Courses - Vertex AI Academy

Machine Learning (Information Course – Non-Certification Course)

VAIA — Sun, 21 Jul 2024 05:17:19 +0000

This Machine Learning course is designed to provide a comprehensive understanding of the development and application of algorithms that enable computers to perform tasks without explicit instructions. Students will learn about various types of machine learning, including supervised, unsupervised, and reinforcement learning, and delve into advanced topics such as neural networks, deep learning, and ensemble methods. The course covers practical applications in natural language processing and computer vision, ensuring that students can apply their knowledge to real-world scenarios. Additionally, the course addresses ethical considerations in machine learning, emphasizing the importance of fairness, transparency, and privacy. By the end of the course, students will be equipped with the skills and knowledge to develop, evaluate, and deploy machine learning models effectively, and will be prepared for future advancements in the field.

(Information Course – Non-Certification Course)

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Data Engineering (Information Course – Non-Certification Course)

VAIA — Sun, 21 Jul 2024 04:35:41 +0000

This comprehensive Data Engineering course is designed to equip students with the knowledge and skills necessary to navigate the complexities of modern data environments. Covering a broad range of topics, from foundational concepts like data modeling and SQL to advanced subjects such as big data technologies and machine learning integration, the course offers a thorough exploration of data engineering. Students will gain insights into the entire data lifecycle, including data ingestion, processing, storage, analysis, and visualization, ensuring they are well-prepared to handle real-world data challenges.

One of the course’s standout features is its emphasis on industry relevance. By incorporating case studies from finance, healthcare, and e-commerce, students can see firsthand how data engineering principles are applied in various sectors. This practical approach is complemented by modules on the latest tools and platforms, such as Apache Spark, Hadoop, AWS, Azure, and Google Cloud, which are essential for modern data engineering tasks. Additionally, the course underscores the importance of data governance, privacy, and security, providing best practices and compliance strategies for regulations like GDPR and HIPAA. With detailed sections on building data pipelines and operationalizing machine learning models, students will be adept at integrating AI and ML into their data engineering processes, making them valuable assets in any data-driven organization.

(Information Course – Non-Certification Course)

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Advanced Artificial Intelligence

VAIA — Fri, 12 Jul 2024 22:59:50 +0000

In the final month, students will dive into advanced topics and applications of AI, pushing the boundaries of their knowledge and skills. They will explore transfer learning, a powerful technique that leverages pre-trained models to accelerate the development of new models. This will include practical applications across various domains, demonstrating the versatility of transfer learning. The month will also cover the integration of AI into web and mobile applications, providing insights into frameworks and tools for deploying AI models in real-world scenarios.

Students will further explore the realm of chatbots and conversational AI, learning how to build and deploy a simple chatbot. Real-world challenges in AI will be discussed, including ethical considerations, biases, and strategies for overcoming common obstacles. Practical sessions on troubleshooting and debugging will equip students with the skills to improve model performance and resolve issues effectively. The course will conclude with a presentation skills workshop, preparing students for their final project presentations. These presentations will showcase their projects, highlight their learning journey, and celebrate their achievements, marking the culmination of their comprehensive AI training program.

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Intermediate Artificial Intelligence

VAIA — Fri, 12 Jul 2024 22:58:20 +0000

The second month focuses on building upon the foundational knowledge acquired in the first month, diving deeper into the tools and techniques essential for developing sophisticated AI models. Students will be introduced to TensorFlow and PyTorch, two of the most widely used frameworks in the AI community. They will learn the key features of each framework and how to perform basic operations and model building. This month emphasizes the importance of proper model training, covering the steps involved, the significance of splitting data, and the algorithms used for training models effectively.

Students will also explore critical concepts such as regularization and overfitting, understanding how to prevent models from becoming too complex and failing to generalize well to new data. The curriculum includes an in-depth look at model evaluation metrics, providing the tools to assess model performance accurately. Towards the end of the month, students will begin working on their final projects, collaborating in groups to apply their learning to real-world problems. This hands-on experience will be crucial in consolidating their knowledge and preparing them for more advanced topics in the final month.

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Beginner Artificial Intelligence

VAIA — Fri, 12 Jul 2024 22:56:19 +0000

In the first month, students will embark on their journey into the world of Artificial Intelligence (AI) by laying a strong foundation. They will start with an introduction to the history and evolution of AI, gaining insights into the milestones and key figures that have shaped the field. This will set the context for understanding the current landscape and future trends in AI. Students will then delve into the basics of data, learning about data collection methods and preprocessing techniques essential for preparing data for machine learning models. The month will also cover fundamental concepts of machine learning, including the differences between supervised and unsupervised learning, illustrated with real-world examples.

As the month progresses, students will be introduced to neural networks, exploring their architecture and the role of activation functions. They will also learn the basics of Natural Language Processing (NLP), understanding its core components and applications. The hands-on sessions will provide practical experience, starting with setting up a Python environment and building a simple AI model. By the end of the month, students will have a solid understanding of basic AI concepts and practical skills, ready to tackle more advanced topics in the following months.

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