Advanced Certificate in Artificial Intelligence

The "Applied Machine Learning" module provides a comprehensive exploration of key machine learning concepts and practical skills crucial for real-world applications. Commencing with an introduction to machine learning, learners progress through instructional units (IUs) covering classification, regression, and tree and ensemble methods. The module extends to practical aspects, guiding participants on working with data and compute in Azure Machine Learning. Learners delve into training models with scripts and gain proficiency in managing and deploying models, essential skills for effective utilization of machine learning in practical scenarios.

Throughout this module, learners acquire knowledge in machine learning fundamentals, ranging from the foundational principles of classification and regression to advanced techniques like tree and ensemble methods. They gain a deep understanding of how machine learning models operate, allowing them to make informed decisions on model selection and optimization. Additionally, participants develop expertise in leveraging Azure Machine Learning for data processing, model training, and deployment. The practical skills gained in this module are diverse and directly applicable to real-world machine learning scenarios. Learners become adept at scripting to train models, a critical skill for customizing machine learning algorithms to suit specific tasks. Moreover, they master the intricacies of managing and deploying models, ensuring seamless integration of machine learning solutions into practical applications.

In the culminating project, learners undertake the task of training, evaluating, and deploying a machine learning model for prediction. This hands-on project allows participants to synthesize their acquired knowledge and skills into a real-world application. By successfully completing the project, participants demonstrate their ability to navigate the end-to-end machine learning process, from model development to deployment.

As a result of this module, learners will emerge with the capability to apply machine learning principles effectively in practical settings. They will have the knowledge to make informed decisions regarding model selection, optimization, and deployment. The hands-on experience gained through the project equips them with the skills needed to tackle real-world challenges, reinforcing their ability to contribute meaningfully to the dynamic field of applied machine learning.

The module "Applied Python Programming" equips learners with essential knowledge and skills in Python programming for computer vision applications. Through the instructional units covered in this module, learners will gain a comprehensive understanding of computer vision concepts and techniques, as well as the ability to apply them effectively using the OpenCV library.

Learners will start with an introduction to computer vision and OpenCV, learning the fundamentals of image processing and analysis. They will acquire knowledge in various image processing techniques, including noise reduction, image enhancement, and feature extraction. Learners will also explore advanced computer vision techniques such as image segmentation and object recognition, further expanding their capabilities in solving complex problems.

The module delves into object detection and tracking, teaching learners how to identify and track objects in images or video streams. They will learn about feature detection and matching, enabling them to identify distinctive features and match them across multiple images. Additionally, learners will gain expertise in image filtering and transformation, equipping them with the skills to manipulate and transform images effectively.

With image analysis with Azure, learners delve into cutting-edge techniques for processing and extracting insights from images using Microsoft Azure"s powerful tools. The instructional units cover image classification with Azure, empowering participants to categorize and label images efficiently. Additionally, the module explores the realm of Optical Character Recognition (OCR), providing learners with the skills to extract text information from images. 

Upon completing this module, learners will have the knowledge and skills necessary to leverage Python programming for computer vision applications. They will be proficient in using OpenCV and its various functionalities to process images, detect objects, match features, and implement advanced computer vision techniques. With the ability to integrate computer vision into real-world projects, learners will be well-prepared to address real-world challenges and contribute to the field of computer vision.

The "Deep Learning" module equips learners with essential knowledge and skills in the field of Artificial Intelligence (AI) and Deep Learning. Throughout this module, learners will gain a comprehensive understanding of AI and Deep Learning concepts, including Artificial Neural Networks, Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs). They will explore techniques for optimizing model performance and develop proficiency in implementing deep learning pipelines. Furthermore, learners will learn how to deploy Deep Learning models effectively in real-world applications.

By the end of this module, learners will be able to implement a Deep Learning model for image recognition using Convolutional Neural Networks (CNNs). They will acquire the necessary skills to design and develop Neural Networks that can effectively process and analyze images. Through hands-on projects, learners will gain practical experience in training CNNs, fine-tuning model performance, and deploying the trained models for real-world image recognition tasks. This module provides learners with the ability to apply Deep Learning techniques to solve complex image recognition problems, empowering them to contribute to advancements in the field of AI and Computer Vision.

Through a combination of theoretical learning, practical application, and project-based tasks, learners will develop a strong foundation in deep learning and acquire the skills necessary to embark on challenging AI projects. The "Deep Learning" module serves as a crucial stepping stone for learners to unlock their potential in the exciting and rapidly expanding field of AI.

In the "Generative AI" module, learners will learn about different AI models and how they are used in real life. We will start with the basics of these AI models, understanding how they work. We will also explore practical tools like ChatGPT, Microsoft Copilot 365, and Microsoft Copilot Studio. By using these tools, learners will discover how Generative AI can be applied in various ways, like creating marketing contents and performing data analysis.

In this module, learners will explore the practical applications of ChatGPT and Microsoft Copilot 365. They will uncover how ChatGPT is used in real life, such as in creating content, data analysis, boosting business productivity, and assisting with customer support. Through hands-on experiences, participants will learn how to use AI tools like ChatGPT and Microsoft Copilot 365 to craft engaging and interactive content. Furthermore, the module will equip learners with the skills to create functional Chatbots using Microsoft Copilot Studio with generative AI, facilitating smooth interactions with users.

By the end of this module, participants will have a solid grasp of AI models, as well as practical expertise in effectively utilizing ChatGPT, Microsoft Copilot 365 and Microsoft Copilot Studio for tasks like content creation, multimedia presentations, improving business productivity, and enhancing customer support.