If you are an entrepreneur or product manager, you have probably heard that artificial intelligence (AI) can do amazing things—predict trends, personalize experiences, generate content, and more. But what does it actually mean to train an AI model? And how does a machine “learn” in the first place?

You do not need a computer science degree to find out. In fact, MIT offers a free lab—part of its Introduction to Deep Learning (6.S191) course—that walks you through building and training your very first AI model. Even if you have never written a line of code, this lab will give you a practical, hands-on understanding of how modern AI works.

What is MIT Deep Learning Lab 1?

Lab 1 is titled "Building and Training Your First Neural Network." It guides you step-by-step as you train a simple AI model to recognize handwritten digits—something our brains do instantly but computers need to learn.

The lab uses Google Colab (a free browser-based platform), so you do not need to install anything. You run small chunks of code with the click of a button, see immediate results, and watch your model improve as it learns.

What You Will Learn – Step by Step

Here is what you will explore in this short lab:

1. What a Neural Network Is
   Think of it as a virtual brain made of layers that learn patterns in data. You will see how each layer transforms information to help the model make better predictions.
2. Working with Image Data
   You will use the MNIST dataset—a collection of 70,000 handwritten numbers (0 through 9). This shows how AI learns from raw visual data.
3. Building a Model with Keras (No Coding Required)
   Using a simple interface, you define the shape of your model: how it takes in data, processes it, and makes decisions.
4. Training the Model
   You will train the model and see how it improves over time. Concepts like loss, accuracy, and epochs will start to make sense—without diving deep into math.
5. Evaluating Predictions
   Finally, you will test the model on new data and visualize how well it performs—watching it correctly identify new handwritten numbers.

💡 Why This Matters for Business Leaders

Going through this lab gives you more than just a peek behind the curtain—it equips you to:

• Ask the right questions when working with AI teams
• Understand the limitations of models and avoid “AI hype”
• Spot opportunities to use AI in your own product or service
• Translate customer needs into meaningful AI features

You will walk away with a clearer grasp of how AI products are built—and the confidence to lead conversations that once felt too technical.

✅ Bonus: Other Great Learning Options

While MIT’s Lab 1 is a great starting point, you can also explore:

• Coursera's Deep Learning Specialization by Andrew Ng (theory + practice)
• Fast.ai’s Practical Deep Learning (hands-on, fast-paced)
• Google’s Machine Learning Crash Course (interactive and beginner-friendly)

But if you are looking for one powerful, free place to begin—MIT’s Lab 1 is hard to beat.

Ready to see AI in action? Head over to introtodeeplearning.com or run Lab 1 on Google Colab and start your journey today.