Popular Online AI/ML Courses November 2024 Edition

Popular Online AI/ML Courses November 2024 Edition

Generative AI is not just a tool; it’s a skill.

— Jensen Huang, CEO of NVIDIA

With the widespread adoption of AI in everyday life, Machine Learning (ML) and Artificial Intelligence (AI) have become two of the most important 21st-century skills today. While some may fear the loss of jobs due to AI, others are excited about the numerous new career options AI has created. For example, AI engineers, prompt engineers, AI consultants, conversational AI specialists, AI product UX designers, AI tutors (AI training specialists), machine learning scientists, AI ethicists, AI researchers, and the list goes on and on. There is no doubt that gaining knowledge in AI and ML is the best investment you can make today.

At first blush, terms like “AI” and “Machine Learning” can sound intimidating to people without a technical background. But among the jobs mentioned earlier, not all require a college degree in AI/ML. However, gaining a foundational understanding will certainly give you a competitive advantage. No time (or energy) to go back to school? No worries! In this post, we’ll share five excellent online AI/ML courses you can consider without leaving the comfort of your home.

1. Elements of AI

https://course.elementsofai.com

Cost: Content is FREE. Certificate available upon completion.

Developed by the University of Helsinki and MinnaLearn, Elements of AI contains up-to-date learning content about AI/ML. It has two tracks: (1) Introduction to AI and (2) Building AI. If you prefer bite-size, text-based learning content supplemented by cute and appealing graphics, then Elements of AI is right for you.

Content in the Introduction to AI series ranges from beginner-friendly topics such as “How do we define AI” to more advanced topics like “Neural networks.” Each module has 3 short lessons followed by 1-2 exercises to test your knowledge. A certificate is available upon completion.

Content in the Building AI series dives into the backend of AI especially about mathematical concepts such as The Bayes Rule, linear regression, logistic regression, and neural networks. You can purchase a certificate for 50 € (~$53 USD) upon completion.

As of November 30 2024, there are no hands-on projects in Elements of AI.

2. Google AI Essentials

https://grow.google/certificates/en_ca/ai-essentials

Cost: $49 USD. Certificate available upon completion.

With a focus on generative AI, Google AI Essentials through Coursera includes five self-paced modules that are taught by Google AI experts for non-technical audiences who are new to AI.

Module 1. Introduction to AI – Understand foundational concepts related to AI (e.g., machine learning)
Module 2. Maximize Productivity with AI tool – Boost productivity and accelerate workflows with generative AI tools
Module 3. Discover the Art of Prompt Engineering – Discover effective prompting techniques
Module 4. Use AI Responsibility – Mitigate biases and inaccuracies to reduce AI harms
Module 5. Stay Ahead of the AI Curve – Continue to develop knowledge and skills in the AI landscape

Each module contains a mix of videos, readings, and no-code assignments. The entire course can be completed within 15 hours. If you prefer learning by watching videos, reading texts, and doing practical assignments (e.g., “Use AI to inform work-related decisions”), then Google AI Essentials is for you.

3. Introduction to Generative AI by Google Cloud

https://www.cloudskillsboost.google/paths/118

Cost: FREE. Course badges available upon completion.

While Google AI Essentials (see above) is designed for non-technical audiences, Introduction to Generative AI by Google Cloud contains learning content that dives deeper into the nuances of generative AI. Although the course is designed for beginners—and no coding is involved—the content is geared toward the effective utilization of Google products, such as Gemini (a multimodal AI model) and Vertex AI (a platform for building AI chatbots, digital assistants, custom search engines, knowledge bases, training applications, and more). There are five modules:

Module 1. Introduction to Generative AI – Learn about Generative AI and how it differs from other ML methods
Module 2. Introduction to Large Language Models – Learn about LLMs and how to improve their performance via prompting
Module 3. Introduction to Responsible AI – Learn about what is means to use AI responsibly and how Google applies the concept to their products
Module 4. Prompt Design in Vertex AI – Learn how to do prompt engineering, image analysis, and apply multimodal generative AI techniques using Vertex AI
Module 5. Responsible AI: Applying AI Principles with Google Cloud – Learn about the importance of building AI responsibly using Google’s AI principles

Technically speaking, Introduction to Generative AI by Google Cloud is one of many “learning paths” Google has designed for the general public to boost their skills and grow their career. If you prefer learning by watching short but high-quality videos, self-learning by looking up the references the instructors provided, and completing quizzes, then Introduction to Generative AI by Google Cloud is for you.

4. DeepLearning.AI

https://www.deeplearning.ai

Cost: FREE on Deep Learning AI. Certificate available upon completing Deep Learning AI courses via Coursera (with a fee).

Founded by Stanford Adjunct Professor Andrew Ng in 2017, Deep Learning AI‘s mission is “To grow and connect the global AI community.” As a renowned pioneer in ML, Ng has a passion for accessible AI education, and Deep Learning AI, his brainchild, is a reflection of that. As of 2023, an estimated number of 8 million learners around the world have taken his courses through Deep Learning AI, Coursera, or other learning platforms.

Compared to the aforementioned courses, Deep Learning AI is designed for folks who want to get their hands dirty by writing codes and building AI projects. Deep Learning AI course content can be accessed in two ways: (1) Deep Learning AI’s platform and (2) Coursera.

Deep Learning AI’s platform offers a plethora of AI courses not only taught by their own instructors but also in collaboration with industry experts, such as AWS, Google Cloud, crewAI, LlamaIndex, MongoDB, Microsoft, Meta AI, and more. These courses come in short courses, regular courses, and specialization, ranging from beginner to intermediate level. Courses are taught mostly through videos and hands-on coding examples. As of November 2024, there are no certificates upon completion, but since Deep Learning AI’s learning platform is only in beta, this is likely to change in the near future.

Deep Learning AI via Coursera provides specialized topics such as “Introduction to Data Engineering,” “Deep Learning Specialization,” “Machine Learning Specialization,” “Advanced Learning Algorithms,” “Convolutional Neural Networks,” and more. Typically certificates are available upon completion, issued by Coursera in collaboration with Deep Learning AI. These courses are mostly delivered through videos and coding projects or assignments. One of the biggest perks of Coursera courses is that some of the courses can be used to build toward a degree (degree credit eligible), which can be transferred to other institutions if you later decide to pursue a full college or graduate degree. Note, however, that the credit transfer decision ultimately depends on each institution.

Overall, Deep Learning AI’s courses are perfect for more technical audiences or people who would like to acquire essential AI/ML skills.

5. NVIDIA Deep Learning Institute (DLI)

https://learn.nvidia.com

Cost: FREE for some self-paced courses. Paid self-paced courses range from $30-$90/course. NVIDIA offers certifications separately with a fee ($135) + an exam. The certificates need to be renewed after 2-3 years.

Diving even deeper into the technical side, NVIDIA’s Deep Learning Institute (DLI) is certainly designed for individuals who have some prior knowledge of AI/ML. The self-paced courses cover highly specialized topics in the areas of accelerated computing, data science, deep learning, generative AI/LLM, and more. Courses are delivered through a combination of videos, assessments, and hands-on coding projects where you can run Python directly on the learning platform.

Similar to how Google integrates tons of Google AI products into their courses, NVIDIA also offers specialized courses with a focus on NVIDIA solutions, such as CUDA (Compute Unified Device Architecture), OpenUSD (Universal Scene Description for building 3D content), and NIM (NVIDIA Inference Microservices).

In short, NVIDIA’s DLI contains in-depth and highly technical content for developers or individuals who would like to acquire the necessary technical skills for a career boost. The content is deep and not as bite-size as the aforementioned courses, but if you prefer learning content that challenges you, then NVIDIA’s DLI is right for you.

Summary

Overall, if you are new to AI, try starting with Google AI Essentials to get a very general understanding of AI. The high-quality videos with supporting graphics will keep you entertained. After that, you can move on to Introduction to Generative AI by Google Cloud, where you will gain a deeper understanding of AI especially in the context of Google AI products.

If you already have some general knowledge of AI and would like to explore the technical side of things, try starting with Elements of AI to gain foundational understanding of the important concepts related to AI/ML, especially relating to mathematics. The bite-size content with cute graphics will make you want to keep learning. After that, you can explore any courses by Deep Learning AI and gain some hands-on coding experiences. Finally, if you are already far in your AI journey, then try out the self-paced courses by NVIDIA’s DLI.

Note: This post is entirely written by a human 🙂 and not sponsored by any organizations or persons mentioned.

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