Andrew Ng, founder and chief of DeepLearning.AI, is leading the AI education space with his meticulously curated courses. In addition to offering the introductory ones, he provides a range of specialised courses, all of which are available for free.

These specialised courses and programs by DeepLearning.AI, curated by a team of AI experts and led by Andrew Ng, provide valuable opportunities for learners to advance their knowledge and expertise in the rapidly evolving field of AI and deep learning. 

Let’s take a look at them.

AI For Everyone

Unlike the other courses by Ng, ‘AI for Everyone’ is a non-technical, introductory course designed to help both business professionals and technical individuals understand AI technologies and their applications. It covers the fundamentals of AI, machine learning, and data science, providing insights into what AI can and cannot do. Participants will learn about the workflow of AI and data science projects, how to choose AI projects and the impact of AI on society. The course aims to equip learners with the knowledge to build a sustainable AI strategy and navigate the challenges brought about by technological change. The course consists of four weeks of content, with a total duration of six hours.

Instructor: Andrew Ng

AI For Good

The ‘AI for Good’ course, in collaboration with Microsoft’s AI for Good lab, is a specialisation designed for an individual’s interested in using AI to address real-world challenges in humanitarian and environmental projects. Participants 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.

The course provides a step-by-step framework for utilising AI in real-world projects and includes hands-on case studies and labs using Python and Jupyter Notebooks. It is suitable for learners from all backgrounds and does not require prior experience in AI or coding. Upon completion, participants receive a certificate and gain valuable knowledge to contribute to AI for Good initiatives worldwide.

Instructor: Robert Monarch, ML leader at Apple

Machine Learning Specialisation 

This is a newly rebuilt and expanded program created by Andrew Ng for beginners seeking to break into the field of AI and machine learning. The specialisation consists of three courses, and provides foundational AI concepts through an intuitive visual approach, followed by hands-on coding and an introduction to the underlying math. The course is designed for beginners, requiring no prior math or coding background. It covers topics such as linear regression, logistic regression, neural networks, decision trees, recommender systems, and more. The updated curriculum uses Python instead of Octave, and the section on applying machine learning has been enhanced with best practices from the last decade. 

Instructor: Andrew Ng

Master the Mathematics Behind AI and Unlock Your Potential

The ‘Mathematics for Machine Learning and Data Science Specialisation’ is a beginner-friendly course that equips learners with a solid understanding of essential mathematical concepts used in machine learning. The course covers calculus, linear algebra, statistics, and probability, providing students with the tools to comprehend algorithms and optimise them for custom implementation.

By enrolling in this specialisation, participants will gain statistical techniques to enhance data analysis, acquire highly sought-after skills by employers for excelling in machine learning interviews and securing their dream jobs. The course features a team of instructors with expertise in the field, and it is designed for individuals with a high-school level of mathematics knowledge.

Instructor: Luis Serrano, founder, Serrano Academy

TensorFlow: Data and Deployment Specialisation

The ‘TensorFlow: Data and Deployment Specialisation’ is a four-month intermediate-level program, with a recommended commitment of three hours per week. The specialisation aims to teach participants how to deploy machine learning models for various devices and platforms using TensorFlow. It covers topics such as running models in web browsers using TensorFlow.js, deploying models on mobile devices using TensorFlow Lite, data pipelines with TensorFlow Data Services, and advanced deployment scenarios with TensorFlow Serving. The courses include practical exercises and projects, and participants will learn to handle data, work with APIs, and use pre-trained models effectively. 

Instructor: Laurence Moroney, lead AI advocate at Google

Generative Adversarial Networks (GANs) Specialisation

The GANs Specialisation is a three course intermediate-level program that focuses on image generation using GANs. Students will learn to create basic GANs using PyTorch, advanced DCGANs with convolutional layers, and conditional GANs. The courses cover comparing generative models, using the FID method to assess GAN fidelity and diversity, detecting bias in GANs, and implementing StyleGAN techniques.

Additionally, participants will explore GANs applications for data augmentation and privacy preservation, as well as building Pix2Pix and CycleGAN for image translation. The program also addresses social implications of GANs, such as bias in machine learning and methods to detect it. Throughout the courses, learners will develop skills in areas like generator design, image-to-image translation, and understanding computer graphics terminology, among others. The specialisation aims to provide a comprehensive understanding of GANs and offers practical hands-on experience.

Instructor: Sharon Zhou, CEO, co-founder, Lamini

AI for Medicine

The course focuses on practical applications of ML in the field of medicine. Participants will learn to estimate treatment effects using data from randomized control trials, interpret diagnostic and prognostic models, and extract information from unstructured medical data using natural language processing. The skills acquired include model interpretation, image segmentation, natural language extraction, machine learning, time-to-event modeling, deep learning, model evaluation, multi-class classification, random forest, model tuning, and treatment effect estimation.

The course will also explore various AI-driven medical applications, such as diagnosing diseases from X-rays and 3D MRI brain images, predicting patient survival rates with tree-based models, and automating the labelling of medical datasets through natural language processing.

Instructor: Pranav Rajpurkar, assistant professor and director, Harvard-Stanford Medical AI Bootcamp 

Generative AI with Large Language Models (LLMs)

This course, developed in collaboration with AWS, focuses on teaching the foundational principles and practical applications of generative AI in real-world scenarios. It covers the entire lifecycle of LLM-based generative AI, starting from data gathering and model selection to deployment and performance evaluation. Participants will gain a functional understanding of LLMs and the transformer architecture that powers them, along with the ability to fine-tune models for specific use cases. The course also explores cutting-edge research in generative AI and offers hands-on training, tuning, and deployment methods to optimise model performance. 

Instructor: Antje Barth and Mike Chambers, developer advocates, Gen AI, AWS. Chris Fregly and  Shelbee Eigenbrode, principal solutions architect, Gen AI, AWS

MLOps Specialisation

The MLOps Specialisation is an advanced 4-month course that equips students with production-ready ML skills. It covers tools, techniques, and experiences to build and maintain integrated systems operating continuously in production, handling evolving data efficiently. The four courses include topics like ML production system design, concept drift, data pipelines, feature engineering with TensorFlow Extended, and model resource management. 

Instructors: Andrew Ng, Robert Crowe, TensorFlow developer engineer, Google; Laurence Moroney, lead AI advocate, Google

Practical Data Science on the AWS Cloud (PDS) Specialisation

This is also another advanced course that equips data-focused developers, scientists, and analysts with the skills needed to deploy scalable ML pipelines using Amazon SageMaker in the AWS cloud. The specialisation covers various topics, including data preparation, feature engineering, automated machine learning (AutoML), model training and evaluation, ML pipelines, artefact and lineage tracking, and human-in-the-loop pipelines.

Participants will gain hands-on experience with algorithms like BERT and FastText for natural language processing (NLP) using Amazon SageMaker. By the end of the program, learners will be able to build and deploy end-to-end ML pipelines, optimise model performance, and reduce costs while improving data products.

Instructors: Antje Barth, developer advocate, Gen AI, AWS; Chris Fregly and  Shelbee Eigenbrode, principal solutions architect, Gen AI, AWS; Sireesha Muppala, principal solutions architect, AI and ML, AWS 

Read more: Top 7 Generative AI Courses by Andrew Ng