- 2 days ago
Wizards, join Anastasia, Isabella, Ethan, Sophia, and Olivia for Day 34 of the DailyAIWizard Python for AI series! 🚀 Learn how to use lists and tuples to organize data for AI projects, from dynamic feature sets to fixed model parameters. Sophia leads two demos with NumPy, Ethan explains every line, and Olivia adds tips. Perfect for beginners building on Day 33! 💻 Get ready for Day 35: Python Dictionaries and Sets. Subscribe, like, and share your ai_data.py output in the comments! Join our Discord or X for more tips! Code the Future, Wizards!
🔗 Links:
• Python: python.org
• VS Code: code.visualstudio.com
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• X: x.com/dailyaiwizard
• Instagram: www.instagram.com/dailyaiwizard
Hashtags:
#Python #LearnPython #PythonForAI #AICoding #PythonTutorial #CodingForBeginners #PythonLists #PythonTuples #AIProgramming #TechTutorial #DailyAIWizard #CodeTheFuture
Tags:
Python, Learn Python, Python for AI, AI Coding, Python Tutorial, Coding for Beginners, Python Lists, Python Tuples, AI Programming, Tech Tutorial, Python 3, Coding Journey, VS Code, Beginner Programming, Data Science, DailyAIWizard, Code the Future
🔗 Links:
• Python: python.org
• VS Code: code.visualstudio.com
• Discord: discord.com/channels/1397945816349675600/1397945819260391521
• X: x.com/dailyaiwizard
• Instagram: www.instagram.com/dailyaiwizard
Hashtags:
#Python #LearnPython #PythonForAI #AICoding #PythonTutorial #CodingForBeginners #PythonLists #PythonTuples #AIProgramming #TechTutorial #DailyAIWizard #CodeTheFuture
Tags:
Python, Learn Python, Python for AI, AI Coding, Python Tutorial, Coding for Beginners, Python Lists, Python Tuples, AI Programming, Tech Tutorial, Python 3, Coding Journey, VS Code, Beginner Programming, Data Science, DailyAIWizard, Code the Future
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📚
LearningTranscript
00:00Hello Wizards, I'm Anastasia, your lead guide for Day 34 of the Daily AI Wizard Python for AI series.
00:09After mastering variables in Day 33, today we're exploring lists and tuples, the magical containers for organizing AI data.
00:17Get ready for a thrilling journey with hands-on demos that'll make your code shine.
00:22Code the future, Wizards!
00:24Hey Wizards, I'm Isabella and I'm so excited you're here.
00:30Lists and tuples are like spellbooks for storing multiple data points, perfect for AI projects like neural networks.
00:38Join us for fun demos and a challenge that'll spark your creativity.
00:43Let's make Python magic happen together.
00:47Ethan here, Wizards.
00:49I'll break down the code behind lists and tuples, showing you how they power AI applications.
00:55From adding data to analyzing it, you'll see why these are essential tools.
01:02Let's dive in and code like pros.
01:06Sophia here.
01:08I'm pumped to lead the demos and show you lists and tuples in action.
01:14Get ready for some serious coding fun.
01:18Olivia here.
01:20I'll share tips and ask questions to keep your learning smooth.
01:24Let's conquer lists and tuples, Wizards.
01:27Wizards, lists, and tuples are your go-to tools for organizing multiple data points in Python, making them essential for AI.
01:38Lists are flexible, letting you store and modify data like feature sets for machine learning models.
01:45Tuples are fixed, perfect for unchangeable AI parameters like learning rates or model names.
01:51These structures are the backbone of data handling in AI projects.
01:57Exactly, Anastasia.
01:59Imagine storing sensor data in a list or fixed model settings in a tuple.
02:05These are real-world AI tasks.
02:08Today, you'll learn to create, manipulate, and use them in demos, setting you up for Day 35's dictionaries and sets.
02:17Stick with us to unlock the power of organized data, Wizards.
02:22Today, Wizards, we're diving deep into lists and tuples.
02:26You'll learn how to create them, modify lists, access elements, and use them in AI with NumPy.
02:33Our demos will show you how to organize data like a true wizard, and we'll wrap up with a fun challenge.
02:40Sophia will lead the demos with her infectious energy, while Ethan explains the code in detail.
02:46And Olivia adds practical tips.
02:50By the end, you'll be ready to handle AI data like a pro.
02:54Let's embark on this magical coding journey together.
02:58Wizards, Master Lists, and Tuples.
03:03Wizards, meet your dream team for Day 34.
03:07Anastasia and I are here to guide you with clear, engaging explanations, making lists and tuples fun and easy.
03:14Ethan's our code wizard, diving into the details to ensure you understand every line of Python magic.
03:22Sophia's leading our demos with her vibrant energy, showing you lists and tuples in action.
03:29Olivia's helping with setup tips and asking key questions to keep you on track.
03:34We're all here to make you an AI coding wizard.
03:38Let's get started.
03:39Your crew's ready, Wizards.
03:41Wizards, lists are like magical spell books that hold multiple data items in Python, such as numbers, strings, or even other lists.
03:52You can add, remove, or change items, making lists perfect for dynamic AI data, like feature sets or sensor readings.
04:00Create a list with square brackets, like features, 0.5, 1.2, 2.8, and get ready to see them in action.
04:12Anastasia, how do lists help with AI projects?
04:16Are they used a lot?
04:18Great question, Olivia.
04:20Lists are everywhere in AI.
04:22Think of storing input data for a neural network or tracking model outputs.
04:28They're flexible, so you can update them as your AI model evolves, which we'll show in our demos.
04:34Lists are AI spell books.
04:37Tuples are like enchanted boxes in Python, wizards, holding data that shouldn't change, like model parameters or fixed settings.
04:47You create them with parentheses, like params, charker, 0.0, 1, 100.
04:54They're fast, secure, and perfect for AI tasks where data needs to stay constant.
05:01Exactly.
05:02In AI, tuples store things like a model's learning rate or number of epochs, ensuring they don't accidentally change.
05:10Unlike lists, tuples are immutable, meaning they're locked after creation.
05:15We'll see how they shine in our demos, wizards.
05:19Tuples lock your AI data.
05:22Wizards, lists, and tuples both store data, but they serve different AI purposes.
05:29Lists are mutable, so you can update data like a growing dataset for machine learning.
05:35Tuples are immutable, ideal for fixed settings like a model's configuration that must stay constant during training.
05:43Think of lists as your dynamic AI notebooks, where you can add or edit data, like sensor inputs.
05:51Tuples are like sealed AI blueprints, keeping critical parameters safe.
05:57Our demos will show you how to use both for AI projects, wizards.
06:02Wizards, creating a list is super easy.
06:07Use square brackets like data, jeert, 1, 2, 3, to store numbers, strings, or mixed data.
06:18In AI, you might use a list like features, shear, 0, 0.5, 1.2, 2.8 for model inputs, and you can modify it anytime.
06:29Great point, Olivia.
06:32Lists are flexible, so you can add or change items as your AI project grows.
06:37Save your list in a .pi file or try it in the Python shell.
06:41We'll show you how in the demos.
06:44Let's start organizing data, wizards.
06:46Lists are so simple.
06:49Wizards.
06:49Tuples are perfect for AI settings that shouldn't change, like a model's learning rate or name.
07:06Once created, tuples stay locked, keeping your data safe and reliable.
07:11Tuples are lightweight and fast, which is great for AI performance.
07:18You can't modify them, but you can access their data, like params, 0, to get 0.01.
07:26We'll show you how tuples work in our demo, wizards.
07:30Tuples secure your data.
07:34Wizards.
07:34For features, you can access list items using their index, starting at 0.
07:40For features equals 0.5, 1.2, 2.8, features 0 gives 0.5, and features 1 gives 1.2.
07:53This is super useful in AI for grabbing specific data points, like model inputs.
07:59Ethan, what happens if I try an index that doesn't exist?
08:06Great question, Olivia.
08:09You'll get an indexer, so always check the list's length with LAN features.
08:15We'll show indexing in our demo, wizards, so you can access data like pros.
08:21Grab list data easily.
08:24Wizards.
08:25Tuples use indexing just like lists.
08:28For params, solder 0.01, 100, params ada gives 0.01.
08:36This is great for AI when you need to pull fixed parameters, like a learning rate, without changing the tuple.
08:44Exactly.
08:46Tuples are immutable, so you can only read data, not change it.
08:51Use params, 1, to get 100, or LAN, params, for the tuple's length.
08:57We'll demonstrate this in our AI demo, wizards.
09:02Wizards lists are mutable, so you can change them.
09:06Use features.
09:08Chartrefuse 1.5 to update the second item, or features.append 3.5 to add a new item.
09:17You can also use methods like features.remove 1.5 to delete items or features.pop to remove the last one.
09:34These operations make lists perfect for evolving AI data.
09:40We'll show append, in our demo, wizards.
09:44Wizards, tuples can't be changed after creation, which is a superpower for AI.
09:50Immutable tuples ensure critical data, like model parameters, stays consistent during training.
09:58For example, params, darj, 0.01, 100, stays locked, keeping your AI reliable.
10:08Anastasia, why not just use lists for everything?
10:11Great question.
10:14Tuples are faster and safer for fixed data, like AI settings, while lists handle changing data.
10:21You'll see this difference in our demos, wizards.
10:24Wizards, lists come with powerful methods.
10:28Use append, to add items, remove, to delete specific values, or sort, to order your list.
10:35For AI, methods like Xtend, can combine datasets, making lists a go-to tool for data manipulation.
10:45These methods make lists super versatile for AI tasks, like sorting model predictions or adding new data points.
10:54Try features.sort, to arrange numbers in order.
10:59We'll use append, as, in our demo to show its magic, wizards.
11:03Wizards, it's time for some coding magic.
11:08We'll run two demos, one with lists to manage AI features and another with tuples and lists for AI model parameters.
11:16I'll lead you through each step, and Ethan will explain the code, it's going to be epic.
11:23Make sure your Python VS Code and NumPy are set up from Day32 Wizards.
11:28Olivia will share tips and will guide you to create lists, .pi and tuples.pi.
11:34Get ready to organize AI data like a pro.
11:38Wizards, let's prep for our demos.
11:42Open VS Code, create lists.py and tuples.py, and save them in a folder like Python Demo.
11:50Run pip install numpy to ensure NumPy's ready for our AI demo.
11:54I'll show you how to run these scripts soon.
11:59Sophia, what if wizards missed installing NumPy?
12:03No worries, Olivia.
12:07Open a terminal, type pip install numpy, and activate your virtual environment if you're using one.
12:13This setup ensures our demos run smoothly, wizards.
12:19Wizards, our first demo in lists.py creates a list of AI features and modifies it.
12:26We'll start with 0.5, 1.2, 2.8, update an item, add a new one, and print results.
12:34This shows how lists handle dynamic AI data.
12:38Let's make it happen.
12:39Here's the code, features equals, 0.5, 1.2, 2.8, features.append, 3.5, features, 1, equals 1.5.
12:54We print the list, its first item, and length with len.
12:59This is perfect for managing AI inputs, wizards.
13:03Lists in action, wizards.
13:07Manage AI data now.
13:10Wizards, features equals, 0.5, 1.2, 2.8, creates a list with three floats, ideal for AI feature data.
13:22Features, 1, equals 1.5 updates the second item, and features.append, 3.5, adds a new item to the end.
13:32These operations make lists dynamic for AI tasks like updating data sets.
13:39I love how flexible lists are, Ethan.
13:43Wizards, this means you can tweak AI inputs as your model learns.
13:48Try changing features, 0, to a new value and see the results, it's like updating a spellbook.
13:55In print, features, features, we display the entire list, showing, 0.5, 1.5, 2.8, 3.5.
14:08Print, first feature, features, 0, grabs the first item, 0.5.
14:16The len, features, function returns 4, the list's length, which is key for AI data processing.
14:23That's so cool, Ethan!
14:28Wizards, printing lists helps you check your AI data, and len, tells you how many items you're working with.
14:35Try printing your own list to see it in action, it's like revealing a spell's power.
14:41Wizards, let's run lists.py.
14:44In VS Code, open the terminal with Control Plus or CMD Plus, type Python3Lists.py, and hit enter.
14:54You'll see the updated list and its details, proving your AI data skills are growing.
14:59The output will show features, 0.5, 1.5, 2.8, 3.5, first feature, 0 for 0.5, and length, 4.
15:14It's like casting a spell to organize your AI data.
15:18Try it yourself, wizards, and share your results.
15:22Wizards, our second demo in tuples.py combines tuples and lists for AI.
15:28We'll use a tuple for fixed model parameters and a list for dynamic data, then convert the list to a number PY array.
15:37This shows how both structures power AI projects.
15:40The code creates model underscore terms equals 0.01, 100, AI wizard model, for fixed settings and data underscore points equals 1.1, 2.2, 3.3, for dynamic data.
15:58We append to the list and convert it to a number PY array with np.array.
16:05It's AI-ready coding, wizards.
16:09Wizards, model underscore terms equals, 0.01, 100, AI wizard model, creates a tuple with a float, integer, and string, perfect for fixed AI settings.
16:22Tuples are immutable, so these values stay safe.
16:27This is great for ensuring model parameters don't change during AI training.
16:33I love how secure tuples are, Ethan.
16:37Wizards, this tuple could hold a learning rate, epics, and model name for an AI project.
16:43Try printing model underscore prums to see it.
16:47It's like a locked spell book for AI.
16:48In data underscore points equals, 1.1, 2.2, 3.3, we create a list, then use data underscore points.append, 4.4, to add a new item.
17:04This makes lists ideal for growing AI datasets, like adding new sensor data.
17:10We print data underscore points to see, 1.1, 2.2, 3.3, 4.4.
17:18That's so powerful, Ethan.
17:23Wizards, lists let you update data as your AI model learns, unlike fixed tuples.
17:30Try adding your own data to data underscore points and print it.
17:34It's like building a dynamic AI spell.
17:36Wizards, NP underscore array equals NP dot array, data underscore points, converts our list, 1.1, 2.2, 3.3, 4.4, into a number py array for AI math.
17:54Arrays are faster for operations like matrix calculations in neural networks.
17:59Printing NP underscore array shows, 1.1, 2.2, 3.3, 4.4, ready for AI.
18:09That's awesome, Ethan!
18:12Wizards, number py arrays are like turbocharged lists for AI models.
18:18Convert your lists to arrays to power up your AI projects.
18:22Try it in our demo.
18:24Arrays supercharge AI
18:25Wizards, let's run tuples.py.
18:31In VS Code's terminal, type python3.py and hit enter.
18:37You'll see model parameters, updated data points, and a number py array, showing how lists and tuples work together for AI.
18:45The output shows model parameters, 0201, 100, AI wizard model, data points, 1.1, 2.2, 3.3, 4.4, and more.
19:05It's like revealing your AI blueprint.
19:08Try it and share your results, Wizards.
19:11Run your AI demo.
19:13Wizards, slicing lets you grab parts of a list, like features, 0,2, to get, 0.5, 1.5.
19:24Use features, 2, for the first two items or features, 1, smiley face, for all after the first.
19:32Slicing is key for selecting AI data subsets, like training samples.
19:40Slicing is like cutting a perfect piece of your AI data set.
19:45In AI, you might slice a list to analyze specific features.
19:50Try Features.
19:50In the Python shell to see it work, Wizards.
19:56Wizards.
19:57Know output from lists.p.
20:00Check if Python's in path with Python 3 version.
20:04Ensure lists.py is saved in your current folder.
20:08Use pwd to verify your directory.
20:11If you're in the wrong folder, navigate with cdpython demo to run your demo.
20:16For tuples.py, module not found error.
20:21No module named numpy means number py isn't installed.
20:26Run pip install numpy in your terminal.
20:30Check your code matches ours and drop any errors in the comments.
20:34We'll help you, Wizards.
20:37Wizards.
20:39Virtual environments keep your AI projects tidy.
20:42Create one with Python 3, m-a-v-n-v-my-n-v.
20:47Then activate it with source, my-env-ach-bintos-activate on Mac Linux.
20:53Or my-env-jute-scripts, activate on Windows.
20:58This isolates numpy for tuples.py, avoiding conflicts.
21:04Olivia, why are virtual environments so useful for AI?
21:07They ensure libraries like NumPy don't clash across projects, Anastasia.
21:15It's like giving each AI project its own spellbook.
21:20Keeping your code clean.
21:22Try it for your demos, Wizards.
21:25Wizards.
21:26Open the Python shell with Python 3 and try data.
21:31Shut 1, 2, 3, print data.
21:34Add an item with data.
21:36Append 4 and print again to see 1, 2, 3, 4.
21:42The shell's a magical playground for testing lists instantly.
21:47It's perfect for experimenting, Wizards.
21:50Try Print Data to get 1 or Print Lend Data for 4.
21:55The shell lets you play with lists before writing full scripts,
21:59prepping you for Day 35's dictionaries.
22:02Wizards, lists, and tuples are key in AI libraries.
22:08NumPy converts lists to arrays for fast math, like in our demo.
22:13Pandas uses lists for data columns.
22:15And TensorFlow uses tuples for fixed model configs.
22:19These structures are your AI foundation.
22:21You're already using lists with NumPy, Wizards.
22:26That's a huge step.
22:29In Day 35, you'll see how dictionaries and sets build on this for more AI power.
22:36Keep practicing lists and tuples to master AI coding.
22:39Wizards, save lists, .pi, and tuples, .pi in VS Code with Contral Plus S or CMD Plus S.
22:50It's like sealing your spells in a vault.
22:53Store them in a folder like Python Demo for easy access.
22:57Back them up on GitHub or Cloud Storage to keep your AI code safe.
23:02Share your scripts with friends or on our Discord, our ex, Wizards.
23:08Your lists and tuples are a badge of AI coding pride.
23:11Saving ensures you're ready for Day 35's dictionaries and sets.
23:16Keep coding the future.
23:18Wizards, here's your challenge.
23:20Create AI data PY with a list of AI features, like features,
23:25JIR 1.0, 2.0, 3.0, and a tuple of model settings, like settings, JIR 0.0.1.50.
23:36Append a new feature, print both, and run with Python 3 AI data PY.
23:42Share your output in the comments.
23:44This is so fun, Wizards.
23:47Try adding features.append 4.0 and print settings for the learning rate.
23:53Show us your results on YouTube or Instagram.
23:57It's like casting an AI spell.
24:00Get ready for Day 35 with this practice.
24:04Wizards, hit subscribe, like this video,
24:07and share your AIData.py output in the comments.
24:12Got questions about lists or tuples?
24:14We're here to help you shine.
24:17Join our profile on LinkedIn to connect with other Wizards
24:20and boost your AI skills.
24:22Our community is magical, Wizards.
24:26Post your code, ask for tips,
24:28or share your coding wins on LinkedIn.
24:32Subscribe to stay ready for Day 35's dictionaries and sets.
24:36Let's code the future together.
24:39Wizards, Day 35 is coming.
24:42Python, dictionaries, and sets.
24:44You'll learn to store key value pairs and unique data,
24:48perfect for organizing complex AI data sets.
24:51Build on your list and tuple skills for even more AI magic.
24:55Dictionaries and sets will take your AI coding to the next level, Wizards.
25:01Subscribe to catch Day 35 and keep practicing your lists and tuples.
25:07Get ready for another exciting lesson in our Python journey.
25:11Dictionaries await, Wizards.
25:15Wizards, you've conquered lists and tuples.
25:18Huge congratulations.
25:21Your lists at PY and tuples to PI demos
25:24show you're ready to organize AI data like a pro.
25:27Keep practicing and get excited for Day 35's dictionaries and sets,
25:32your AI data Wizards.
25:34I'm beyond proud of you, Wizards.
25:37You've learned to manage dynamic lists and secure tuples,
25:41skills that power real AI projects like neural networks and data analysis.
25:46Your AI data PY challenge is a testament to your growing Python powers.
25:51Share it in the comments or on Instagram.
25:54Our daily AI wizard.
25:55Subscribe, hit the bell, and join our Discord or X
25:59to connect with other Wizards, ask questions, and share tips.
26:04Day 35 will dive into dictionaries and sets,
26:08so keep your Python setup ready.
26:10You're coding the future, Wizards.
26:12Keep shining and let's make more AI magic together.
26:15Keep coding the future.
26:17Amazing work, Wizards.
26:20Your lists and tuples are AI ready,
26:23and I loved explaining the code.
26:25See you in Day 35 for more coding adventures.
26:29Lists and tuples nailed.
26:33Wizards, you're absolutely incredible.
26:37Leading the demos was so fun,
26:39and watching you master lists and tuples is pure magic.
26:43Your AI skills are soaring,
26:45and I can't wait to see your AI underscore data dot PY outputs.
26:50Share them with it daily AI wizard.
26:53Subscribe for Day 35's dictionaries and sets,
26:56join our community on Discord or X,
26:58and keep coding with confidence.
27:01You're true Wizards,
27:03and together, we'll code the future Wizards.
27:06You're Python stars.
27:08You nailed lists and tuples, Wizards.
27:14Your AI data skills are awesome,
27:17and I'm thrilled to see you grow.
27:21Let's dive into Day 35 together.
27:24On to Day 35.