7 Google AI Courses to Learn AI That Cost Nothing

0:00 There are five Google AI courses you can start today for free. And in this video, I'll show you what those are and everything you'll learn inside. I'm going to walk you through the five AI courses that one of the best companies in tech uses to train their six-figure employees on AI. And by the end of it,

0:15 you'll know exactly which AI course is the best for your situation. But first, you need to understand how this tech actually works if you don't want to be just another AI user. And that's because when new major AI updates drop, if you don't understand the principles behind it, you'll be starting from scratch

0:30 again. But that's exactly what the first Google course teaches you how to avoid. Now, of course, I can't fit every lesson from it into one video. So, if you want the complete training and official badges from Google you can actually put on your resume or LinkedIn, I'll leave a link to all the courses in the

0:45 description. This covers all the fundamentals you need to understand why AI behaves the way it does and why sometimes the outputs are incredibly good and sometimes they're complete garbage. So, these fundamentals will help you get rid of the frustration of trial and error when you don't get the

0:59 expected [music] results from AI. They will show you what's the root problem and how to actually solve it instead of praying to work. Inside the course, Google starts by defining AI as any computer program that does tasks which usually require human intelligence. So, when the YouTube algorithm recommends

1:14 you a video, that's AI. When you search a new restaurant on Google Maps and get the fastest route, that's AI. Every AI runs on machine learning, which is a system that learns patterns from examples. There are also other machine learning models like the discriminative model, which can only say yes or no. So,

1:30 if you show it a photo with a cat and ask if it's a dog, it'll know there's a cat in the image and say no. But if you ask it to generate a photo with a cat, it can't even though it knows exactly what a cat looks like. Now, between all these other models, there's one that's become extremely popular in the last few

1:45 years, and that's generative AI. This one's no longer limited to yes or no. It can create anything from text, images, videos to files. This is AI that creates new content, not just predictions. Everyone is using it nowadays, but very few of them really know how it works. The majority of people simply play

2:02 around with it. But, this is actually one of the biggest job opportunities for those who know the fundamentals. It can get you raises, promotions, or even a better job. And that's because when a hiring manager sees you know how AI works as a full system, he's 10 times more likely to choose you instead of

2:18 someone who barely knows to paste in a prompt. So, now let me tell you the core foundation of generative AI and what makes it different from any other machine learning model. It's what Google calls a neural network. This is a mass system built on the same structure as the human brain. It copies the way

2:32 neurons talk to each other in a real human brain. And this new method made it possible to train AI on billions of examples. So, now the model learns patterns and structure so well that it can predict what comes next. And this is what makes it so powerful. A discriminative model could only tell you

2:47 what's in a photo. But, if you tell generative AI to draw a cat, it'll actually create the picture. And a perfect example of this is Gemini. More and more new generative AI tools are coming, but the fundamentals behind will never change. So, if you know those, you'll already know 80% of the new tools

3:03 while your friends and colleagues have to start from zero again. That's the reason why Google made this course. But, if you want a complete base, you also need to know how language models like ChatGPT and Claude actually work behind the scenes. And that's exactly what the next Google AI course dives into. Intro

3:18 to large language models covers how generative AI can understand and create human language. And this is crucial to know if you want to write better prompts high-quality outputs. But, in the same time, this technology is why thousands of people get in trouble at [music] their job. And it's not because AI is

3:34 going to replace them or something similar. It's because nobody told them how large language models work. And that's why they make a big mistake without even knowing. These models are usually shortened to LLMs. And what an LLM actually is is a word predictor, just like the auto complete on your

3:49 phone. It gets trillions of words from the internet [music] and studies the relationships between them, hence the name large. Now, the theory for LLMs existed even 30 years ago, but the hardware and software back then couldn't handle this many inputs. These inputs are called parameters, and the more

4:04 there are, the better the results. Modern LLMs have billions of parameters, which is why they can suddenly understand complex stuff like sarcasm, coding logic, and even poetic tone. It's very powerful, but here's what makes it dangerous. When you run an LLM, it looks at all the previous words and tries to

4:20 guess the next one. So, if you're writing, "How are", the LLM will predict the next word is you. It sees a 90% chance [music] for you, and maybe only 2% for things. It always picks the highest probability, and the same [music] thing happens inside the most popular AI chatbots like ChatGPT and

4:37 Claude. >> [music] >> You can ask them to write an entire research paper and get it in seconds. But, Google also warns that these LLMs are dangerous. When they predict the next word, they focus on what's most likely over what's actually logical. If you wanted to write, "How you things",

4:50 the AI would pick, "How are you?" because that's the correct phrase. LLMs can confidently say things that aren't true, a phenomenon called hallucination. This happens because the model's designed to be creative. So, if it doesn't have enough data, it'll just make up an answer. And this isn't a big

5:04 deal for a text message, but it can quickly become a real problem in other areas. There are thousands of people who have serious problems at their job because of this. They think that just because AI is based on math, it'll always get the right answer. So, they let AI work with sensitive data, which

5:18 later ends up in a real problem. You need to know how and when these AI mistakes happen because they can cause serious effects. That's why Google made this course. Now, if you still want to use AI at your work or anywhere else without worrying about these potential mistakes that it usually makes, the next

5:33 course covers how to avoid them. Google called it responsible AI, and it's something that more and more employers started to appreciate among their employees. But, the problem is very few people actually know how to use AI responsibly. So, this course shows you everything you need to get another

5:48 advantage inside the job market, but the principle doesn't only apply to work, it applies to any information you get from AI. This Google AI course took me less than an hour, so it's worth considering the time. Now, there are two key principles I discovered inside. The first one I call the mirror effect, and

6:03 this is one of the toughest problems we're still dealing with in AI. So, AI isn't naturally objective, it's a mirror of its training data. This means its results depend 100% on the inputs it gets. If the training data is full of human the AI will copy those biases. It doesn't have morals or logic. In the

6:21 beginning AI used to call doctors men and nurses women. That's biased, and depending on where it pulls data from, it can give you totally different results. But, this is usually fixed using the second principle called the human in the loop. Google strongly suggests AI should be used as a tool,

6:36 not the decision-maker. The human should analyze the problem, look at what the AI gives them, and make the final call. And that's because AI still makes mistakes. It has biases and it hallucinates. [music] That's why you need a human in the process. Imagine asking AI to write an email for you. Dozens of people just

6:52 copy-paste and send it, but what if the tone's [music] off, or maybe it's way too informal. The better move is to get the result, read it, and then tweak it until it's exactly what you want. Now, [music] for emails, it's not a huge deal, but there are situations where being human in the loop is absolutely

7:06 crucial, especially in health and finance. And now that you know how AI generates text and how to use it responsibly, I need to show you the other big part of generative AI, image generation. Google has this course called intro to image generation, and the information inside is way above

7:22 other paid courses on the topic. [music] Here, Google doesn't give you a copy-paste prompt to generate an image. It shows you the real process that goes behind creating an image. So, if you ever wondered why some people generate realistic photos with AI while you kept struggling with poor quality, this is

7:37 why. Inside, you'll see several algorithms used for [music] image generation, and that's because AI doesn't paint like a human. It uses different algorithms that help it create visuals from nothing. But once you understand how it works, you know exactly [music] what to do to get the

7:51 same realistic photos you've seen online. Google starts by showing you the popular algorithms, but the main [music] focus is a model created in 2015 that exploded once ChatGPT came out. I'm talking about the diffusion model, which is the secret behind tools like Google Nano Banana. The way this works is it

8:07 doesn't actually draw anything. Instead, it's like a sculptor who sees a statue inside a block of marble and chips away until the figure shows up. But for AI, the block of marble is just a screen full of noise that looks like an old TV with no signal. This process is called denoising. To train the AI, Google shows

8:24 it a clear picture, like a sunset, and slowly adds noise until it's unrecognizable. The AI's job is learning how to reverse that. So, when you give it a prompt like a sunset over the ocean, the AI starts from pure noise and removes pixels that don't match a sunset while keeping the ones that do. It goes

8:40 pixel by pixel until everything lines up. This is why sometimes you'll see the image looking blurry at first, then suddenly it's perfect. And this method works for two main categories. The first one is unconditioned generation, which means you don't give the AI any extra instructions. This is used for creating

8:56 human faces or taking low-res images to high-res. And the second is conditioned generation. This is for any method that needs extra instructions. For example, when you write a prompt to get an image, that's called text-to-image [music] and uses this method. Same with image-to-image where you give the AI an

9:12 image and a prompt and get a new image back. This can remove an object from an image or add one. So, if you want to create images with AI, this course dives deeper into it. Now, all the courses I've showed you so far are dependent on the encoder-decoder architecture. This is the secret behind every AI that can

9:27 summarize massive texts, answer tough questions, and translate between languages instantly. It also explains why some AI models crush certain tasks while others struggle. So, if you want to choose the best AI model for each task you do on a daily basis, Google just released a full course on what's

9:43 called the encoder-decoder architecture. Here's the simplest way to understand it. Imagine you want to translate a sentence from English to Spanish. You can't just go word for word because languages have different rules. A nine-word sentence in English might need 11 words in Spanish to mean the same

9:58 thing. To solve this, AI uses two parts that work together. The encoder is the first part. Think of it like a translator who listens to the English sentence and figures out the exact meaning behind the words. It doesn't just look at vocabulary, it looks at how words relate to each other. It takes all

10:13 that complex language and turns it into a math format called a context vector. If the encoder listens, the decoder speaks. It takes that math and translates it into Spanish. Google also talks about the attention mechanism, which lets the decoder look back at the sentence. [music] If it's translating a

10:28 specific noun, it looks back at the adjectives near it in the original English sentence. This helps it focus on the most important parts, not just treat everything equally. This is why Google Translate nowadays feels way more natural than 5 years ago. Now, there are a lot of AI models out there. Some have

10:44 just the encoder, some just the decoder, and others have both. But, it doesn't mean one is better than the other. Each one is the best at a different type of task. So, choosing the right model can drastically increase the quality of your results. And Google covers everything about that in this course. [music] But,

10:59 now that you know these five Google AI courses exist, did you know that there's seven AI certifications that are worth more than degrees in 2026? I did a separate breakdown for them. That video is on the screen right now. Thanks for watching, and I'll see you in the next one.