Table of Contents
Andrew Ng, founder of Deep Learning.AI and co-founder of Coursera, is a leading expert in machine learning and deep learning. His AI courses are highly respected for their structured approach and up-to-date content on the latest advancements in the field.
Ng’s courses feature practical assignments and projects, providing hands-on experience in applying deep learning algorithms and models. They are regularly updated to reflect the newest developments in deep learning.
Here are the latest courses by Andrew Ng that will help you gain knowledge and develop skills in AI.
1. AI Agents in LangGraph
In this short course, you’ll discover how to improve an agent’s knowledge by integrating agentic search, which provides query-focused answers in predictable formats. You’ll also learn about using agentic memory to save state for reasoning and debugging and how human-in-the-loop input can guide agents.
You’ll have the opportunity to build an agent from scratch and then enhance it with LangGraph to understand the framework better. Finally, you’ll create a sophisticated essay-writing agent that incorporates all the lessons learned in the course.
Enroll now for more details on the course.
2. AI Agentic Design Patterns with AutoGen
In this course, you’ll learn how to use AutoGen to apply agentic design patterns like multi-agent collaboration, sequential and nested chat, reflection, tool use, and planning.
You’ll also discover how to create and integrate specialized agents—such as researchers, planners, coders, writers, and critics—that work together to perform complex tasks, like generating detailed financial reports, which would otherwise require a lot of manual work.
The course includes important agentic design principles along with fun demonstrations. For example, you can build a conversational chess game with two player agents that validate moves, update the board state, and engage in lively banter about the game.
Find out more about the course and enroll here.
3. Introduction to On-device AI
This course teaches you how to deploy a real-time image segmentation model on a device. You’ll discover the essential steps for on-device deployment, including capturing the neural network graph, compiling it on the device, enabling hardware acceleration, and ensuring numerical correctness.
You’ll also explore how quantization can make the model four times faster and four times smaller, improving performance on devices with limited resources. These techniques allow you to deploy models on various devices, such as smartphones, drones, and robots, opening up new and creative applications.
Find out more about the course details here.
4. Multi-AI Agent Systems with Crew AI
In this course, you’ll learn how to divide complex tasks into smaller parts for multiple AI agents, each with its specific role.
For instance, creating a research report might involve researchers, writers, and quality assurance agents working together. Like managing a team, you can define their roles, expectations, and interactions.
You’ll also explore important AI techniques such as role-playing, tool use, memory, guardrails, and agent collaboration. Plus, you’ll build multi-agent systems to handle challenging tasks, making designing and observing these agents collaborating both productive and enjoyable.
Enroll now to learn more about the course details.
5. Building Multimodal Search and RAG
In this course, you’ll learn about contrastive learning and how to enhance RAG with multimodality. This allows models to utilize various relevant contexts to respond to questions.
For example, if you inquire about a financial report, the answer might include text excerpts, graphs, tables, and slides. You’ll also discover visual instruction tuning and incorporating image comprehension into language models. Additionally, you’ll build a multi-vector recommender system using Weaviate’s open-source vector database.
Find out more about the course details here.
6. Building Agentic RAG with LlamaIndex
This course explores a significant change in RAG methodology. Instead of developers having to write specific routines to fetch information for the LLM context, an RAG agent can now access various tools.
You’ll delve into routing, where the agent makes decisions to send requests to multiple tools; tool use, where agents can create an interface to choose the right tool (function call) and generate the correct arguments; and multi-step reasoning with tool use.
Discover more about the course details here.
7. Quantisation In Depth
This course teaches you to implement various linear quantization techniques from scratch, including asymmetric and symmetric modes. To maintain performance, you’ll also explore quantization at different levels (per tensor, per channel, per group).
You can create a quantizer to compress the dense layers of any open-source deep-learning model to 8-bit precision. Additionally, you’ll practice quantizing weights into 2 bits by combining four 2-bit weights into a single 8-bit integer.
Find out more details about the course here.
8. In Prompt Engineering for Vision Models
In this course, you’ll discover how to prompt and refine vision models for personalized image generation, editing, object detection, and segmentation. Depending on the model, prompts can be text, coordinates, or bounding boxes. Additionally, you’ll learn to adjust hyperparameters to shape the output.
You’ll work with models like the Segment-Anything Model (SAM), OWL-ViT, and Stable Diffusion. Moreover, you’ll fine-tune Stable Diffusion using a few images to create personalized results, such as images of a specific person.
To learn more and enroll in the course, click here.
9. Getting Started with Mistral
In this course, you’ll explore Mistral’s open-source models (Mistral 7B, Mixtral 8x7B) and commercial models by accessing them through API calls and Mistral AI’s Le Chat website.
You’ll learn how to implement JSON mode to generate structured outputs that can be easily integrated into larger software systems. Additionally, you’ll use function calling to utilize tools like custom Python code to query tabular data.
Using RAG, you’ll ground the responses of Language Learning Models (LLMs) with external knowledge sources. You’ll also build a Mistral-powered chat interface capable of referencing external documents. This course will help enhance your prompt engineering skills.
For more details and to enroll in the course, click here.
10. Preprocessing Unstructured Data for LLM
In this course, you’ll learn to enhance your knowledge of Language Learning Models (LLMs) by extracting and organizing content from formats like PDF, PowerPoint, and HTML. This involves adding extra information, known as metadata, to the data for better searching and reasoning.
You’ll discover how to prepare data for LLM applications by focusing on different types of documents. Additionally, you’ll learn methods for extracting and organizing documents into a common JSON format with enriched metadata to improve search results.
The course also covers techniques for analyzing document images, including layout detection and vision transformers, to handle PDFs, images, and tables. Furthermore, you’ll learn to create a RAG bot capable of processing diverse documents like PDFs, PowerPoints, and Markdown files.
For more information and enrollment in the course, click here.
Conclusion
As we conclude this list of must-take AI courses from Andrew Ng, reflecting on the wealth of knowledge and skills you’ve gained through these offerings is important. From foundational concepts to cutting-edge applications, each course has equipped you with invaluable insights into the world of artificial intelligence. Whether you’re a novice looking to explore the field or a seasoned professional seeking to expand your expertise, Andrew Ng’s courses provide a comprehensive and accessible learning experience.
By delving into machine learning, deep learning, natural language processing, and computer vision, you’ve acquired the tools to tackle real-world challenges and drive innovation in various industries. Moreover, the practical exercises and hands-on projects offered in these courses have enabled you to apply theoretical knowledge to solve practical problems, thereby honing your skills and building a robust portfolio.
Furthermore, Andrew Ng’s commitment to making AI education accessible to all learners shines through in the clarity of instruction, engaging lectures, and supportive online community fostered within each course. Whether you prefer self-paced learning or structured study, the flexibility of these courses ensures that you can tailor your educational journey to suit your individual needs and goals.
As you embark on your AI learning journey, remember that continuous growth and adaptation are essential in this rapidly evolving field. By staying curious, remaining open to new ideas, and actively engaging with the AI community, you’ll keep pace with the latest developments and contribute to the advancement of AI technology and its ethical implementation.
In conclusion, the 10 AI courses from Andrew Ng listed here serve as a solid foundation for anyone interested in mastering the principles and applications of artificial intelligence. By investing your time and effort in these courses, you’re investing in your future and the future of AI innovation and its positive impact on society. So, seize this opportunity to learn, grow, and make a difference in the exciting world of AI.
FAQs
Q1: What are the 10 AI courses from Andrew Ng that you recommend?
Ans: The 10 recommended AI courses are Machine Learning, Deep Learning Specialization, AI For Everyone, Natural Language Processing Specialization, Structuring Machine Learning Projects, Convolutional Neural Networks, Sequence Models, AI For Medicine, AI For Business, and Reinforcement Learning Specialization.
Q2: Why should I take these AI courses from Andrew Ng?
Ans: These courses offer comprehensive coverage of essential AI concepts and applications taught by Andrew Ng, one of the leading experts in the field. They provide a solid foundation for beginners and advanced practitioners, with practical exercises and real-world examples to reinforce learning.
Q3: Are these courses suitable for beginners?
Ans: Yes, many of these courses are designed to be beginner-friendly, assuming no prior knowledge of AI. However, some familiarity with programming and basic mathematics may benefit certain courses.
Q4: What programming languages are used in these courses?
Ans: Python is the primary programming language used in these courses. It is widely used in AI and machine learning. Familiarity with Python or a willingness to learn it is recommended.
Q5: Are there any prerequisites for these courses?
Ans: While most courses do not have strict prerequisites, a basic understanding of mathematics (linear algebra, calculus, probability) and programming concepts (particularly in Python) will help grasp the material more effectively.
Q6: Are certificates offered upon completion of these courses?
Ans: Yes, certificates of completion are provided for most courses upon successfully finishing all required assignments and assessments. These certificates can be a valuable addition to your resume or LinkedIn profile.
Q7: How much time should I dedicate to each course?
Ans: The time commitment varies depending on the course complexity and your prior knowledge. Generally, you can expect to spend a few hours per week on lectures and assignments, but this can vary based on individual learning pace and goals.
Q8: Are any discounts or financial aid options available for these courses?
Ans: Coursera, where most of these courses are hosted, often offers financial aid for learners who cannot afford the full course fee. Additionally, discounts or promotions may be available occasionally, so it’s worth checking the platform for any current offers.
Q9: Can I audit these courses for free?
Ans: Yes, many of these courses allow free auditing, which grants access to course materials but excludes graded assignments and certificates. Auditing can be a great way to explore the content before committing to the full course.
Q10: How can I get started with these courses?
Ans: You can enroll in these courses directly through online learning platforms like Coursera, where they are hosted. Simply search for the course titles and follow the enrollment instructions to begin your AI learning journey with Andrew Ng.
