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Learn how Python dictionaries organize metadata and sets identify unique values. Follow demonstrations with NumPy and practice working with AI project data.

In this episode:
• Dictionary keys and values
• Sets and unique data
• AI metadata examples and an ai_metadata.py task

This is Day 35 of the 90-day AI and Python course on DailyAIWizard.
Full course playlist: https://www.dailymotion.com/playlist/x9qwfu
Previous: Day 34: Python Lists and Tuples for Beginners
https://www.dailymotion.com/video/x9o4mve
Next: Day 36: Python Control Flow for Beginners
https://www.dailymotion.com/video/x9o9ukk

Links from this episode:
https://python.org
https://code.visualstudio.com
https://dailyaiwizard.com
https://discord.com/channels/1397945816349675600/1397945819260391521
https://x.com/dailyaiwizard
https://www.instagram.com/dailyaiwizard

Hosted by Oliver Bodemer. Presented with AI-generated tutor avatars: Anastasia, Sophia, Isabella, Ethan, Olivia.
Channel: https://www.dailymotion.com/DailyAIWizard
YouTube: https://youtube.com/@dailyaiwizard

#PythonDictionaries #PythonSets #DailyAIWizard

Category

📚
Learning
Transcript
00:00Hello, Wizards. I'm Anastasia, your guide for Day 35 of the daily AI Wizard Python for AI series, and I'm thrilled to dive into dictionaries and sets.
00:09After conquering lists and tuples in Day 34, today we'll explore these powerful tools for organizing AI data, like model metadata or unique features.
00:19Get ready for an epic journey with demos and challenges that'll ignite your coding passion. Let's code the future, Wizards.
00:25Hey, Wizards. I'm Isabella, and I'm so excited you're here to unlock the magic of dictionaries and sets.
00:33These structures are like enchanted vaults for storing key-value pairs or unique data, perfect for AI tasks like tracking model parameters.
00:42Join us for vibrant demos and a challenge that'll make your Python skills shine.
00:47Ethan here, Wizards. I'll break down the code for dictionaries and sets' bio data.
00:52Sets are like magical filters that keep only unique data, and dictionaries organize data efficiently.
00:58Both are AI essentials.
01:00Stick with us to master these tools and level up your coding game.
01:04Sophia here, Wizards.
01:07I'm pumped to lead today's demos, showing you dictionaries and sets in action for AI.
01:12Get ready for some serious coding magic.
01:15Olivia here. I'll share tips and ask questions to make your learning smooth and fun.
01:23Let's dive into dictionaries and sets, Wizards.
01:31Wizards, dictionaries, and sets are game changers for AI coding.
01:35Dictionaries store key-value pairs, like a model's name and accuracy,
01:40making data retrieval lightning-fast for AI applications.
01:43Sets keep only unique items, perfect for filtering duplicate features and data sets.
01:49These tools streamline your AI projects, and we'll show you how in our demos.
01:54Absolutely, Anastasia.
01:56Imagine organizing model metadata in a dictionary,
01:59or ensuring unique data points in a set.
02:02These are real-world AI superpowers.
02:05Today's lesson builds on Day 34 and preps you for Day 36's control flow.
02:10Get ready to organize data like a true wizard.
02:14Wizards, today we're unlocking dictionaries and sets.
02:18Your keys to mastering AI data.
02:20You'll learn to create dictionaries for structured data,
02:23manage unique items with sets,
02:25and apply them in AI with NumPy demos.
02:28Our challenge will spark your creativity,
02:30so let's dive into this magical coding journey.
02:34Sophia's leading our demos with energy,
02:36Ethan's explaining every line,
02:38and Olivia's got your back with tips.
02:40By the end, you'll handle AI data like a wizard,
02:44ready for Day 36 control flow.
02:46This is your chance to shine in Python.
02:49Wizards, meet your Day 35 dream team.
02:54Anastasia and I are here to guide you with clear, engaging explanations,
02:58making dictionaries and sets fun to learn.
03:01Ethan's our code expert,
03:02diving deep into every line to ensure you grasp the magic.
03:06Sophia's leading our demos with infectious enthusiasm,
03:10showing you how to wield dictionaries and sets for AI.
03:13Olivia's here with setup tips and questions to keep you on track.
03:17We're all dedicated to making you an AI coding wizard.
03:20Let's get started.
03:26Wizards' dictionaries are like enchanted vaults
03:29that store data as key value pairs,
03:32like Model Info,
03:33Wa,
03:34La,
03:34Name,
03:35AI Wizard Model,
03:36Accuracy,
03:370.85.
03:39They're perfect for organizing AI metadata,
03:42letting you access values instantly with keys.
03:45You'll see how dictionaries streamline AI projects in our demos.
03:50Anastasia,
03:51how do we create a dictionary,
03:52and why is it so useful for AI?
03:55A great question, Olivia.
03:56Use curly braces like ski,
03:59value to create one,
04:00and it's useful because it organizes complex AI data,
04:04like model parameters for quick access.
04:06We'll show you how to build and use dictionaries in our demos wizards.
04:11Sets are like magical filters that store only unique items,
04:15wizards like features.
04:170.5,
04:181.2,
04:192.8.
04:20They're perfect for AI tasks,
04:23like ensuring no duplicate data points in a data set.
04:26Sets are fast and efficient and will show their power in our demos.
04:31Exactly, Anastasia.
04:32Sets automatically remove duplicates,
04:35making them ideal for cleaning AI feature data before analysis.
04:39You'll learn to create sets with curly braces and use them with NumPy for AI.
04:44Get ready to filter data like a wizard.
04:47Wizards,
04:48dictionaries,
04:48Dictionaries and sets serve unique AI purposes.
04:51Dictionaries organize structured data,
04:54like model metadata,
04:55with fast key-based access,
04:58perfect for AI configurations.
05:00Sets ensure uniqueness,
05:01like filtering duplicate features,
05:03which is critical for clean AI data sets.
05:06Think of dictionaries as your AI organizer,
05:09storing key value pairs for quick lookups,
05:12like model accuracy.
05:14Sets are your data purifiers,
05:16ensuring no redundant data in AI tasks.
05:19Our demos will show you how to use both to power your AI projects.
05:23Wizards,
05:25creating a dictionary is simple.
05:27Use curly braces like model on info,
05:30Chizakalak name,
05:32AI wizard model,
05:34accuracy 0.85.
05:36You can store strings,
05:38numbers,
05:38or even lists as values,
05:40making dictionaries versatile for AI metadata.
05:43Try it in the Python shell or a .pi file,
05:46and we'll show you how in our demos.
05:50Olivia's right.
05:51Dictionaries are perfect for organizing,
05:53complex AI data,
05:55like model parameters,
05:56because keys make retrieval fast.
05:59Use descriptive keys like accuracy,
06:01to keep your code clear.
06:02Let's build dictionaries to power your AI projects, wizards.
06:07Wizards.
06:08Sets are created with curly braces,
06:10like features,
06:12dash to trace 0.5,
06:141.2,
06:152.8,
06:17and automatically remove duplicates.
06:20They're ideal for AI tasks,
06:21like ensuring unique feature values in a data set.
06:25Sets are fast and unordered,
06:27and we'll show their magic in our demos.
06:30Sets are super efficient for AI,
06:32wizards.
06:33If you add 0.5 twice to features,
06:35it only appears once.
06:37You can perform operations like unions to combine sets,
06:41which we'll demonstrate for AI data cleaning.
06:44Wizards,
06:44access dictionary values using keys,
06:47like model underscore info,
06:49name,
06:49to get AI wizard model.
06:52Keys act like labels,
06:53making data retrieval instant for AI tasks,
06:56such as fetching model accuracy.
06:58If a key doesn't exist,
07:00you'll get a key error,
07:01so we'll show you how to handle it.
07:03Ethan,
07:04what happens if I use a wrong key?
07:06How do I avoid errors?
07:08Great question,
07:09Olivia.
07:10Use model underscore info dot get.
07:12Key,
07:13default,
07:14to avoid key errors by returning a default value if the key's missing.
07:18This is super useful for AI data,
07:20and we'll demo it,
07:21wizards.
07:23Wizards,
07:24sets shine with operations like union,
07:27intersection,
07:28and difference.
07:28For example,
07:30features dot union new features,
07:33combines two sets,
07:35keeping only unique items,
07:37perfect for merging AI data sets.
07:40These operations are fast,
07:42and make data cleaning a breeze.
07:44In our demo,
07:45we'll use union,
07:46to combine feature sets for AI analysis.
07:50Sets also support add,
07:52to insert items and remove,
07:53to delete them.
07:55These tools are key for ensuring clean,
07:57unique data in AI projects,
07:59wizards.
08:00Wizards dictionaries are mutable,
08:02so you can update them.
08:04Add a new key value pair with model info accuracy,
08:07chart 0.85,
08:09or change an existing one like model info,
08:12epochs,
08:13chart of 200.
08:15This flexibility is perfect,
08:18for updating AI model metadata,
08:20in real time.
08:21You can also use update,
08:23to add multiple pairs at once,
08:25like model underscore info.
08:28Update,
08:28layers,
08:29three,
08:30neurons,
08:3064.
08:32Deleting keys with pop,
08:33or Dell,
08:34keeps your dictionary lean.
08:36We'll show these updates in our demo,
08:38wizards.
08:39Wizards,
08:40sets don't maintain order,
08:41which makes them unique.
08:43They focus on ensuring items are distinct,
08:46like 0.5,
08:471.2,
08:482.8,
08:50perfect for AI tasks,
08:51where duplicates could skew results.
08:54Their speed and simplicity,
08:55make sets a go-to,
08:56for data cleaning.
08:58Anastasia,
08:59why don't sets keep order,
09:01like lists?
09:02Great question,
09:03Olivia.
09:04Sets prioritize uniqueness,
09:05and speed over order,
09:07which is ideal for tasks,
09:08like filtering AI features.
09:10You'll see how sets shine,
09:12in our demo wizards.
09:14Wizards,
09:15dictionaries offer methods,
09:16like keys,
09:17values,
09:18and items,
09:18to access data.
09:20For example,
09:21model underscore info.
09:23Keys,
09:24returns all keys,
09:25like,
09:26name,
09:26accuracy.
09:28These methods are essential,
09:29for navigating AI metadata,
09:31efficiently in your projects.
09:33Methods make dictionaries,
09:34versatile for AI.
09:35Wizards,
09:36use items,
09:38to loop through key value pairs,
09:40or popitum,
09:41to remove the last pair.
09:43We'll demo keys,
09:44to show you how to extract data,
09:46like a pro.
09:48Navigate AI data easily.
09:54Wizards,
09:55it's demo time,
09:56and I'm so excited,
09:57to lead you through,
09:58two AI focused scripts.
10:00We'll use dictionaries,
10:01to manage model metadata,
10:03and sets to filter,
10:04unique features,
10:05both powered by number PY,
10:06for real AI magic.
10:08Get your Python setup ready,
10:10and let's make data organization fun.
10:14Sophia's demos,
10:15will show you dictionaries,
10:16and sets in action.
10:17Wizards,
10:18ensure Python,
10:19VS Code,
10:20and NumPy,
10:21and NumPy,
10:21are installed,
10:22from day 32,
10:23and create dictionaries.py,
10:25and sets.py.
10:27Ethan,
10:28and Olivia,
10:28will guide you,
10:29with code explanations,
10:30and tips.
10:31Let's code.
10:33Wizards,
10:33let's prep for our demos,
10:35to make them seamless.
10:37Open VS Code,
10:38create dictionaries.py,
10:40and sets.py,
10:41and save them,
10:42in a Python demo,
10:43folder.
10:44Run pip install NumPy,
10:45to ensure NumPy's ready,
10:47and I'll show you,
10:48how to run these scripts,
10:49like a pro.
10:51Sophia,
10:52what if wizards,
10:53forgot how to set up,
10:54NumPy,
10:54or their environment?
10:56Awesome question,
10:58Olivia.
10:59Run pip install NumPy,
11:01in a terminal,
11:01and activate,
11:02your virtual environment,
11:03with source my,
11:04and slash bin,
11:05slash activate,
11:06if used.
11:07This ensures,
11:08our AI demos,
11:09run smoothly,
11:10wizards.
11:11Get your demo ready.
11:14Wizards,
11:15our first demo,
11:16in dictionaries.py,
11:17creates a dictionary,
11:18to store AI model metadata,
11:20like name and accuracy.
11:23We'll add a new key,
11:24access values,
11:25and list keys,
11:26to show how dictionaries,
11:27organize data.
11:28This is your chance,
11:30to see AI data management,
11:31in action.
11:32Let's do it.
11:34The code is,
11:35model underscore info,
11:37equals,
11:38name,
11:39AI wizard model,
11:40learning underscore rate,
11:420.01,
11:44epics,
11:45100.
11:46We add,
11:46model underscore info,
11:48accuracy,
11:49equals 0.85,
11:51and print keys,
11:51with list,
11:52model underscore info,
11:54keys.
11:55This shows,
11:56how dictionaries,
11:57streamline AI metadata,
11:58wizards.
11:59Wizards,
12:00model underscore info,
12:02equals,
12:03name,
12:03AI wizard model,
12:04learning underscore rate,
12:060.01,
12:08creates a dictionary,
12:09with key value pairs,
12:10for AI metadata.
12:12Each key,
12:13like name,
12:14maps to a value,
12:15like,
12:16AI wizard model,
12:17for instant access.
12:18This structure,
12:19is perfect,
12:20for organizing,
12:21complex AI data efficiently.
12:23I love how dictionaries,
12:25make AI data,
12:25so accessible,
12:26Ethan.
12:28Wizards,
12:29you can store,
12:29any data type,
12:30as values,
12:31like numbers,
12:32or strings,
12:32and retrieve them,
12:33with keys.
12:35Try adding,
12:36your own key value pair,
12:37to see the magic.
12:39In print,
12:40model underscore info,
12:42name,
12:43we retrieve,
12:44AI wizard model,
12:45using the,
12:46name,
12:46key,
12:47perfect for AI tasks,
12:48like fetching model details.
12:51Model underscore info,
12:52accuracy,
12:53equals 0.85,
12:55adds a new key value pair,
12:57and list,
12:58model underscore info,
13:00keys,
13:00lists all keys.
13:02These operations,
13:03make dictionaries,
13:04a powerhouse,
13:05for AI data management.
13:07That's so cool,
13:08Ethan.
13:09Wizards,
13:10printing dictionary values,
13:12helps you check your AI data,
13:13and updating keys,
13:14lets you adapt,
13:15as projects evolve.
13:17Try printing,
13:18your own dictionary,
13:19to see it in action,
13:20it's like opening,
13:21a treasure vault.
13:24Wizards,
13:24let's run dictionaries.py.
13:27In VS code,
13:28open the terminal,
13:29with control plus,
13:30or CMD plus,
13:31type python3dictionaries.py,
13:34and hit enter.
13:35You'll see your model metadata,
13:37like name and accuracy,
13:39proving your AI data skills,
13:40are top notch.
13:42The output shows model info,
13:45name,
13:47AI wizard model,
13:49model name,
13:50AI wizard model,
13:52and more.
13:53It's like casting a spell,
13:56to reveal your AI data.
13:58Try it,
13:59and share your results,
14:00wizards.
14:02Wizards,
14:02our second demo in sets.py,
14:04uses sets to filter unique AI features,
14:07and combine them with number py.
14:09We'll create a set,
14:10merge it with another,
14:12and convert it to a number py array,
14:13for AI analysis.
14:15This is your chance,
14:17to master clean data,
14:18for AI projects.
14:19The code creates features equals,
14:230.5,
14:241.2,
14:250.5,
14:262.8,
14:27which removes duplicates,
14:29and unique underscore features equals features.
14:32Union,
14:33new underscore features,
14:34combines sets.
14:36We convert to a numpy array,
14:38with np.
14:39Array,
14:39list,
14:40unique underscore features,
14:42for AI math.
14:43It's pure AI magic,
14:45wizards.
14:46Wizards,
14:47features equals,
14:480.5,
14:491.2,
14:500.5,
14:522.8,
14:52creates a set,
14:53that keeps only unique values,
14:55like,
14:560.5,
14:571.2,
14:582.8.
14:59This is perfect,
15:00for AI to avoid,
15:01duplicate data points,
15:03in feature sets.
15:04Sets are unordered,
15:05but lightning fast,
15:06for data cleaning.
15:08I love how sets,
15:09simplify AI data,
15:10Ethan.
15:12Wizards,
15:12sets ensure your feature data,
15:14is clean and unique,
15:15ready for analysis.
15:17Try creating your own set,
15:19to see the duplicate,
15:20removing magic.
15:22In unique underscore features,
15:24equals features.
15:25Union,
15:26new underscore features.
15:28We combine two sets,
15:29to get all unique items,
15:31like,
15:310.5,
15:321.2,
15:342.8,
15:353.5.
15:36Operations like intersection,
15:38find common items,
15:39and difference,
15:40finds unique ones.
15:42These are key,
15:43for merging,
15:43for merging,
15:44or filtering AI data sets.
15:46That's so powerful,
15:47Ethan.
15:49Wizards,
15:50set operations,
15:50let you clean,
15:51and combine AI data,
15:52efficiently,
15:53like merging feature sets.
15:55Try Union,
15:56in our demo,
15:57to see how it streamlines,
15:58your AI workflow.
16:01Wizards,
16:02NP underscore,
16:03array equals,
16:04NP.
16:05Array,
16:05list,
16:06unique underscore features,
16:08converts a set,
16:08to a numpy array,
16:09for AI math,
16:10like,
16:110.5,
16:121.2,
16:142.8,
16:153.5.
16:16Arrays are optimized,
16:17for operations like,
16:18matrix calculations,
16:20in neural networks.
16:21This step,
16:22makes your set data,
16:23I ready.
16:24I love how sets,
16:25and number py,
16:26team up,
16:26Ethan.
16:28Wizards,
16:29converting sets,
16:29to arrays powers up,
16:30your AI models,
16:31with clean,
16:32unique data.
16:34Try it in our demo,
16:35to see the transformation.
16:36Wizards,
16:39let's run sets.py.
16:41In VS Code's terminal,
16:43type python3 sets.py,
16:45and hit enter.
16:47You'll see unique features,
16:48and a number py array,
16:50showing how sets,
16:51clean AI data like magic.
16:53The output shows,
16:55unique features,
16:560.5,
16:571.2,
16:592.8,
17:003.5,
17:01numpy array,
17:020.5,
17:041.2,
17:052.8,
17:053.5,
17:07and more.
17:08It's like casting a spell,
17:10to purify your AI data.
17:12Share your results,
17:13wizards.
17:14Wizards,
17:15you can loop through dictionaries,
17:17using for key,
17:17in model underscore info.
17:19To access keys,
17:20or for key,
17:21value in model underscore info.
17:23Items,
17:24for pairs.
17:25This is perfect,
17:26for processing AI metadata,
17:28like printing all model parameters.
17:30Iteration,
17:31makes dictionaries dynamic,
17:33for AI workflows.
17:34Looping is like flipping through your AI spellbook wizards.
17:38Use items,
17:39to process key value pairs,
17:41like model names and accuracies,
17:43in a loop.
17:45Try it in the Python shell,
17:46to see how it powers your AI projects.
17:54Wizards,
17:54no output from dictionaries py,
17:57check Python's path with Python 3 version,
18:00and ensure dictionaries.py is saved in your current folder.
18:03Use pwd to verify.
18:05Navigate with cdpython demo if needed,
18:08and your demo will run smoothly.
18:09For sets.py,
18:12a,
18:13module not found error,
18:14no module named numpy,
18:16means numpy's missing.
18:18Run pip install numpy.
18:20Verify your code matches ours,
18:22and check Python 3.11 or later.
18:25Drop errors in the comments.
18:27Wizards,
18:28we're here to help.
18:29Wizards,
18:30virtual environments are like magical bubbles for your projects.
18:33Create one with Python 3,
18:36mvenvmynv,
18:37and activate it.
18:39With source,
18:40myenvbin,
18:41activate on Mac Linux,
18:43or myenvju scripts,
18:46activate on Windows.
18:47This isolates numpy for sets.py,
18:50keeping your AI projects clean.
18:53Olivia,
18:54why are virtual environments crucial for AI?
18:57They prevent library conflicts,
18:59Anastasia,
19:00ensuring numpy and other tools
19:03work perfectly for each project.
19:05It's like a dedicated spell book for your AI code.
19:09Try it for your demos, wizards.
19:12Wizards,
19:13open the Python shell with Python 3
19:15and try info,
19:17yashtir,
19:17name,
19:18wizard,
19:19score,
19:2095,
19:21print,
19:22info.
19:23Add a key with info level,
19:25and two,
19:26and print again to see the update.
19:28The shell's a playground for testing dictionaries instantly.
19:30It's a fantastic way to experiment, wizards.
19:34Try print info name to get wizard or print info.keys for all keys.
19:40The shell preps you for day 36 control flow by letting you test dictionaries hands-on.
19:45Wizards,
19:47dictionaries,
19:47and sets power AI libraries.
19:50Dictionaries store model configurations in TensorFlow or metadata in Pandas,
19:54while sets ensure unique data points in NumPy arrays.
19:59These structures are your foundation for efficient AI data handling.
20:02You're already using sets with NumPy, wizards.
20:07That's a huge step.
20:08In day 36,
20:10control flow will build on this for smarter AI logic.
20:14Keep practicing dictionaries and sets to dominate AI coding.
20:17Wizards,
20:19save dictionaries.pi and sets.pi in VS code with Cotral plus S or CMD plus S.
20:26It's like locking your spells in a vault.
20:28Store them in a Python demo folder and back them up on GitHub or cloud storage.
20:33Your AI code is a treasure worth protecting.
20:36Share your scripts on GitHub, Discord, or here in the commons, wizards.
20:41Your dictionaries and sets are proof of your AI coding skills.
20:45Show them off.
20:46Saving ensures you're ready for day 36 control flow adventures.
20:55Wizards, here's your challenge.
20:57Create AI metadata.pi with a dictionary like model,
21:02wizardnet, accuracy, 0.9,
21:05and a set like 1.1, 2.2, 1.1.
21:09Add a key to the dictionary,
21:11merge the set with another,
21:12and print results with python3aimetadata.pi.
21:17Share your output in the comments or on Instagram.
21:20This is so exciting, wizards.
21:22Try adding layers.
21:24Three casts a door into your dictionary
21:26and printdike.keys.
21:28Show us your results on YouTube or Daily AI Wizard.
21:31It's like casting an AI spell.
21:34Prep for day 36 with this challenge.
21:37Wizards, hit subscribe, like this video,
21:40and share your AI metadata.py output in the comments.
21:44Got questions about dictionaries or sets?
21:48We're here to help you excel.
21:50Join our Discord or here to connect with wizards
21:52and boost your AI skills.
21:53Our community is a magical hub, wizards.
21:58Post your code, ask for tips,
22:00or share your wins on Discord, here, or Instagram,
22:04at halfdaliawizard.
22:06Subscribe for day 36's control flow,
22:09and let's code the future together.
22:11Wizards, day 36 is next.
22:15Control flow in Python.
22:17You'll learn to make decisions and loops in your code,
22:20like if statements for AI logic,
22:22building on your dictionary and set skills.
22:25Get ready for smarter AI programming.
22:28Control flow will make your AI code dynamic, wizards.
22:32Subscribe to catch day 36,
22:34and keep practicing dictionaries and sets.
22:37It's another step toward mastering Python for AI.
22:40Wizards, you've conquered dictionaries and sets.
22:43Huge congratulations.
22:45Your dictionaries.pi and sets.pi demos
22:49prove you're a pro at organizing AI data.
22:52Keep practicing and get excited for day 36's control flow
22:55to make your code even smarter.
22:57I'm incredibly proud of you, wizards.
23:00You've mastered dictionaries for structured AI metadata
23:02and sets for clean, unique data,
23:05skills that power real-world AI projects
23:07like neural networks and data pipelines.
23:11Share your AI metadata.py output
23:14in the comments or on Instagram,
23:16arvdailyaiwizard, to show off your magic.
23:20Subscribe, hit the bell, and join our Discord.
23:22Or here to connect with other wizards,
23:25ask questions, and share insights.
23:28Day 36's control flow will add logic to your AI code,
23:31so keep your Python setup ready.
23:33You're shaping the future of AI, wizards.
23:36Keep shining, and let's make more coding magic together.
23:40You nailed dictionaries and sets, wizards.
23:43Your AI data skills are top tier,
23:45and I loved breaking down the code for you.
23:48Get ready for day 36's control flow adventures.
23:51Wizards, you're absolutely phenomenal.
23:55Leading these demos was a blast,
23:57and seeing you master dictionaries and sets
23:59is pure AI magic.
24:01Share your AI underscore metadata dot py results
24:04with it daily AI wizard on Instagram
24:06or in the comments.
24:07I can't wait to see your work.
24:10Subscribe for day 30 SIXS control flow lesson,
24:13join our Discord or X community,
24:15and keep coding with confidence.
24:17You're true wizards,
24:19building the AI future one line at a time.
24:22Let's code the future, wizards.
24:24You crushed dictionaries and sets, wizards.
24:29Your AI skills are soaring,
24:31and I'm thrilled to be part of your journey.
24:34Let's dive into day 36 together.

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