Discover how to extract business value from AI. Learn to scope opportunities for AI, create POCs, implement solutions, and develop an AI strategy.

Explore techniques for implementing a huge integer class in programming, focusing on advanced data structures and algorithms for handling large numbers.

Training a machine learning model is only the first step. This course will teach you how to deploy, monitor, and scale your machine learning solution in preparation for the Machine Learning Specialty exam.

TensorFlow is popular a library for implementing a range of deep learning solutions but is especially useful for solutions that deal with images. This course will teach you the basics of how to use TensorFlow to implement the most typical scenarios.

In this course, you will learn how you can develop your machine learning workflow using RapidMiner Studio, a data science platform for data preparation, machine learning, and predictive model deployment.

In this course, you will learn how you can develop your machine learning workflow using Weka, an open-source machine learning software for data preparation, machine learning, and predictive model deployment.

This course covers implementing neural network solutions and models in an enterprise. The topics include model deployment strategies, monitoring systems in production, integration with ETL and databases, and security.

TensorFlow is a widely-used data science and machine learning software library. This course will teach you the basics of implementing predictive analytics using TensorFlow, including supervised learning, recommendation, and reinforcement systems.

Enhance customer support efficiency using open-source Large Language Models (LLMs). Learn how to automate responses, optimize workflows, and provide superior service with AI-driven solutions.

Enhance customer support with AI-powered voice services. Learn how to create intelligent voice systems that can understand and respond to customer queries efficiently and effectively.

Strengthen your skills in algorithmics and graph theory, and gain experience in programming in Python along the way.

Can we characterize intelligent behavior? Are there theoretical foundations on which Artificial Intelligence can be grounded?