their careers in AI, machine learning, or data science.

Spark and Machine Learning at Scale
Course ID: WA3290
Delivery: On-Site or Instructor-led Virtual

This Spark and Machine Learning training teaches participants how to build, deploy, and maintain powerful data-driven solutions using Spark and its associated technologies. The course begins with an introduction to Spark, its architecture, and how it fits into the Hadoop and Cloud-based ecosystems. Participants learn to set up Spark environments using DataBricks Cloud, AWS EMR clusters, and SageMaker Studio. In addition, students learn about Spark's core functionalities, including RDDs, DataFrames, transformations, and actions.

Building Applications with Large Language Models
Delivery: On-Site or Instructor-led Virtual

In this Large Language Models (LLMs) course, participants learn how to build practical, innovative, and impactful applications using LMMs like ChatGPT. The course covers model selection, API integration, and prompt engineering. Participants also explore techniques for optimizing AI-generated content to ensure content safety and address biases in AI-driven applications.

Frequently Asked AI Questions
What is Artificial Intelligence?

The replication of human intellectual processes by machines, particularly computer systems, is known as artificial intelligence. Expert systems, natural language processing, speech recognition, and machine vision are examples of AI applications.

Why do we need artificial intelligence?

Today, the amount of data that is generated, by both humans and machines, far outpaces humans’ ability to absorb, interpret, and make complex decisions based on that data. Artificial intelligence forms the basis for all computer learning and is the future of all complex decision making.

There are many useful applications of AI that have changed our lives such as Google Maps, self-driving cars, automated marketing, e-commerce recommendation systems, automated fraud detection, and many more.

What is Machine Learning and how does it differ from Artificial Intelligence?

Artificial Intelligence is the broader concept of endowing machines with intelligence, whether it is emotional intelligence, social intelligence, logical intelligence, planning, creativity, etc.

Machine Learning is a subfield of AI, it can be seen as a way to implement decision-making in AI and getting computers to learn.

Can I take AI Training online?

Yes! Our AI training is available as “onsite live training” or “online live training”. Onsite live AI training can be carried out locally on customer premises or in Web Age corporate training centers. Our live online AI training is carried out by way of an interactive, remote desktop.

We can customize any AI course to meet your team’s experience levels and goals.

What are the pros of AI?

Since the hype generated for Artificial Intelligence in the modern era is massive, it has a lot of pros.

Apart from the many job opportunities created by AI, it also has other pros, such as the completion of looping or repetitive tasks that humans need to perform without the disadvantage of a human-prone error.

AI has the ability to perform faster computations compared to human speed on a wide range of problems with precise results. There are also many real-life applications to make our daily lives simpler. The pros of Artificial Intelligence are limitless.