- 2 days ago
Wizards, join Anastasia, Isabella, Ethan, Sophia, and Olivia for Day 35 of the DailyAIWizard Python for AI series! 🚀 Learn how to use dictionaries to organize AI metadata like {'model': 'WizardNet'} and sets to filter unique data for AI projects. Sophia leads two demos with NumPy, Ethan explains every line, and Olivia adds tips. Perfect for beginners building on Day 34! 💻 Get ready for Day 36: Control Flow in Python. Subscribe, like, and share your ai_metadata.py output in the comments! Join our Discord, X, or Instagram (@DailyAIWizard) for more tips! Code the Future, Wizards!
🔗 Links:
Python: python.org
VS Code: code.visualstudio.com
Website: dailyaiwizard.com
Discord: discord.com/channels/1397945816349675600/1397945819260391521
X: x.com/dailyaiwizard
Instagram: www.instagram.com/dailyaiwizard
Hashtags:
#Python #LearnPython #PythonForAI #AICoding #PythonTutorial #CodingForBeginners #PythonDictionaries #PythonSets #AIProgramming #TechTutorial #DailyAIWizard #CodeTheFuture
Tags:
Python, Learn Python, Python for AI, AI Coding, Python Tutorial, Coding for Beginners, Python Dictionaries, Python Sets, 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
Website: dailyaiwizard.com
Discord: discord.com/channels/1397945816349675600/1397945819260391521
X: x.com/dailyaiwizard
Instagram: www.instagram.com/dailyaiwizard
Hashtags:
#Python #LearnPython #PythonForAI #AICoding #PythonTutorial #CodingForBeginners #PythonDictionaries #PythonSets #AIProgramming #TechTutorial #DailyAIWizard #CodeTheFuture
Tags:
Python, Learn Python, Python for AI, AI Coding, Python Tutorial, Coding for Beginners, Python Dictionaries, Python Sets, 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 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.