Agentic AI

Nikita NamjoshiDeepLearning.AI

In this course, you will explore the properties of word and sentence embeddings and learn how to use them to measure semantic similarity between texts. You will apply text embeddings for tasks such as classification, clustering, and outlier detection. Additionally, you will learn to modify the text generation behavior of an LLM by adjusting parameters like temperature, top-k, and top-p. The course also covers the application of the open-source ScaNN library for efficient semantic search and building a Q&A system by combining semantic search with an LLM.

Manish GuptaMaven

In this course, you will learn to curate an AI toolkit designed for your goals, craft effective prompts for AI models, create customized models for unique needs, analyze financial statements and big data, manage your finance team effectively, and create strategic plans through market research and analysis. The course includes interactive live sessions, lifetime access to course materials, 15 in-depth lessons, direct access to the instructor, 7 projects to apply learnings, guided feedback, and a private community of peers.

Michele CatastaDeepLearning.AI

In this course, you'll explore the growing practice of 'vibe coding' where AI coding agents assist in the development process. You'll learn to structure your work, refine prompts, and use frameworks for cleaner, more efficient code. By building two web applications, you'll gain hands-on experience with Replit's tools and improve your development skills.

Miguel AmigotedX

In this course, you will gain a deep understanding of ChatGPT's capabilities and limitations, including its use cases in data, coding, and tech. You will develop proficiency in using ChatGPT for generating code programmatically, from ideation to testing and debugging. Explore the applications of ChatGPT in architecture and design, code rewriting, and other areas of programming. Learn how to integrate ChatGPT into your programming workflow and leverage its human-like conversation capabilities to enhance productivity.

Sandi BesenDeepLearning.AI

In this course, you will learn to build ACP-compliant agents by wrapping them in an ACP server, launching the server to activate the agents, and making them discoverable by ACP clients. You will explore how to chain ACP-compliant agents in linear and hierarchical workflows and use a router agent to delegate tasks. The course will guide you through importing ACP-compliant agents into a registry for easy discovery and sharing across teams. By the end, you'll be equipped to create agents that can communicate regardless of their frameworks, enhancing collaboration and integration within multi-agent systems.

Dr. Marily NikaMaven

In this comprehensive workshop, you will learn how to approach AI Product Management interviews with confidence. Dr. Marily Nika will guide you through three main interview themes: Product Sense, Execution/Analytical, and Strategy Insight. You will get the opportunity to practice live in a safe space, gain access to valuable resources, and receive direct feedback from the instructor. This workshop is designed to help you succeed in AI PM interviews, whether you are an aspiring AI PM, a recent graduate, an experienced PM or ML Engineer, or a team lead.

Fábio DuarteGetSmarter

In this course, you will gain a broad perspective on the future of real estate, including disruptions to commercial and residential properties, parking lots, curbsides, warehousing, and last mile delivery. You will understand how AI and self-driving vehicles are transforming the real estate industry and learn to leverage trends in micro-mobility, on-demand mobility, and self-driving vehicles for real estate investment opportunities. The course includes exposure to real city case studies and industry-relevant tools, equipping you with practical skills to navigate the evolving mobility landscape.

Doug TurnbullMaven

In this course, you'll learn how to enhance your search engine's relevance using Large Language Models (LLMs). The course covers query and document understanding, deploying LLMs to production, and making unstructured search structured. You'll gain insights into modern search techniques, helping your team achieve better outcomes faster.

Andreas KolleggerDeepLearning.AI

In this course, you will learn the basics of how knowledge graphs store data using nodes and edges, use Neo4j’s query language Cypher to retrieve information, add a vector index to a knowledge graph, and build a knowledge graph of text documents from scratch. You will also explore advanced techniques for connecting multiple knowledge graphs and using complex queries for comprehensive data retrieval. By the end of the course, you will be well-equipped to use knowledge graphs to uncover deeper insights in your data and enhance the performance of LLMs with structured, relevant context.

Dr. Marily NikaMaven

In this comprehensive bootcamp, you'll learn from top industry experts, including Google's Marily Nika, to master AI Product Management. The course covers technical and strategic aspects, offering live sessions, practical exercises, and a capstone project. You'll gain skills in AI development, metrics, strategy, and more, preparing you to lead AI initiatives in your organization.

Nikita NamjoshiDeepLearning.AI

In this course, you will learn to retrieve real-time data on global energy mixes and carbon intensity from the ElectricityMaps API. You'll identify power grids that produce electricity from low-carbon sources and run machine learning training jobs using low-carbon electricity. The course will also cover analyzing the carbon footprint of Google Cloud usage data and using the Google Cloud Carbon Footprint tool. By the end, you'll be equipped to make more sustainable decisions in your development processes.

Josh StarmerDeepLearning.AI

Dive deep into the attention mechanism that revolutionized AI with transformers. This course, taught by Josh Starmer, covers the evolution of attention, the role of Query, Key, and Value matrices, and the differences between self-attention and masked self-attention. Gain hands-on experience coding these concepts in PyTorch, and understand how they contribute to building scalable AI applications.

Sophia YangDeepLearning.AI

In this course, you’ll access Mistral AI’s collection of open source and commercial models, including the Mixtral 8x7B model, and the latest Mixtral 8x22B. You’ll learn about selecting the right model for your use case, and get hands-on with features like effective prompting techniques, function calling, JSON mode, and Retrieval Augmented Generation (RAG). By the end of this course, you’ll be equipped to leverage Mistral AI’s leading open source and commercial models.

Younes Bensouda MourriDeepLearning.AI

In the Natural Language Processing Specialization, you will learn to use logistic regression, naïve Bayes, and word vectors for sentiment analysis, analogies, and translations. You will also explore dynamic programming, hidden Markov models, and word embeddings for autocorrect, autocomplete, and part-of-speech tagging. Advanced topics include dense and recurrent neural networks, LSTMs, GRUs, and Siamese networks for sentiment analysis, text generation, named entity recognition, and question identification. Finally, you will delve into encoder-decoder, causal, and self-attention models for machine translation, text summarization, question-answering, and chatbot creation.

Luis SerranoDeepLearning.AI

In this specialization, you will gain a deep understanding of the mathematical principles that underpin machine learning algorithms. You will learn how to apply statistical techniques to enhance your data analysis, and acquire skills that are highly sought after by employers. By the end of the course, you will be able to implement and optimize machine learning algorithms, making you a valuable asset in the field of AI and data science.

Sebastian WitalecDeepLearning.AI

This course provides a comprehensive understanding of building multimodal search and RAG systems, focusing on the integration of various data types like text, images, and video into AI models. You will learn about contrastive learning for training multimodal models, visual instruction tuning, and the implementation of multi-vector recommender systems that enhance the capabilities of AI applications in industry settings.