Learn AI from Google Free Course
Learn AI Google Free Online Course- If you don’t have a technical background but you still want to learn the basics of artificial intelligence, then google has provided free AI courses that can help you understand aritficial intelligence. I did the Google AI Courses and found it very useful.
I was initially very skeptical because I thought the course would be too conceptual. But I found the underlying concepts actually made me better at using tools like ChatGPT and Google Gemini. It cleared up a bunch of misconceptions I had about AI, machine learning, and large language models.
What Is Artificial Intelligence?
Starting with the broadest possible question — what is artificial intelligence?
It turns out (and I’m so embarrassed to admit I didn’t know this):
- AI is an entire field of study like physics.
- Machine learning is a subfield of AI, much like how thermodynamics is a subfield of physics.
- Deep learning is a subset of machine learning.
- Deep learning models can be further divided into:
- Discriminative models
- Generative models
- Large language models (LLMs) also fall under deep learning.
- ChatGPT and Google Bard sit at the intersection of generative models and LLMs.
Understanding the Landscape
Now that we have an understanding of how different disciplines sit in relation to each other, let’s go over the key takeaways for each level.
Machine Learning – The Basics
Machine learning is a program that uses input data to train a model.
That trained model can then make predictions based on data it has never seen before.
Example:
If you train a model based on Nike sales data, you can then use that model to predict how well a new shoe from Adidas would sell based on Adidas sales data.
Two Common ML Types
1. Supervised Learning
- Uses labeled data.
- Example:
Historical data plots total bill amount vs tip amount.
Blue dot = picked up
Yellow dot = delivered - Using a supervised model we can predict:
→ Expected tip amount for next order.
2. Unsupervised Learning
- Uses unlabeled data.
- Example:
Employee tenure vs income.
The model groups employees based on natural patterns. - We can then ask:
→ “Is a new employee on the fast track or not?”
Pro Tip –
Supervised learning compares its predictions to training data and tries to close the gap.
Unsupervised learning does not do this.
Deep Learning
Now we have a basic grasp of machine learning, it’s a good time to talk about deep learning.
Deep learning is a type of machine learning that uses something called artificial neural networks.
You only need to know:
- Inspired by the human brain
- Made up of layers of nodes/neurons
- More layers = more powerful model
Semi-Supervised Learning
Thanks to neural networks, we can do semi-supervised learning:
- A small amount of labeled data (e.g., 5%)
- A large amount of unlabeled data (e.g., 95%)
Example (Fraud Detection):
- Bank labels 5% of transactions as fraudulent or not.
- Leaves 95% unlabeled.
- Deep learning model learns from the 5%.
- Applies the learnings to the remaining 95%.
- Creates an aggregated dataset.
- Uses it to predict fraud in future transactions.
That’s pretty cool.
Discriminative vs Generative Models
There are tow models – Discriminative and Generative. Discriminative differentiates the things and Generative creates new things from the data.
Discriminative Models
- Learn from relationships between data labels.
- Classify only.
- Example:
Label images as cat/dog → Model predicts dog.
Generative Models
- Learn patterns in training data.
- Generate new data samples based on those patterns.
- Example:
Model learns features (ears, legs, tail, barking) → Generates a new dog image.
How to Know if Something Is Generative AI
If the output is:
- A number
- A classification (spam / not spam)
- A probability
➡️ Not generative AI
If the output is:
- Natural language text
- Speech
- Image
- Audio
➡️ It is generative AI
Types of Generative AI Models
1. Text-to-Text Models
Examples:
- ChatGPT
- Google Bard
2. Text-to-Image Models
Examples:
- Midjourney
- DALL·E
- Stable Diffusion
Can generate and edit images.
3. Text-to-Video Models
Examples:
- Google’s Imageen Video
- Cog Video
- Make-A-Video
4. Text-to-3D Models
Used to create game assets.
Example: OpenAI’s Shap-E.
5. Text-to-Task Models
Perform a specific task.
Example:
“Gmail summarize my unread emails.”
Large Language Models (LLMs)
Don’t forget:
- LLMs are a subset of deep learning.
- LLMs and generative AI overlap but are not the same.
Pre-Training vs Fine-Tuning
Large language models are:
- Pre-trained with massive general datasets.
- Text classification
- Question answering
- Summarization
- Text generation
- Fine-tuned with smaller, domain-specific datasets.
- Retail
- Finance
- Healthcare
- Entertainment
Example–
A hospital buys a pre-trained LLM and fine-tunes it with medical data to improve diagnostic accuracy for X-rays and tests.
Why this works:
- Big companies build general LLMs.
- Smaller companies refine them for their specific domain.
- Win-win for both.
Pro Tip (From the Course)
When taking the full course:
- Right-click video player
- “Copy video URL at current time”
- Helps jump back to exact timestamps
There are 5 modules total and you get a badge after each one.
Conclusion
I hope you like this article about Learn AI with Google Courses for Free . You can take these courses and learn about artificial intelligence and machine learning. These courses will help you understand how all this works. You can learn to use so many ai tools. And it is possible that you can create ai tools as well. I have already few articles on AI Agents for work automation on my site. You can check those to learn about ai agents and increase your productivity.
If you have any query then comment. Smile