Wizards, join Anastasia, Isabella, Ethan, Sophia, and Olivia for Day 37 of the DailyAIWizard Python for AI series! 🚀 Learn functions—def, parameters, and return statements—to create reusable, modular AI code. Sophia leads two demos with NumPy, Ethan explains every line, and Olivia adds tips. Perfect for beginners building on Day 36! 💻 Get ready for Day 38: Python Modules and Libraries. Subscribe, like, and share your ai_functions.py output in the comments! Join our Discord, X, or Instagram (@DailyAIWizard) for more tips! Code the Future, Wizards!
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Python, Learn Python, Python for AI, AI Coding, Python Tutorial, Coding for Beginners, Python Functions, 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
#PythonForAI #LearnPython #AICoding #DailyAIWizard
Hashtags:
#Python #LearnPython #PythonForAI #AICoding #PythonTutorial #CodingForBeginners #PythonFunctions #AIProgramming #TechTutorial #DailyAIWizard #CodeTheFuture
Tags:
Python, Learn Python, Python for AI, AI Coding, Python Tutorial, Coding for Beginners, Python Functions, 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 37 of the Daily AI Wizard Python for AI series, and I'm thrilled to dive into functions.
00:11After mastering control flow in Day 36, today we'll learn how to create reusable code blocks to make your AI programs modular and powerful.
00:20This epic journey will spark your coding passion with demos and challenges. Let's code the future, Wizards.
00:27Hey, Wizards. I'm Isabella, and I'm so excited to explore functions with you.
00:33Functions, like Def Evaluate Model, let you reuse code for AI tasks, such as processing data or evaluating models.
00:43Join us for vibrant demos and a challenge that'll make your Python skills shine.
00:49Ethan here, Wizards. I'll break down function code, showing how they streamline AI workflows, like model evaluation or data processing.
00:58Functions are your key to clean, reusable AI code, and I'm thrilled to explain every detail.
01:04Get ready to code like true wizards.
01:06Sophia here.
01:08I'm pumped to lead our demos, showing functions in action for AI projects.
01:13Let's make coding magic happen.
01:16Olivia here.
01:18I'll share tips and ask questions to keep your learning smooth and fun.
01:23Let's master functions, Wizards.
01:30Wizards' functions are like reusable spells that make your AI code efficient and organized.
01:35With Def Process features, you can process data once and reuse it across AI projects, like filtering data sets.
01:44They save time and make your code clean, and we'll show you how in our demos.
01:48Absolutely, Anastasia.
01:51Functions bundle control flow and logic into reusable blocks, perfect for AI tasks, like evaluating models or transforming data.
02:01They build on Day 36's skills and prepare you for Day 38's modules.
02:06Get ready to streamline your AI code, Wizards.
02:09Wizards, today we're unlocking functions, def, parameters, and return statements to create modular AI code.
02:18You'll learn to define functions, pass data, and use them with NumPy for AI tasks.
02:23Our challenge will ignite your creativity, so let's dive into this magical coding journey.
02:29Ophia's leading our demos with vibrant energy.
02:32Ethan's explaining every line.
02:34And Olivia's adding tips to keep you on track.
02:37By the end, you'll write reusable AI code like a wizard, ready for Day 38's modules.
02:45This is your chance to make Python shine.
02:48Wizards, meet your Day 37 dream team.
02:51Anastasia and I are here to guide you with clear, engaging explanations, making functions fun and accessible.
02:58Ethan's our code expert, diving deep into function logic to ensure you grasp every detail.
03:04Sophia's leading our demos with infectious enthusiasm.
03:07Showing you functions in action for AI projects.
03:10Olivia's here with setup tips and questions to keep your learning smooth.
03:14We're all dedicated to making you an AI coding wizard.
03:18Let's get started.
03:18Let's get started.
03:24Wizards, functions are like magical spells you define once and use anywhere.
03:28They bundle code for tasks like model evaluation, making AI programs modular and reusable.
03:39You'll see how functions simplify AI coding in our demos.
03:43Anastasia, how do functions make AI coding easier?
03:47Can you give an example?
03:48Great question, Olivia.
03:51Functions like def process drawn features.
03:54Data.
03:55Let you reuse data processing logic across AI projects without rewriting code.
04:00They save time and reduce errors.
04:02And we'll show you how in our demos, wizards.
04:06Wizards define functions with def function name and add code inside, like print, hello AI.
04:12Functions can take parameters like accuracy to process AI data dynamically.
04:18You'll learn to create functions in our demo to evaluate models.
04:22Functions are like custom AI tools, wizards.
04:26Write def evaluate model accuracy to check model performance once and reuse it anywhere.
04:34Get ready to craft functions that make your AI code powerful and clean.
04:38Wizards, parameters like accuracy and def evaluate model accuracy.
04:44Let functions process data.
04:47Arguments like 0.85 are the values you pass when calling evaluate model year 0.85.
04:54This makes functions flexible for AI tasks like evaluating models.
04:59Parameters are like placeholders for AI data wizards.
05:02You can pass multiple arguments like features and threshold to process data sets.
05:07We'll show you how to use parameters in our demo to make AI code dynamic.
05:13Wizards, return statements send results back from functions, like return, excellent, in evaluate underscore model.
05:20This lets you store or use the output, like result equals evaluate underscore model, 0.85, for AI logic.
05:29Return values are key for modular AI workflows.
05:32Ethan, why is return better than print, for AI functions?
05:38Great question, Olivia.
05:40Return lets you use the output in other parts of your AI code, like saving result for further processing, while print, just displays it.
05:47We'll demo return to show its power, wizards.
05:50Wizards, default parameters let functions work without arguments, like def process features, features threshold to 1.0.
05:59If you call process features, data, it uses threshold 1.0 automatically.
06:05This is perfect for AI functions with optional settings.
06:08Defaults make functions user-friendly wizards.
06:12In AI, you can set default thresholds for data processing, making your code flexible.
06:18We'll use defaults in our demo to simplify AI workflows.
06:22Wizards, functions are the backbone of AI code.
06:26Use def evaluate model to check model performance or def process, SHA's features, to pre-process data for neural networks.
06:34They make your AI code reusable and organized, and we'll show you how in our demos.
06:40In real AI projects, functions handle tasks like data cleaning or model evaluation, used in libraries like Scikit-learn.
06:48Your Day 37 skills are industry-relevant, wizards.
06:52Get ready to apply functions in Day 38's modules.
06:55Wizards, it's demo time, and I'm thrilled to lead you through two AI-focused function scripts.
07:06We'll use functions to evaluate models and process data with number PY, making AI code modular.
07:13Get your Python setup ready, and let's make functions shine.
07:16Sophia's demos will show you functions in action, wizards.
07:21Ensure Python, VS Code, and NumPy are set up from Day 32, and create function demo.pi and AIprocessing.pi.
07:31Ethan and Olivia will guide with code explanations and tips.
07:34Let's code.
07:36Wizards, let's prep for our demos to make them seamless.
07:39Open VS Code, create function underscore demo.py and AI underscore processing.py, and save them in a Python demo folder.
07:49Run pip install NumPy to ensure NumPy's ready, and I'll guide you to run these scripts like a pro.
07:55Sophia, what if wizards forgot how to set up NumPy or their environment?
08:01Awesome question, Olivia.
08:03Run pip install NumPy in a terminal, and activate your virtual environment with source myinv slash bin slash activate on Mac slash Linux or myinv backslash scripts backslash activate on Windows.
08:15This ensures our AI demos run smoothly, wizards.
08:20Wizards, our first demo in function underscore demo dot PY creates a function to evaluate AI model accuracy.
08:26We'll define evaluate underscore model accuracy to return excellent, good, or needs improvement based on thresholds.
08:35Let's run this script and see modular AI logic in action.
08:40The code defines def evaluate underscore model accuracy with if statements and calls it with evaluate underscore model 0.85.
08:49It returns, good, and prints, model evaluation, good.
08:54This shows how functions make AI evaluation reusable, wizards.
09:00Wizards, def evaluate underscore model, accuracy, defines a function with a parameter accuracy for AI evaluation.
09:08The function body uses if statements to return a result, like, good.
09:12This modular design lets you reuse evaluation logic across AI projects.
09:17I love how functions organize AI code, Ethan.
09:20Wizards, you can define any logic inside a function, like, model checks, and call it any time.
09:28Try defining your own function to see the magic.
09:32In return, good.
09:34The function sends back a result for use elsewhere, like, result equals evaluate underscore model 0.85.
09:41This is perfect for AI, where you need outputs for further processing, like, logging model performance.
09:48Return makes functions powerful and flexible.
09:51Return statements are so cool, Ethan.
09:54Wizards, you can store result for analysis or pass it to other functions.
10:00Try returning a different message in your function to see it work.
10:03Wizards, our second demo in AI underscore processing.py, creates a function to process AI data with number py.
10:12We'll define process underscore features, features, threshold, to filter high features and return a number py array.
10:19This is your chance to see functions streamline AI workflows.
10:23The code defines def process underscore features, features, threshold equals 1.0, to filter features above threshold and convert to np, array.
10:35We call it with process underscore features, data, 2.0, to get high features.
10:41This shows functions powering AI data tasks, wizards.
10:45Wizards, def process underscore features, features, threshold equals 1.0,
10:50uses features as a list and threshold as a default parameter.
10:54The function filters features above threshold using a list comprehension.
10:58This makes functions flexible for AI data processing.
11:02Parameters are so powerful, Ethan.
11:05Wizards, you can pass any data, like lists or numbers, to customize function behavior.
11:12Try changing threshold to see different results.
11:16In return np, array, high underscore features.
11:19We convert a filtered list to a numpy array for AI math, like, 2.8, 3.5.
11:26Arrays are optimized for operations like matrix calculations in neural networks.
11:31This makes functions eye-ready, wizards.
11:34I love how functions and number py team up, Ethan.
11:38Wizards, returning arrays lets you process data for AI models efficiently.
11:43Try it in our demo to see the transformation.
11:46Wizards, let's run function underscore demo dot py.
11:51In VS Code, open the terminal with control plus or cmd plus, type python3 function underscore demo dot py and hit enter.
12:00You'll see the model evaluation, like model evaluation, good, showing your AI function skills.
12:07The output shows model evaluation, good for accuracy.
12:130.85.
12:15It's like casting a modular AI spell.
12:18Try it and share your results, wizards.
12:21Wizards, now let's run AI underscore processing dot py.
12:25In VS Code's terminal, type python3ai underscore processing dot py and hit enter.
12:32You'll see high features as a number py array, proving your AI data processing skills.
12:39The output shows high features, 2.8, 3.5.
12:43And feature count, 2.
12:45It's like weaving AI data magic.
12:48Share your output, wizards.
12:50Wizards, scope defines where variables are accessible.
12:54Variables inside a function, like high underscore features and process underscore features, are local and only exist inside.
13:02Global variables outside functions can be used but need careful handling in AI code.
13:07Scope keeps your AI code organized, wizards.
13:11Local variables prevent conflicts, like reusing result in different functions.
13:16Try printing a local variable outside a function to see scope in action.
13:21Wizards, keyword arguments let you specify parameter names like process, talk features, data, threshold, 2.0.
13:30This makes function calls clear and flexible, especially for AI functions with multiple parameters.
13:37It's like labeling your AI spells for clarity.
13:39You can call process underscore features, threshold equals 2.0, features equals data, in any order with keywords.
13:48This is great for AI functions with optional settings, like thresholds.
13:53We'll use keyword args in our demo, wizards.
13:56Wizards, no output from function demo.py.
14:05Check Python's path with Python 3, version and ensure the files in your current folder.
14:11Use pwd to verify.
14:13Navigate with cdpython demo if needed, and your demo will run smoothly.
14:17For i underscore processing dot py, a module not found error.
14:23No module named numpy means numpy's missing.
14:27Run pip install numpy.
14:29Verify your code matches ours and use Python 3.11 or later.
14:34Drop errors in the comments, wizards, we'll help.
14:37Wizards.
14:38Virtual environments are like magical bubbles for your AI projects.
14:41Create one with Python 3, mm, venvy, myenvy, and activate it with source, myenvy, chao, bin.
14:50Activate on Mac Linux, or myenvyju scripts.
14:54Activate on Windows.
14:55This isolates numpy for AI processing dot py, keeping your projects clean.
15:01Olivia, why are virtual environments crucial for AI coding?
15:05They prevent library conflicts, Anastasia.
15:08Ensuring numpy and other tools work perfectly for each project.
15:13It's like a dedicated spellbook for your AI code.
15:17Try it for your demos, wizards.
15:20Wizards.
15:21Open the Python shell with Python 3 and try def greet name, return of hello, name, print greet wizard.
15:29Call the function with different names to see it work instantly.
15:32The shell's a playground for testing functions.
15:35It's perfect for experimenting, wizards.
15:37Try def add xyzary1, return x plus y, print add 5, to test default parameters.
15:47The shell preps you for day 38's modules by mastering functions hands-on.
15:56Wizards.
15:57Functions are central to AI libraries like NumPy and TensorFlow.
16:01Define functions to pre-process data in NumPy or evaluate models in scikit-learn, as in our demos.
16:09These skills make your AI code reusable and industry-ready.
16:13You're already using functions with NumPy wizards.
16:16That's a huge step.
16:18In day 38, modules will build on functions for larger AI projects.
16:23Keep practicing to dominate AI coding.
16:26Wizards, Lambda functions are quick, one-line functions, like Lambda x, x greater than 1 for filtering data.
16:35They're perfect for AI tasks, like passing functions to NumPy's filter.
16:39We'll show Lambda functions in the shell for fast AI coding.
16:43Lambda functions are like instant AI spells wizards.
16:47Use Lambda x, x2, to transform data quickly.
16:52Try them in our challenge to see their power.
16:55Wizards, clean functions are readable and efficient.
16:59Use clear names like Evaluate Model, add doc strings like Evaluate AI Model Accuracy,
17:05and keep functions short for AI tasks.
17:08This makes your code a joy to use and share.
17:10Great tips, Anastasia.
17:13In AI, clean functions improve collaboration and reduce bugs.
17:18Practice these habits to prep for day 38's modules, wizards.
17:23Wizards, combine functions with day 36's control flow.
17:27Use if inside def Evaluate underscore model to check conditions or loops in process underscore features to filter data.
17:34This creates powerful, reusable AI logic.
17:38It's like combining spells, wizards.
17:39Functions with if or for make your AI code modular and dynamic.
17:44Try combining them in our challenge to see the magic.
17:48Wizards, save function.demo.pi and ai.processing.pi in VS code with Cottrell Plus S or CMD Plus S.
17:57It's like sealing your spells in a vault.
17:59Store them in a Python demo folder and back them up on GitHub or cloud storage.
18:04Your AI code is a treasure worth protecting.
18:06Share your scripts on GitHub, Discord or ex-wizards.
18:11Your function code is proof of your AI skills.
18:13Show it off.
18:14Saving ensures you're ready for day 38's modules.
18:361.1, 2.2, 3.3.
18:39Print results with python3aifunctions.pi and share your output in the comments or on Instagram.
18:45This is so exciting, wizards.
18:49Try using return in your function and call it with different thresholds.
18:53Show us your results on YouTube or Daily AI Wizard.
18:56It's like casting an AI spell.
18:59Prep for day 38 with this challenge.
19:01Wizards, hit subscribe, like this video, and share your AI line functions.py output in the comments.
19:09Got questions about functions?
19:11We're here to help you excel.
19:13Join our Discord or ex to connect with wizards and boost your AI skills.
19:17Our community is a magical hub, wizards.
19:21Post your code, ask for tips, or share your wins on Discord, ex, or Instagram.
19:27Haas Daily AI Wizard.
19:29Subscribe for day 38's modules and let's code the future together.
19:35Wizards, day 38 is coming.
19:37Python modules and libraries.
19:39You'll learn to use import and libraries like NumPy to supercharge your AI code,
19:44building on your function skills.
19:45Get ready for powerful, reusable AI programming.
19:50Modules will make your AI code even more powerful, wizards.
19:54Subscribe to catch day 38 and keep practicing functions.
19:58It's another step toward mastering Python for AI.
20:06Wizards, functions power real AI projects.
20:09Use def pre-processed chunk data to clean data sets or def predict
20:13for model predictions in machine learning.
20:16These skills are used in tools like TensorFlow, and you're learning them now.
20:21From chatbots to image recognition, functions drive AI logic and data handling.
20:27Your day 37 skills are industry-relevant wizards.
20:30Wizards, keep practicing to shine in day 38's modules.
20:34Wizards, combine functions with day 34 minus 35's lists and dictionaries.
20:39Use def process underscore dict, data, to loop through key value pairs or def filter underscore list,
20:46data, to process lists.
20:48This creates modular AI logic for data tasks.
20:52Combining these is like weaving powerful AI spells, wizards.
20:56Functions with dictionaries process model metadata and lists handle data sets.
21:01Try it in our challenge to see the magic.
21:04Wizards, debug functions by printing parameters, like print, accuracy, inside evaluate underscore model.
21:11Check return values with print, result, and ensure arguments match parameters.
21:17This keeps your AI functions error-free.
21:20Debugging is like perfecting your AI spells, wizards.
21:23Use print to trace values or VS Code's debugger to step through functions.
21:28Share issues in the comments and will help.
21:31Wizards, optimize functions with single-purpose designs and clear names.
21:36For example, def pre-process out data should only clean data, not evaluate models.
21:43Use doc strings and avoid global variables for clean AI code.
21:47Optimization makes AI functions efficient, wizards.
21:51Single-purpose functions are easier to test and reuse, prepping you for day 38's modules.
21:57Keep practicing clean code habits.
22:01Wizards' functions are critical in AI pipelines.
22:03Use def-clean data to pre-process data sets or def-evaluate to check model performance in a pipeline.
22:11These modular blocks power real-world AI, like recommendation systems.
22:17Your day.
22:1837 skills drive AI workflows, from data prep to model evaluation.
22:25Wizards.
22:26Practice functions to excel in AI pipelines.
22:29Day 38's modules will make these reusable across projects.
22:34Wizards, for an extra challenge, update AI functions.pi to include a function def scale, are features
22:41data, factored 2.0, that multiplies each value in data.
22:46Use it with data, tracks 1.1, 2.2, 3.3, and share your output in the comments.
22:52This challenge is epic, wizards.
22:55Try adding a docstring and default parameter to your function.
22:59Show us your results on YouTube or a daily AI wizard.
23:02It's like mastering an AI spellbook.
23:05Wizards, you've conquered functions.
23:07Huge congratulations.
23:08Your function, demo.pi and AIprocessing.pi, prove you can create modular AI code.
23:15Keep practicing and get excited for Day 38's modules to supercharge your projects.
23:19I'm incredibly proud of you, wizards.
23:22You've mastered functions, creating reusable code for AI tasks like model evaluation and
23:28data processing.
23:29Skills that power real-world AI projects, from neural networks to data pipelines.
23:34Share your AI functions.pi output in the comments or on Instagram, i.dailyaiwizard, to show off
23:43your magic.
23:44Subscribe, hit the bell, and join our Discord or X to connect with other wizards, ask questions,
23:50and share insights.
23:51Day 38's modules and libraries will take your AI code to new heights, so keep your Python
23:56setup ready.
23:58You're shaping the future of AI with every function.
24:00Keep shining and let's make more coding magic together, wizards.
24:05You nailed functions, wizards.
24:07Your modular AI skills are phenomenal, and I loved explaining the code.
24:11Get ready for Day 38's modules to build bigger projects.
24:15Wizards, you're absolutely incredible.
24:18Leading these demos was a blast, and seeing you master functions is pure AI magic.
24:23Share your AI underscore functions.py results with that daily AI wizard on Instagram or in
24:28the comments, I can't wait to see your work.
24:31Subscribe for Day 38's modules and libraries, join our Discord or X community, and keep coding
24:36with confidence.
24:37You're true wizards, building the AI future one function at a time.
24:42Let's code the future, wizards.
24:44You crushed functions, wizards.
24:47Your AI skills are soaring, and I'm thrilled to be part of your journey.
24:52Let's dive into Day 38's modules together.
24:55Let's go.
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