Day 04 Fast: How Does Machine Learning Work? The Basics | DailyAIWizard - video Dailymotion

DailyAIWizard

Welcome to Day 4 of DailyAIWizard, your 20-minute journey to mastering AI!  In this beginner-friendly lesson, I’m Anastasia, your AI guide, and we’ll explore the basics of how Machine Learning works—the foundation of many AI systems. Sophia joins us for a demo using Orange to predict flower types with the Iris dataset, showing Supervised Learning in action! Whether you're new to AI or following along from Days 1-3, this series will guide you from the fundamentals to Python programming. Let’s dive in!   Task of the Day: Try Orange with the Iris dataset yourself and share your prediction results in the comments!   Link: orangedatamining.com/   Subscribe for Daily Lessons: Don’t miss Day 5, where we’ll dive deeper into Supervised Learning Explained. Hit the bell  to stay updated!    #AIForBeginners #MachineLearning #HowMachineLearningWorks #ArtificialIntelligence #DailyAIWizard

Transcript

00:00Welcome to Day 4 of Daily AI Wizard, your 20-minute journey to mastering AI.

00:09I'm Anastasia, your AI guide, here to make learning AI simple and fun.

00:14Today we're diving into the basics of how machine learning works,

00:17a key part of AI that powers many smart systems.

00:20We'll explore the process types and key concepts with a demo to bring it all to life.

00:24Let's get started.

00:25Let's quickly review what machine learning is since we touched on it in Day 3.

00:33Machine learning, or ML, is a subset of AI where machines learn from data without being explicitly programmed.

00:39Instead of writing rules, we give ML systems data, and they find patterns themselves.

00:44For example, spam filters learn from your emails to decide what's spam.

00:49ML is the foundation of many AI applications, and today we'll see how it works under the hood.

00:55How does machine learning work?

00:59It follows a simple process with four main steps.

01:02First, we collect data to teach the machine.

01:05Then, we train the model using that data.

01:08Next, we test the model to see how well it learned.

01:10Finally, we use the model to make predictions on new data.

01:14It's a cycle of learning and improving,

01:16and we'll break down each step to understand how ML brings intelligence to machines.

01:20Now, let's revisit the three main types of machine learning, which we introduced in Day 3.

01:30They are supervised learning, unsupervised learning, and reinforcement learning.

01:34Each type has unique uses and works differently depending on the data and task.

01:39We'll explore each one to understand how they fit into the ML process we just learned about.

01:43First up is supervised learning.

01:49This type uses labeled data, meaning we give the model inputs and the correct outputs.

01:53The model learns to predict outputs from inputs by finding patterns.

01:57For example, to predict house prices, we give the model data like house size as the input and the price as the output.

02:03It's like teaching a child with examples.

02:05This is a cat.

02:06This is a dog.

02:07So they can learn to identify them.

02:08Supervised learning is used in many applications.

02:15For example, spam detection labels emails as spam or not spam to train the model.

02:21Weather forecasting uses labeled data like past temperatures to predict the future.

02:26And image classification, like our cat and dog demo, labels images to teach the model.

02:31Supervised learning is widely used in machine learning because it's so effective for tasks with clear labels.

02:38Next is unsupervised learning.

02:43This type uses unlabeled data, meaning there are no correct outputs provided.

02:48The model finds patterns or groups in the data on its own.

02:50For example, it might group customers by their buying habits to help a store understand its audience.

02:56Unsupervised learning is like exploring without a guide.

02:59The model discovers hidden patterns by itself, which can be very powerful.

03:02Unsupervised learning has many uses.

03:09Market segmentation groups customers based on behavior, like who buys similar products.

03:14Anomaly detection, such as spotting fraud, finds unusual patterns in data.

03:19Recommendation systems, like suggesting similar products on Amazon, also use unsupervised learning to find patterns.

03:25It's great for discovering hidden patterns in data when we don't have labels to guide us.

03:29The third type is reinforcement learning.

03:35This method learns through trial and error, using rewards and penalties to improve.

03:40For example, a robot learning to walk might get a reward for taking a step and a penalty for falling.

03:45It's like training a pet with treats.

03:47The model learns by trying different actions and improving based on feedback.

03:51Reinforcement learning is great for tasks that need decision-making.

03:54Reinforcement learning is used in exciting applications.

04:01Game-playing AI, like AlphaGo, which beat a world champion in Go, learns by playing millions of games and getting rewards for winning.

04:08Robotics uses it to teach robots tasks like picking objects.

04:12Even self-driving cars use reinforcement learning to navigate traffic by learning from rewards.

04:17It's all about decision-making, making this type of ML perfect for dynamic environments.

04:22Let's talk about a key concept in machine learning.

04:29Features and labels.

04:30Features are the inputs we give the model, like the size of a house or the text of an email.

04:35Labels are the outputs we want to predict, like the house price or whether the email is spam.

04:40These are used in supervised learning to teach the model.

04:43For example, in our cat and dog demo, the features were the pet images, and the labels were cat or dog.

04:50Another key concept is training and testing data.

04:57Training data is what we use to teach the model.

05:00It's the study material.

05:02Testing data is separate and used to evaluate how well the model learned.

05:06A common split is 80% for training and 20% for testing.

05:09This split helps avoid overfitting, where the model memorizes the data instead of learning general patterns.

05:16It's like studying for a test, but saving some questions to check your understanding.

05:23Machine learning relies on algorithms, which are the rules the model uses to learn from data.

05:29Examples include decision trees, which make simple decisions like yes or no,

05:34and neural networks, which handle complex patterns, like in deep learning.

05:38The choice of algorithm depends on the task and data.

05:41For example, a simple task might use a decision tree, while a complex one might need a neural network.

05:46It's like picking the right tool for a job.

05:51Machine learning isn't without challenges.

05:54First, data quality matters.

05:56Poor data leads to poor results, like trying to learn from a blurry textbook.

06:00Overfitting is another issue, where the model memorizes the data instead of learning general patterns.

06:04ML can also be resource-intensive, needing significant computing power for large data sets.

06:10These challenges mean ML requires careful tuning to get the best results.

06:18That's it for Day 4, everyone.

06:20Thank you for joining me on this AI journey.

06:22I'm Anastasia, and I hope you enjoyed learning the basics of machine learning.

06:26If you found this lesson helpful, please give it a thumbs up, subscribe, and hit the bell for daily lessons.

06:31Tomorrow, we'll dive deeper into supervised learning.

06:35Let's hear from Sophia before we go.

06:36Loved showing you orange today, and I can't wait for more demos in this series.

06:41Day 5 is going to be amazing, so don't miss it.

06:44See you tomorrow, wizards!

Day 04 Fast: How Does Machine Learning Work? The Basics | DailyAIWizard

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