Founder and Head of DeepLearning.AI, Andrew Ng leads the AI education field with thoughtfully crafted courses. In addition to introductory courses, we also offer a range of specialized courses, all of which are free of charge.
Curated by a team of AI experts and led by Andrew Ng, these specialized courses and programs by DeepLearning.AI offer learners a unique opportunity to advance their knowledge and expertise in the rapidly evolving field of AI and deep learning.
Let’s see them.
AI for everyone
Unlike Ng’s other courses, “AI for Everyone” is a non-technical introductory course designed to help both business professionals and technologists understand AI technology and its applications. It covers the fundamentals of AI, machine learning, and data science, and provides insight into what AI can and cannot do. Attendees will learn about the workflow of AI and data science projects, how to choose AI projects, and the impact of AI on society. This course aims to equip learners with the knowledge to build sustainable AI strategies and address the challenges posed by technological change. This course consists of his 4 weeks of content with a total duration of his 6 hours.
Lecturer: Andrew Ng
AI for Good
In collaboration with Microsoft’s AI for Good Lab, the AI for Good course is a specialization designed for individuals interested in using AI to address real-world challenges in humanitarian and environmental projects. Attendees will learn how to contribute to AI-powered initiatives that create positive change, such as mitigating climate change, supporting disaster response, and improving public health. This course provides a step-by-step framework for leveraging AI in real-world projects and includes hands-on case studies and labs using Python and Jupyter Notebooks. Suitable for learners of all backgrounds, no AI or coding experience required. This course is a collaboration with Microsoft’s AI for Good Lab and offers insights from experts working in the field. Upon completion, participants will receive a certificate and gain valuable knowledge to contribute to AI for Good initiatives around the world.
Lecturer: Robert Monarch, Apple ML Reader
Machine learning specialization
This is a newly restructured and enhanced program created by Andrew Ng for beginners looking to enter the AI and Machine Learning field. This specialization consists of his three courses, which provide foundational AI concepts through an intuitive visual approach, followed by hands-on coding and an introduction to the underlying mathematics. The course is designed to be approachable to complete beginners and does not require prior math knowledge or a rigorous coding background. Topics include linear regression, logistic regression, neural networks, decision trees, and recommender systems. The updated curriculum replaces his Octave with Python, and the section on applying machine learning is enhanced based on best practices from the last decade.
Lecturer: Andrew Ng
Master the math behind AI and unlock your potential
Mathematics for Machine Learning and Data Science is an introductory course that helps learners gain a solid understanding of the key mathematical concepts used in machine learning. This course covers calculus, linear algebra, statistics, and probability, providing students with the tools to understand algorithms and optimize them for custom implementations. By enrolling in this specialization, participants will master statistical techniques to enhance data analysis, perform well in machine learning interviews, and acquire highly sought-after skills by employers to secure their dream jobs. This course features a team of instructors with subject matter expertise and is designed for individuals with high school level mathematics knowledge.
Lecturer: Luis Serrano, Founder, Serrano Academy
TensorFlow: Data and Deployment Specialization
“TensorFlow: A Data and Deployment Specialization” is an intermediate-level program consisting of 4 courses over 4 months with a recommended commitment of 3 hours per week. This specialization is designed to teach participants how to use TensorFlow to deploy machine learning models on various devices and platforms. Topics include running models in a web browser with TensorFlow.js, deploying models to mobile devices with TensorFlow Lite, data pipelines with TensorFlow Data Services, and advanced deployment scenarios with TensorFlow Serving. Courses include hands-on exercises and projects that teach participants how to process data, work with APIs, and use pre-trained models effectively.
Lecturer: Lawrence Moloney, Principal AI Advocate at Google.
Specialty of Generative Adversarial Networks (GAN)
The GANs Specialization is a three-course intermediate program focused on image generation using GANs. Students will learn how to create basic GANs using PyTorch, advanced DCGANs using convolutional layers, and conditional GANs. This course covers comparing generative models, evaluating the fidelity and diversity of his GANs using the FID method, detecting biases in GANs, and implementing the StyleGAN technique. Additionally, participants will explore GAN applications for data augmentation and privacy protection, and build Pix2Pix and CycleGAN for image transformation. The program also covers the social implications of GANs, including biases in machine learning and how to detect them. Throughout the course, learners develop skills in areas such as generator design, image-to-image conversion, and understanding computer graphics terminology. This specialization aims to provide a comprehensive understanding of GANs and hands-on hands-on experience.
Lecturer: Lamini CEO and Co-Founder Sharon Zhou
AI for healthcare
This course focuses on practical applications of ML in the medical field. Participants will learn how to use data from randomized controlled trials to estimate treatment effects, interpret diagnostic and prognostic models, and use natural language processing to extract information from unstructured medical data. Skills acquired include model interpretation, image segmentation, natural language extraction, machine learning, time-to-event modeling, deep learning, model evaluation, multi-class classification, random forests, model tuning, treatment effect estimation, and more. The course also explores various AI-powered medical applications, such as diagnosing disease from X-rays and 3D MRI brain images, predicting patient survival with tree-based models, and automating the labeling of medical datasets with natural language processing.
Lecturer: Pranav Rajpurkar, Assistant Professor and Director of Medical AI Bootcamp at Stanford University, Harvard University
Generative AI using Large Language Models (LLM)
Developed in partnership with AWS, this course focuses on teaching the fundamental principles and practical application of generative AI in real-world scenarios. It covers the entire lifecycle of LLM-based generative AI, from data collection and model selection to deployment and performance evaluation. Participants gain an understanding of the capabilities of LLM and the transformer architecture that drives it, as well as the ability to fine-tune the model for specific use cases. The course also explores cutting-edge research in generative AI and provides hands-on training, tuning, and deployment methods for optimizing model performance.
Instructors: Antje Barth and Mike Chambers, Developer Advocates, Gen AI, AWS. Chris Fregly and his Shelbee Eigenbrode, AWS Gen AI Principal Solutions Architect.
MLOps Expertise
The MLOps Specialization is an advanced four-month course that provides students with production-ready ML skills. It covers the tools, techniques, and experience to build and maintain integrated systems that operate continuously in production environments and efficiently handle evolving data. The four courses cover topics such as ML production system design, concept drift, data pipelines, feature engineering with TensorFlow Extended, and model resource management.
Speakers: Andrew Ng, Robert Crowe, Google TensorFlow Developer Engineer, Google Lead AI Advocate, Laurence Moroney
Hands-On Data Science (PDS) Specialization on the AWS Cloud
This is also another advanced course that gives data-centric developers, scientists, and analysts the skills they need to deploy scalable ML pipelines using Amazon SageMaker on the AWS Cloud. This specialization covers a variety of topics including data preparation, feature engineering, automated machine learning (AutoML), model training and evaluation, ML pipelines, artifact and lineage tracking, human-involved pipelines, and more. Attendees will get hands-on experience with algorithms such as his BERT and FastText for Natural Language Processing (NLP) using Amazon SageMaker. By the end of the program, the learner will be able to build and deploy his end-to-end ML pipeline to optimize model performance and reduce costs while improving data products.
Lecturers: Antje Barth, Developer Advocate, Gen AI, AWS. Chris Fregly and his Shelbee Eigenbrode, AWS Gen AI Principal Solutions Architect. Sireesha Muppala, Principal Solutions Architect for AI and ML, AWS.
Read more: Top 7 Generative AI Courses by Andrew Ng
