I took AI for Everyone course on Coursera (Review)

Understanding AI is no longer a luxury, but a necessity.

AI For Everyone by Andrew Ng on Coursera is a great course to help start your journey.

Are you a business leader trying to incorporate AI strategies in your company? A student looking to strengthen your base knowledge? Or, just someone too curious for their own good?

Whatever the answer may be, let’s see whether this course can quench your thirst for AI knowledge.

Quick Summary: AI for Everyone Review

AI for Everyone	Rating
Content Quality	4.5/5
Engagement Level	4.7/5
Instructor Expertise	4.8/5
Practicality	4.1/5
Value for Money	4/5
Community & Support	4.6/5
Overall Rating	4.4/5

Pros and Cons

AI for Everyone by Coursera promises a gateway into the fascinating world of artificial intelligence, but like any course, it comes with its unique highs and lows. Based on my experience, here’s a closer look at what makes this course shine and where it might fall short:

From Whom You’ll Learn?

In this Coursera AI For Everyone course, you will learn from Andrew Ng, founder of Deeplearning.AI and co-founder of Coursera.

He has been the chief scientist at Baidu and the founding lead of the Google Brain team (he even refers to the infamous Google Cat in his lesson).

Andrew is well known for his interest in accelerating responsible AI practices. Therefore, you are in good hands. Be prepared to learn a lot.

Course Structure and Content

The AI For Everyone by Andrew Ng Coursera course has 4 modules consisting of 4 assignments (quizzes), 7 readings (mostly lecture notes), and 35 video lessons.

The platform mentions it takes around 6 hours to complete it; however, you can take your time learning the stuff. Let’s see what each module can teach us.

Module 1: What is AI?

As the title suggests, we get a realistic view of what AI is, its types, and its capabilities. Andrew discusses terminologies like machine learning, data, deep learning, etc. We also learn quite a bit about supervised learning (a subset of machine learning) and ensuring better performance through training of large neural networks using big data.

We also learn how Large Language Models work, the acquisition of valuable data, and what AI can and cannot accomplish. His rules of thumb to determine that, though imperfect, are quite effective. Andrew even provides a summary of ML’s strengths and weaknesses. The optional videos explain how artificial neural networks function. It was interesting.

Module 2: Building AI Projects

This section in the Coursera AI For Everyone course deals with starting AI projects. Andrew discusses the requirements for starting and the key steps in a machine learning project. We learn more about data science projects and the examples used to explain this are easily understandable yet engaging. Who knew learning how data science projects can help optimize the sales funnel could be so interesting?

Andrew then moves on to how AI and data science projects affect various job functions/tasks. All this is useful, but the significant part of this module is learning to choose AI projects. Are they feasible and valuable to the business? Should you outsource them or build them in-house? He also explains about technical, business, and ethical diligence. I had a wonderful time learning.

Finally, we learn about what working with the AI team is like. He delves Delves into acceptance criteria, test sets, and pitfalls to avoid while working with AI teams. Andrew also explains the reasons for inaccurate outputs. The optional video dives into the various AI tools and frameworks the AI teams use.

Module 3: Building AI in your Company

This module has awesome case studies on smart speakers and self-driving cars. Andrew explains the AI Pipeline (the steps AI takes to process commands) quite well. Who knew Alexa has to ‘think’ so much when we ask it to play music, tell the time, or call someone? The self-driving car case study was illuminating, as it helps us understand how complex AI projects are.

Now that we understand the process better, what do you think makes up an AI team? Here is a list of the most common job roles: software engineer, machine learning engineer, machine learning researcher, applied ML scientist (an in-between of MLE and MLR), data scientist, data engineer, and AI product manager.

The video lectures in this module also help us gain an idea of what it would take for companies to become AI or AI-first companies. The fun part? We get a glimpse into Andrew’s AI transformation playbook for this. You can also download the PDF version of the playbook using the link provided. Andrew also discusses the 5 pitfalls to avoid while working with AI.

The optional videos are technical. So, skip them if you want, but I think they can be incredibly helpful for interacting with and understanding the AI team in your company. Andrew explains concepts like Computer vision, Natural Language Processing (NLP), Speaker ID, TTS, Generative AI, Unsupervised learning, transfer and reinforcement learning, Generative Adversarial networks (GAN), and knowledge graphs. Believe me, having a better understanding of all this does help.

Module 4: AI and Society

In this final module, we learn about AI’s limitations, its impact on economies and jobs, and the ethical use of AI. Andrew mentioned some serious limitations such as bias and adversarial attacks. However, among the adversarial attacks, the one that struck me the most was deepfakes. (Obviously, anyone who watched The Blacklist knows how convincing deepfakes can be.) Andrew provides some tips to combat these limitations.

We also learn about how it helps develop economies and jobs. Its impact on developing economies is clearly explained. Moreover, many studies predict that AI will create more jobs than it displaces. But what I didn’t consider before was a way to predict which jobs AI can easily displace. Andrew discusses how to figure that out and deal with the consequences, too. Briefly put, this module was highly informative.

My Key Takeaways

• Neural networks were inspired by but are unrelated to the biological brain.
• Not all data is valuable.
• AI doesn’t do well with new types of data.
• Pick AI projects that are feasible and valuable.
• Progress in AI projects is possible even with small data, as the data required is problem-dependent.
• AI projects can be valuable even without 100% accurate results.
• Some AI projects require only a small team or just one knowledgeable person.
• In the long term, an in-house AI team is indispensable.
• AI strategies are company and industry-specific.
• AI is neither our savior nor our destructor. It is a tool.

Enrollment Options and Cost

The AI For Everyone Coursera course is free to audit. However, if you want to access graded quizzes or get the AI For Everyone certificate, you must upgrade.

You can purchase the course separately ($49) or subscribe to Coursera Plus (59/month or $399/annum); the latter would be a more cost-effective option if you plan on enrolling in multiple AI classes.

Who Should Take This Course?

Anyone who wants to know what exactly AI is and what working with AI teams is like should take this course.

You will love the examples the instructor provides.

If you are on a technical team, you will benefit from understanding the processes and principles he sets out.

You will get a lot more out of this course if you are a non-technical person wanting to grasp the extent of AI’s reach and influence.

In short, this course is really for everyone! 

What Others Say About This Course

After 50,000+ reviews, it still has a solid 4.8/5 rating from other learners.

Many people appreciate the course structure, clear and engaging explanations, and the content that helps them gain foundational knowledge about AI. However, some consider the content too basic and deem it unworthy to pay for a certificate.

They probably think that because they are already familiar with some of the concepts explained. Because this class is incredibly valuable to rookies who have just started to learn about AI and its capabilities.

Final Verdict: Is AI for Everyone course worth it?

AI For Everyone on Coursera is a wonderful course for anyone mystified as to what AI is or how it can affect our personal and professional lives. You won’t regret taking this course if you start watching with an open mind devoid of preconceived notions. In my opinion, it is one of the best foundational courses on AI accessible to all.

FAQs

1. Is AI For Everyone free?
You can access the course materials for free. However, you have to subscribe to Coursera Plus or purchase this course to submit your quiz answers or get a certificate of completion.

2. What makes AI for Everyone worth it?
The explanations are easy to grasp without making you feel like an idiot. It helps gain a better understanding of AI’s strengths and shortcomings. Moreover, you can download Andrew Ng’s Transformation playbook to gain insights into AI strategies. So, yep, worth it, don’t you think so?

3. Can I become an expert in AI by completing the AI For Everyone course?
No. The course only promises to provide you with a foundational knowledge of AI, and it does a fantastic job of fulfilling that promise. However, you can become an expert overnight with what is taught. I recommend enrolling in more courses with a deeper insight into AI.