This comprehensive course provides the knowledge and skills to deploy deep learning tools using AI/ML frameworks effectively. You will explore the fundamental concepts and practical applications of deep learning while gaining a clear understanding of the distinctions between deep learning and machine learning. The course covers a wide range of topics, including neural networks, forward and backward propagation, TensorFlow 2, Keras, performance optimization techniques, model interpretability, Convolutional Neural Networks (CNNs), transfer learning, object detection, Recurrent Neural Networks (RNNs), autoencoders, and creating neural networks in PyTorch. By the end of the course, you will have a solid foundation in deep learning principles and the ability to build and optimize deep learning models effectively using Keras and TensorFlow.

The Natural Language Processing (NLP) course provides an in-depth exploration of how machine learning algorithms are used to analyze and process vast amounts of natural language data. As NLP continues to drive advancements in AI, this course equips you with the essential skills to pursue a career as an NLP Engineer. Throughout the course, you will delve into key concepts such as statistical machine translation, neural models, deep semantic similarity models (DSSM), neural knowledge base embedding, and deep reinforcement learning techniques. Additionally, you will explore the application of neural models in image captioning and visual question answering, leveraging Python’s Natural Language Toolkit (NLTK).

The AIOps Foundation® is a two-day training course designed to cover the roots of AIOps. Learners will gain insight into processes that combine big data analytics, machine learning algorithms, automation and optimization into a single platform. The AIOps Foundation course will equip learners to successfully complete the AIOps Foundation certification exam.

This is a comprehensive two-day training designed to provide participants with a solid understanding of AI principles and practices. The course prepares individuals for the EXIN BCS Artificial Intelligence Foundation certification exam.

This Machine Learning with Python course provides an in-depth overview of ML topics, including working with real-time data, developing supervised and unsupervised learning algorithms, regression, classification and time series modelling. In this machine learning certification training course, you will learn how to use Python to make predictions based on data. Upon completion of this Machine Learning using Python course, you will receive a certificate attesting to your skills as a machine learning expert.

Ride this wave and accelerate your career with this acclaimed AI and Machine Learning program. This program features a thorough mix of theory, projects, and extensive hands-on practice while leveraging Purdue's academic excellence. More importantly, you’ll learn about not only the theoretical underpinnings of AI and ML but also gain the practical know-how to quickly and successfully apply these tools and techniques to new problems. This program is a blend of self-paced online videos, live virtual classes, hands-on projects, labs, and masterclasses. Mentorship sessions will provide you with a high-engagement learning experience and real-world applications, helping you master essential AI and ML skills. This program covers essential mathematical and statistical concepts, as well as Python programming, machine learning, deep learning, generative AI, prompt engineering, explainable AI, ChatGPT, computer vision, natural language processing, and more.