#Supervised and Unsupervised Learning

[eliodion]

Supervised and Unsupervised Learning

Supervised and Unsupervised Learning are two primary approaches in machine learning, each used for different types of tasks. Here’s a breakdown of their differences:

Definition and Purpose

Supervised Learning: In supervised learning, the model is trained on labeled data, meaning each input is paired with a correct output. The goal is to learn the mapping between inputs and outputs so that the model can predict the output for new, unseen inputs. Example: Predicting house prices based on features like size, location, and number of bedrooms (where historical prices are known). Unsupervised Learning: In unsupervised learning, the model is given data without labeled responses. Instead, it tries to find patterns or structure in the data. The goal is often to explore data, find groups (clustering), or detect outliers. Example: Grouping customers into segments based on purchasing behavior without predefined categories.

Types of Problems Addressed Supervised Learning: Classification: Categorizing data into classes (e.g., spam vs. not spam in emails). Regression: Predicting continuous values (e.g., stock prices or temperature). Unsupervised Learning: Clustering: Grouping similar data points (e.g., market segmentation). Association: Finding associations or relationships between variables (e.g., market basket analysis in retail). Dimensionality Reduction: Reducing the number of features while retaining essential information (e.g., principal component analysis for visualizing data in 2D).

Example Algorithms - Supervised Learning Algorithms: Linear Regression Logistic Regression Decision Trees and Random Forests Support Vector Machines (SVM) Neural Networks (when trained with labeled data) Unsupervised Learning Algorithms: K-Means Clustering Hierarchical Clustering Principal Component Analysis (PCA) Association Rule Mining (like the Apriori algorithm)

Training Data Requirements Supervised Learning: Requires a labeled dataset, which can be costly and time-consuming to collect and label. Unsupervised Learning: Works with unlabeled data, which is often more readily available, but the insights are less straightforward without predefined labels.

Evaluation Metrics Supervised Learning: Can be evaluated with standard metrics like accuracy, precision, recall, F1 score (for classification), and mean squared error (for regression), since we have labeled outputs. Unsupervised Learning: Harder to evaluate directly. Techniques like silhouette score or Davies–Bouldin index (for clustering) are used, or qualitative analysis may be required.

Use Cases Supervised Learning: Fraud detection, email classification, medical diagnosis, sales forecasting, and image recognition. Unsupervised Learning: Customer segmentation, anomaly detection, topic modeling, and data compression. 

In summary:

Supervised learning requires labeled data and is primarily used for prediction or classification tasks where the outcome is known. Unsupervised learning doesn’t require labeled data and is mainly used for data exploration, clustering, and finding patterns where the outcome is not predefined.

[newsbirdeye]

Understanding Little-Known Supervised and Unsupervised Learning Algorithms

Introduction IntroductionSupervised Learning AlgorithmsDefinition and OverviewTypes of Supervised Learning AlgorithmsApplications of Supervised LearningUnsupervised Learning AlgorithmsDefinition and OverviewTypes of Unsupervised Learning AlgorithmsApplications of Unsupervised LearningKey Differences Between Supervised and Unsupervised LearningChoosing the Right AlgorithmConclusion In the realm…

[a-polite-melody]

I bet the UK public would be in an uproar if they tried, unfortunately. It’s still the dominant attitude over there that cats Need to roam Freely, unsupervised, to be fully enriched.

the only political candidate who really matters

[mitsde123]

How to Choose the Right Machine Learning Course for Your Career

As the demand for machine learning professionals continues to surge, choosing the right machine learning course has become crucial for anyone looking to build a successful career in this field. With countless options available, from free online courses to intensive boot camps and advanced degrees, making the right choice can be overwhelming. 

[juliebowie]

Supervised Learning Vs Unsupervised Learning in Machine Learning

Summary: Supervised learning uses labeled data for predictive tasks, while unsupervised learning explores patterns in unlabeled data. Both methods have unique strengths and applications, making them essential in various machine learning scenarios.

Introduction

Machine learning is a branch of artificial intelligence that focuses on building systems capable of learning from data. In this blog, we explore two fundamental types: supervised learning and unsupervised learning. Understanding the differences between these approaches is crucial for selecting the right method for various applications. 

Supervised learning vs unsupervised learning involves contrasting their use of labeled data and the types of problems they solve. This blog aims to provide a clear comparison, highlight their advantages and disadvantages, and guide you in choosing the appropriate technique for your specific needs.

What is Supervised Learning?

Supervised learning is a machine learning approach where a model is trained on labeled data. In this context, labeled data means that each training example comes with an input-output pair. 

The model learns to map inputs to the correct outputs based on this training. The goal of supervised learning is to enable the model to make accurate predictions or classifications on new, unseen data.

Key Characteristics and Features

Supervised learning has several defining characteristics:

Labeled Data: The model is trained using data that includes both the input features and the corresponding output labels.

Training Process: The algorithm iteratively adjusts its parameters to minimize the difference between its predictions and the actual labels.

Predictive Accuracy: The success of a supervised learning model is measured by its ability to predict the correct label for new, unseen data.

Types of Supervised Learning Algorithms

There are two primary types of supervised learning algorithms:

Regression: This type of algorithm is used when the output is a continuous value. For example, predicting house prices based on features like location, size, and age. Common algorithms include linear regression, decision trees, and support vector regression.

Classification: Classification algorithms are used when the output is a discrete label. These algorithms are designed to categorize data into predefined classes. For instance, spam detection in emails, where the output is either "spam" or "not spam." Popular classification algorithms include logistic regression, k-nearest neighbors, and support vector machines.

Examples of Supervised Learning Applications

Supervised learning is widely used in various fields:

Image Recognition: Identifying objects or people in images, such as facial recognition systems.

Natural Language Processing (NLP): Sentiment analysis, where the model classifies the sentiment of text as positive, negative, or neutral.

Medical Diagnosis: Predicting diseases based on patient data, like classifying whether a tumor is malignant or benign.

Supervised learning is essential for tasks that require accurate predictions or classifications, making it a cornerstone of many machine learning applications.

What is Unsupervised Learning?

Unsupervised learning is a type of machine learning where the algorithm learns patterns from unlabelled data. Unlike supervised learning, there is no target or outcome variable to guide the learning process. Instead, the algorithm identifies underlying structures within the data, allowing it to make sense of the data's hidden patterns and relationships without prior knowledge.

Key Characteristics and Features

Unsupervised learning is characterized by its ability to work with unlabelled data, making it valuable in scenarios where labeling data is impractical or expensive. The primary goal is to explore the data and discover patterns, groupings, or associations. 

Unsupervised learning can handle a wide variety of data types and is often used for exploratory data analysis. It helps in reducing data dimensionality and improving data visualization, making complex datasets easier to understand and analyze.

Types of Unsupervised Learning Algorithms

Clustering: Clustering algorithms group similar data points together based on their features. Popular clustering techniques include K-means, hierarchical clustering, and DBSCAN. These methods are used to identify natural groupings in data, such as customer segments in marketing.

Association: Association algorithms find rules that describe relationships between variables in large datasets. The most well-known association algorithm is the Apriori algorithm, often used for market basket analysis to discover patterns in consumer purchase behavior.

Dimensionality Reduction: Techniques like Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) reduce the number of features in a dataset while retaining its essential information. This helps in simplifying models and reducing computational costs.

Examples of Unsupervised Learning Applications

Unsupervised learning is widely used in various fields. In marketing, it segments customers based on purchasing behavior, allowing personalized marketing strategies. In biology, it helps in clustering genes with similar expression patterns, aiding in the understanding of genetic functions. 

Additionally, unsupervised learning is used in anomaly detection, where it identifies unusual patterns in data that could indicate fraud or errors.

This approach's flexibility and exploratory nature make unsupervised learning a powerful tool in data science and machine learning.

Advantages and Disadvantages

Understanding the strengths and weaknesses of both supervised and unsupervised learning is crucial for selecting the right approach for a given task. Each method offers unique benefits and challenges, making them suitable for different types of data and objectives.

Supervised Learning

Pros: Supervised learning offers high accuracy and interpretability, making it a preferred choice for many applications. It involves training a model using labeled data, where the desired output is known. This enables the model to learn the mapping from input to output, which is crucial for tasks like classification and regression. 

The interpretability of supervised models, especially simpler ones like decision trees, allows for better understanding and trust in the results. Additionally, supervised learning models can be highly efficient, especially when dealing with structured data and clearly defined outcomes.

Cons: One significant drawback of supervised learning is the requirement for labeled data. Gathering and labeling data can be time-consuming and expensive, especially for large datasets. 

Moreover, supervised models are prone to overfitting, where the model performs well on training data but fails to generalize to new, unseen data. This occurs when the model becomes too complex and starts learning noise or irrelevant patterns in the training data. Overfitting can lead to poor model performance and reduced predictive accuracy.

Unsupervised Learning

Pros: Unsupervised learning does not require labeled data, making it a valuable tool for exploratory data analysis. It is particularly useful in scenarios where the goal is to discover hidden patterns or groupings within data, such as clustering similar items or identifying associations. 

This approach can reveal insights that may not be apparent through supervised learning methods. Unsupervised learning is often used in market segmentation, customer profiling, and anomaly detection.

Cons: However, unsupervised learning typically offers less accuracy compared to supervised learning, as there is no guidance from labeled data. Evaluating the results of unsupervised learning can also be challenging, as there is no clear metric to measure the quality of the output. 

The lack of labeled data means that interpreting the results requires more effort and domain expertise, making it difficult to assess the effectiveness of the model.

Frequently Asked Questions

What is the main difference between supervised learning and unsupervised learning? 

Supervised learning uses labeled data to train models, allowing them to predict outcomes based on input data. Unsupervised learning, on the other hand, works with unlabeled data to discover patterns and relationships without predefined outputs.

Which is better for clustering tasks: supervised or unsupervised learning? 

Unsupervised learning is better suited for clustering tasks because it can identify and group similar data points without predefined labels. Techniques like K-means and hierarchical clustering are commonly used for such purposes.

Can supervised learning be used for anomaly detection? 

Yes, supervised learning can be used for anomaly detection, particularly when labeled data is available. However, unsupervised learning is often preferred in cases where anomalies are not predefined, allowing the model to identify unusual patterns autonomously.

Conclusion

Supervised learning and unsupervised learning are fundamental approaches in machine learning, each with distinct advantages and limitations. Supervised learning excels in predictive accuracy with labeled data, making it ideal for tasks like classification and regression. 

Unsupervised learning, meanwhile, uncovers hidden patterns in unlabeled data, offering valuable insights in clustering and association tasks. Choosing the right method depends on the nature of the data and the specific objectives.

[edsonjnovaes]

Curso de Inteligência Artificial para todos - Aula 1

Curso de Inteligência Artificial para todos – Aula 1. Diogo Cortiz – 2020 23 mar Este primeiro vídeo é para discutir o panorama de IA e as principais abordagens existentes. Vou apresentar a história da inteligência artificial e a sopa de letrinhas que confunde muita gente: ia, machine learning, deep learning. Também explico as principais abordagens de aprendizado e treinamento: aprendizado…

[softlabsgroup05]

Discover the fundamentals of Machine Learning algorithms through our comprehensive guide. This simplified overview breaks down the essential principles behind ML algorithms, making it easier to grasp their concepts and applications. Perfect for anyone eager to delve into the world of artificial intelligence. Stay informed with Softlabs Group for more insightful content on cutting-edge technologies.

[biggest-gaudiest-patronuses]

i watched My Neighbor Totoro for the first time, here's my chronological viewing experience:

woo-hoo! dusty old japanese house with japanese architectural details aplenty

these kids got some ENERGY my goodness

family dynamic's adorable. peak quality dad humor

kids: our house is haunted. parents: that's so cool!

hell yeah, wrinkled old lady rep. we need more friendly old women with potato faces and warts like storybook witches. the backbone of society, these ladies

Plot Summary: Small Child Bothers Local Wildlife

sacred tree sacred tree sacred tree

Introducing Totoro! nobody said this fucker's got TEETH???

Uh-Oh! Inadequate Parental Supervision Detected

(you misplaced your four year old! you're not supposed to do that)

4-year-old: i met a magic forest spirit. dad: oh shit fr?

4-year-old: *angrily hugs sister* missed u bitch

this small child has a smile like a toad. like a really really cute toad. like the cutest toad in all existence. i love her she's perfection please just let this child be happy

rice paddies are so pretty....so back breaking....rice is such a prissy crop

*my crush is stranded in a rainstorm* takethisumbrellait'syoursnowBYE *runs away in panic im so good at flirting*

Giant Chinchilla Learns To Hold Umbrella, Is Fucking Delighted By Experience

take this, it will help you on your quest! *hands u trail mix wrapped in a leaf*

LO-FI HIP HOP STUDY LIST!

crouching down to peer at dirt--A++ top notch foundational childhood experience

mom has a big ass forehead

honey! the chinchillas are performing Rituals in the backyard again

help yeah let's jack and the bean stalk this shit

huh so we're all just climbing aboard the giant chinchilla's tiddies now ok

class trip!

the pure adrenaline of Vegetable Gardening

no! the small child is crying! she is bawling her eyes out. no no no. i can't cope with this. emotionally i cannot cope 🥺🥺🥺

i've only had Mei one hour but if anything happens to her i will raze this earth and everyone on it

please someone make this small child smile again

oh no the tall child is crying too

i can't take this. my heart can't take this.

i need a drink

small child running determined to deliver magic veggies to the hospital. this kid is my hero

she is also unsupervised. so, so unsupervised

babe you are FOUR

godDAMMIT ghibli, you cannot give me watercolor sunsets while a small child is missing. u are killing me. my heart is giving out. this is me, experiencing heart failure.

Totoro to the rescue!

no wait CATBUS to the rescue!

i admit i initially thought the cat was a creep. alice in wonderland prejudiced me. i have revised my notions of smiling cats

i've decided the cat is a metaphor for the magic of a robust public transport system

MEI'S OKAY!!!!!!!!!!!!!

and so is mom. she's a lovely lady im sorry for what i said about her forehead. it's a noble forehead.

happy ending YES bitch!!!!!!

ok. ok ok ok. that was magical.

(as a first-time adult viewer i was worried i wouldn't be able to Access the Magic. but i could and i did and it was incredible. that was culture. that was ART. joy distilled into animated form. holy rites of childhood. i understand now. how glorious, this world we grow out of. how full of marvels. i'm going outside to smell grass and sun and get dirt under my fingernails. miraculous.)

[theaifusion]

Feature Selection Using Wrapper Method

Feature selection in machine learning is gaining so much popularity because it makes the data more organized by reducing the number of features and keeping only relevant features, It removes irrelevant features by using techniques of feature selection. There are generally three types of feature selection techniques which are feature selection using the filter method, feature selection using the wrapper method, and feature selection using the embedded method.

Here's a complete guide to Feature selection using the wrapper method in Python!

[deadsetobsessions]

“I ate paint once,” Danny nonchalantly threw out in the middle of game night.

The entire table stopped. Heads whipped towards Danny.

“Yeah, me too. Cardamom yellow was my favorite. Ugly as hell but the chemicals just tasted right.” Tim replied, using the distraction to nab some of Bruce’s money. Monopoly money, that is. Everyone’s heads snapped towards Tim, only Cass and Danny (who was part of the scheme) caught him cheating.

“Really? I think mine was those spray can blue cosmos paint. But that might have been more my thing for space than the actual taste.”

“WHY WERE YOU EATING PAINT?!” Dick asked, looking like he wanted to lunge over the table and shake Danny until he puked out paint. Bruce looked like he was about to have a heart attack.

“Yeah, what the fuck, Tim?” Jason snickered.

“In my defense,” Danny grinned. “I was left unsupervised. Also, Steph, you owe me $24 in rent.”

“Ugh! I’m almost out of money! Can’t you loan me some, Alfred?”

“I am sorry, Miss Stephanie, you are not qualified for another loan. In fact, one of your properties is about to be confiscated as per the collateral agreement.”

“Noooo!” Stephanie made dramatic dying noises.

“What was your excuse, Timothy?” Damian asked, eyes glued to the board and determined to win the game.

“Hey, I was probably less supervised than Danny was.”

“Yeah,” Danny perked up. “My parents brought us down to their lab all of the time. Taught us a lot of stuff.”

“Really? Like what?” Duke asked, casually slapping away Tim’s sneaky hands.

“Oh, like what a rocket launcher sounded like up close! And how to build a laser gun! Oh! And what human organs looked like when they’re fresh!” Danny chirped, collecting his money from a stunned Stephanie’s hands. He looked up.

“Oh, don’t worry! I at least learned what not to do when it comes to lab safety. And we wore hazmat suits to protect ourselves from the radiation.” Danny smiled in a ditzy fashion as the table fell silent in a horrified manner. Cass tapped his arm amusedly, but allowed his bullshit to stand. After all, it’s not like he lied.

“Radiation?” Duck’s voice raised a couple of octaves. Oh yeah, Danny’s going to laugh about that pitch for a long while.

“Organs?!” Jason’s hands closed around the plastic house he was holding rather forcefully.

“Do you even know what basic lab safety practices are, Danny?” Damian demanded, finally looking up with brows furrowed. He rolled the dice and grabbed a mystery card. He gets $100 from Alfred.

“How old were you??” Duke asked.

“Like… 8, when they first brought me in?”

“Eight.” Bruce rumbled, slipping into a more Batman like persona. When Danny sent him a confused look, Bruce straightened back into his Bruce persona. “Wow, they must have trusted you a lot!”

“Sure?”

“What were their names again?” Stephanie asked sweetly, Cass nodding at him.

“Jack and Maddie Fenton.” Not that they’ll find them here, considering his parents are dead and in another universe.

“Cool, cool, cool!” Stephanie blinked, beaming as her hands formed lethal fists underneath the table.

Danny blinked and tilted his head in an unassuming way, pretending like he had no idea what Stephanie was thinking of. He sneakily handed over $600 to Cass in order to complete his monopoly on his side of the board.

Danny stood up and spread his hands out, one hand clutching his new found victory.

"Well, lady and gents, you've all been floundering against the inevitable tide of capitalism. I am here, as a reminder that you can never win against the hopelessness that will be your financial ruin! I, Danny Fenton, have obtained a quarter of the board and therefore have won against even your best efforts!" He cackled, holding up his fan of properties triumphantly. He shot a mischievous grin at Cass, who held up a solemn thumbs up in support for his monetary takeover.

"... Danny, are you... planning on a career in villainy?" Bruce asked, after a brief and total wave of shocked silence. Damian looked like he was having a conniption at having been bested, unknowingly. Yeah, Danny was disarming like that.

"Yeah, that was concerning." Tim piped up, nabbing a ten from a shell-shocked Damian.

"Hey! The Riddler gives surprisingly good monologues! And he's really loud, so it's hard not to pick up on things. Duke, your turn." Danny sat back down, pouting. The villainy comment was a little too close to his fears.

"Damn it." Duke, who had rolled, landed smack middle of Danny's territory. He handed over a sheaf of bills to a grinning Danny.

"Wait a minute! You have cheated!" Damian bolted upwards from his seat, finally done running through the purchases he remembered Danny making. "You acquired that property not within the games' rules!"

"Okay, first of all, the rule book is a suggestion, like lab safety rules," Danny saw the others open their mouths to protest, but he quickly shut it down. "Second, there's totally no rules about selling and buying places from a private owner so suck on it. And thirdly? Cass sold it to me, so you all can take it up with her."

"Diabolical!" Damian muttered indignantly.

"... Dammit." Dick sighed, falling back into the chair and balancing on its two legs. He couldn't say anything, considering his current of bankruptcy.

"Danny. Danny, I'll buy a property from you." Jason said, eyeing one of Danny's other properties near his own cluster.

"What do you have that would interest me?" Danny asked, falling back into his Vlad-like imitation.

"Ew, don't do that," Steph reached over to jab him in the arm.

"Yeah, Jason, what do you have?" Duke said, the lovely subtle instigator that he is.

"Red Hood's signature."

The others blue-screen, gaping at the actual audacity Jason had to offer up something that would take him no effort. Danny, prepared with a poker face that came with lying straight to Jazz's ever perceptive eyes about whether he nabbed the last of her ice cream or not, was prepared.

"Red Hood? The condom guy working out of the... um. Upper East Side?" Danny asked, pretending to hesitate. He knows where Jason operated. That doesn't mean he couldn't simply pretend otherwise. For science, of course.

...

...

...

The table howled with laughter, Jason's indignant spluttering unable to say anything against Danny's wide eyed look of innocence. Cass leaned against the table, chuckles falling out of her mouth and eyes crinkled in mirth. Dick had fallen out of his chair, helplessly wheezing on the floor. Duke is hiding his face in his hands, mirroring Bruce's pose as they both shake from silent laughter. Damian is smirking, wicked and sharp as he smugly stared at Jason. Stephanie and Tim are leaning against each other, repeating "the CONDOM GUY" in alternating and increasingly louder voices. Alfred had a smile on his face and a tight grip on the bills in front of him that betrayed his amusement.

"He's a crime lord!" Jason exclaimed, indignant.

"Uh, okay. Well, I mean, why would I want a crime lord's signature? I don't want to be on his radar. Or echolocation or whatever. He's... a Bat, right? That's what you guys call that group, yeah?"

"How do you know the Rogues better than the vigilantes?!" Jason glared at his unhelpful family. Those assholes better prepare for a load of rubber bullets the next time they're on patrol near Crime Alley.

"Hey, it's not my fault the vigilantes here are unsociable. Maybe if they monologued more, I'd know who they are."

"Wouldn't- wouldn't that make them more villain like?" Tim asked, stuttering from his laughter.

"I dunno?" Danny replied, enjoying his the family's unabashed joy. "I mean, they're pretty legit and they help people already so I guess they don't need to be sociable... but still I swear I haven't heard anything about Batman other than that he grunts and is mean towards criminals."

Is mean towards criminals, Duke mouthed at a recovering Dick who was in the process of heaving himself back up. It sent him careening back down to the floor with restrained giggles. Cass tapped Danny, reminding him to eat some food.

"Tt. Of course not. They're efficient at their jobs and have no need to be seen as welcoming to criminals." Damian puffed up.

"Yeah, but they've gotta feel safe, right?" Danny shrugged as he plucked a cookie from the cookie platter. "The... one with the sword, what was it?"

"Robin." Damian supplied, eyes narrowed and trained on him.

"Yeah, the baby bird. The kids think his swords are cool so they trust him. But like, the others? The flippy blue one? Not so much."

"Wait," Dick said from the floor. "They don't trust Nightwing?"

"Nah, they trust him to protect them, but he has a history of bringing the kids to the police, you know?"

"What's wrong with that?"

Danny shrugged. "ACAB. But also because everybody knows that half the guys in the GCPD and CPS are child traffickers."

"Wait, what?" Jason and Tim straightened.

Bruce piped in, the emotional whiplash of amusement to concern to amusement to concern visibly making itself known on the man's baffled face. "I thought Batman and Commissioner Gordon took care of that?"

"Sure, the obvious ones." Danny hesitated. Well, he's pretty sure they think he's a meta so... "There's... a meta trafficking ring that they're a part of. That's. That's kind of what I was running from."

Danny looked up pleadingly. Cass placed a hand on his arm in comfort, not knowing that he was fibbing about running from them.

Danny was on the streets helping his own Alley metas to run from them.

Danny is as feral as she was, and that meant he could hide just as much as she could read off of him. Cass was the best and he felt kind of bad about lying to her, successfully or not.

"Uh. Some people said you know Batman, Bruce. I know- uh, that might not be the case but if you do, could you ask him to look into it?" Danny made his eyes tear up. "And maybe he wouldn't care about me much, I mean, I know he doesn't really like metas but if he helps out, I could totally like, leave the city once the kids are safe, promise."

Ooh, Danny put a little too much sincerity into that. He could practically hear the hearts breaking in the game room as everyone glared at Bruce.

"You won't have to leave."

"... Promise?" And Danny's voice was a little too desperate, too hopeful, because Bruce's eyes tugged down in sadness.

"Promise." He rumbled, all Bruce Wayne and all Batman. Danny's core warmed. Danny also saw the rest of the family's faces darken in pure agreement. And partial wrath.

"Yeah! We'll kick Batman's ass if he even thought about kicking you out!" Stephanie proclaimed.

"He's far more proficient in combat than you are, Brown." Damian immediately leapt to Batman's defense and that was that.

Well, later, as Danny was "sleeping" and Phantom was hovering in the cave, invisible and intangible, he got confirmation that his Alley meta kids were going to be safe, soon.

After all, the entire Batclan was suiting up and baying for blood, with Oracle's all encompassing presence behind them, fingers reaching for their enemies' weak points.

[aicognitech]

Machine Learning: Exploring the Main Components and Functions of this Powerful AI Technique

Delve into the sector of Machine Learning as we discover its fundamental additives and functions. Discover the intricacies of supervised learning, unsupervised getting to know, and reinforcement gaining knowledge of, and understand how Machine Learning is revolutionizing industries and using AI advancements.

Machine Learning

[usaii]

Supervised vs Unsupervised Machine Learning: Understanding the Contrasts | USAII®

Learn the nuances of supervised and unsupervised machine learning from the perspective of an AI professional. Delve deeper into their functioning, characteristics, and types of algorithms used; and pave a successful AI career.

Read more: https://bit.ly/3XGcm2W

Supervised Learning, supervised learning algorithms, supervised learning in machine learning, supervised and unsupervised machine learning, supervised learning models, unsupervised learning methods, Unsupervised Learning, unsupervised learning algorithms, unsupervised machine learning, AI applications, machine learning algorithms, machine learning techniques, supervised and unsupervised learning

[voxslays]

Hi i loved your Hazbin Men as Dads Writing! Maybe you could write the same for the Hazbin Woman x reader as parents if the Idea intrests you ;-) ?

OMG OFC!!! I was thinking of doing a sequel lol but I wasn’t sure if I should. Now I definitely will though! <3

HAZBIN WOMEN AS MOTHERS

Featuring >>> Charlie, Carmilla, Rosie, Sera, Velvette, & Vaggie x Reader as mothers!

Rosie:

Let’s be honest, your child is going to be a cannibal. Assuming the three of you live in cannibal town together as a family, (which is very likely), this may happen sooner…like before your baby turns a year old…BUT ANYWAYS-!

Your child will grow up surrounded by Rosie’s loyal subjects. Nobody in cannibal town would dare lay a hand on your precious child, but for the few outsiders who try…let’s just say they pasta way. Their ashes may or may not end up in Rosie’s spice cabinet, and then on your dinner plate…but rosie is such a good cook!

Rosie would be such a good mom. Sure, she is a very busy woman, but she would always make time for her little (demonic) spawn! Overall, 10/10 parenting.

Carmilla:

Carmilla is already a mother, she has lots of experience. Having two or three daughters, (I can’t remember how many she has in the show lol), she has seen it all. She knows all the tricks. She is strict…but loving!!!

She is shown being protective and willing to anything to save her daughters, so it would be the same for your child, if not more. She would not let the poor kid out of her sight for the first few months. She knows hell is a dangerous place, and will teach your child how to defend themselves from a young age.

Like I mentioned with Valentino in my other post, Carmilla would likely have your child learning Spanish young. It is very important your child is well educated both in language (and fighting). But then again, she’s already portrayed to be a good mother in the show, so what did you expect?

Velvette:

Oh lord. With her there is no way your child isn’t a mistake. Velvette would be ‘way too busy’ to deal with a child. She is one of the Vees and the top designer in pride! What did you expect!? She doesn’t have time for some random child!

Velvette is literally an adult screenager, so like Valentino I don’t think she would be very responsible with your baby. Velvette would leave your child unsupervised, or under the supervision of one of her models who wasn’t busy at the moment, while she does fittings and preps her models for the next big fashion show.

While in public she puts up a front of being too busy, in private I think she would genuinely feel guilty. Overtime I think she would grow to care for the child, teaching them all about fashion and social media. She is totally the type of mom to show your child off on social media or just create an account from scratch. Its safe to say your child is already a star.

Charlie:

BEST MOM EVER??? I mean first of all, she is the princess of hell, and with her personality, that basically means your child is going to be spoiled rotten! Your child has all the (mostly duck themed toys, brought to you by Lucifer) they could ever dream of. This child is royalty, and will be treated as such.

She would NEVER yell at your child, god forbid the poor kid cries…she might start crying too! Charlie is also always up for playing with your child. Whether it’s arts and crafts, dress up, dollies, etc. she will drop whatever she’s doing—or finish it up quickly—and play.

Grandpa Luci is also around very frequently. He has just reconciled with his daughter after all, and his daughter has a daughter??? If Charlie wasn’t spoiling your child enough, Lucifer is doing ten times more. Every time he visits he brings your child a trinket, like one of his ducks, a duck themed onesie, or just a sugary treat.

Vaggie:

Literally a carbon copy of Carmilla but like ten times more protective. I mean how could she not be? Her precious child is living under the same roof as the radio demon! (Let’s just say that if Alastor steps within even ten feet of your baby he is getting threatened with a spear to the neck.

I feel like she would be a boy mom. Not in the tiktok boy mom sense, but I just generally feel like she would get along better with a son than a daughter. No matter which one you have though, she will love them unconditionally.

Supportive of her children’s dreams in the same way she supports Charlie’s. She is always very supportive, but can sometimes be a little doubtful. However, to balance that out, she always brings good advice to the table. I can also see her keeping secrets, like the fact she was an exterminator from her child until they get older.

Sera:

She’s like Carmilla but more angelic. Sera is very strict and by the book, and would expect her child(ren) to be the same. She can be hard on others, especially her children, but in reality she just wants the best for them.

If the two of you had a child, I feel they would be a mix of Emily and Lucifer. Kind, energetic, and a dreamer. This worries Sera a lot. She lived with Lucifer in heaven, she saw his dreams. Sera watched him fall for the dreams he tried to make a reality. Therefore, she would try to stop your child from turning into a dreamer.

Overtime, I think she would realize that your child’s dreams are nothing like Lucifer’s, and would become more supportive. Overall, Sera is very overprotective, strict, and hard on others, but she is truly looking out for their well-being.

[mirrorofliterature]

where is my bail organa takes anakin under his wing as a political mentee story?

I think bail organa could have fixed anakin.

picture this:

anakin meets bail. bail is like. cute troubled kid! must protect!

[the urge to protect children can be so strong]

bail finds out about palpatine's grooming mentorship and is like. hm I do not like this. I do not trust palpatine with anakin. is he meeting with him unsupervised? that's literally illegal*. he doesn't even have a working with children's check :/

bail, turning up to the jedi council: hi I am one (1) non-creepy, friendly and working with children's check carrying non-corrupt politician who would like to mentor (PROTECT) anakin skywalker. under your supervision, of course, because unlike SOME other people, I'm not a creep.

and the jedi council is like: yeah okay we don't want to have to deal with anakin anyway he's a difficult child anyway**

then bail goes and asks anakin, with obi-wan there, if he wants to be mentored by bail

bail: anakin, would you like to learn more about anti-slavery advocacy in the republic? my home planet, alderaan, is strongly involved. if obi-wan agrees, of course, as you are a child in his care.

anakin agrees

bail organa foils palpatine's plans by being like 'hm, shady' and is like: someone NOT creepy and corrupt needs to give this child a proper political education and if the jedi won't, then it's my job

he also hooks anakin up to a therapist for freed slaves asap

anyway bail & anakin mentorship (good) fucking up palpatine & anakin mentorship (bad) fix-it when?

*there are strong child safety laws where I live. probably would also be on alderaan.

**the parallels between how the jedi council/order treats anakin because he is different versus how mainstream society treats neurodivergent children as nuisances to be managed is strong (particularly given anakin's neurodivergent coding), in this essay I will -

[saturnniidae]

Oddly specific Hiccup childhood headcanons

Didn't start speaking until he was 5, even then he mostly just said the word 'why'. He also had a lisp.

People were struggling to understand him so he decided to communicate through other means

He has dyspraxia and spent hours everyday trying to perfect his handwriting so it was actually legible enough for people to understand him (though reading is not a strongsuit for most vikings and they often just ignored the pieces of paper shoved at them). He also tried his best to better coordinate his movements and be less clumsy. He didn't have as much luck with that one.

At some point he kind of gave up and embraced being brushed off by adults, he got tired of trying to cater to them and it wasn't worth it if they just ignored him anyway (the only time they payed attention was when they were judging him for something tbh).

He started wandering off into the woods for hours at a time to draw or play with animals, or most importantly, going hunting for trolls. He'd often lose track of time but always came back before sunset, and always scared the shit out of Stoick (he thought little Hiccup got carried off by a dragon, even though they're not often in berk's forests, or mauled by a bear).

He was accused multiple times (for various different reasons) of being a changeling. Though the only serious accusations were from Mildew, other times it was more an in-joke villagers made behind Stoick's back (if he heard them he'd shut it down immediately).

Hiccup didn't mind. The 'bad and dangerous' parts from stories never really sunk in, he was in awe more than anything. Everything 'magic' fascinated him to no end and he'd always run to Gobber for more stories (not at all helping him beat the changeling allegations) who was the only one who'd humor him.

He loved the forest animals and would often bring wounded ones home to try and help them.

He didn't have much interest in playing with other kids, and apart from the rare times he'd be invited to join, they had no interest in him (this was before they were influenced by adults, before they realized different meant bad). He's just naturally introverted, as well as most knowing better than to get attached to a hiccup. Runts rarely make it to adulthood.

He brought home a stray cat once. Stoick had no idea where it came from and was going to make it leave but couldn't bring himself to when he saw it curled up and purring in Hiccup's lap. He named it Fiddlesticks

A few years later Fiddlesticks died during a dragon raid, Hiccup had run out to try and help and while he was gone their roof collapsed. Hiccup was devastated. Stoick was just glad that for once, Hiccup wasn't where he was supposed to be.

He still wandered in the forest, making little animal friends and hunting for trolls (with less enthusiasm than before) but he tried not to get as attached.

Once he climbed a tree to try and sketch a birds nest but fell and broke his arm. He was under constant supervision until it healed, and it would've been longer if not for him getting in the way of Stoick's duties

He started working in the smithy with Gobber after Stoick had enough of him wandering about unsupervised. Perfecting the steady hand needed to work in the forge felt like learning to write all over again and it was incredibly frustrating for him, he almost gave up more than once but Gobber was encouraging enough.

When they were little, he and Snotlout used to be close. Snotlout never minded Hiccup not talking as it never really affected the games they'd play and he ignored the notion he shouldn't get attached. Hiccup was famliy, so naturally, Snotlout would protect him.

Eventually Snotlout realized (was pressured by snide comments and looks from adults) Hiccup was 'weird' and being his friend, despite the fact they are literally cousins, was making him weird by proxy. He stopped playing with Hiccup, but never told him why which caused some animosity that then turned into outright bullying a few years later.

[wileycap]

Special Instructions For Luke Skywalker

(approved by hon. Sen. Organa, Gen. Solo, entirety of Rogue Squadron, Adm. Ackbar, hon. Sen. Mothma, distributed to Cmdr. Skywalker)

1. No acrobatics, except in designated excercise areas.

1.1. Not even if you can "jump really high, the Force is so bright right now, look!"

2. Absolutely NO mind reading.

2.1. If you do it on accident, try not to get a weird look on your face and also tell us what you learned. Proverb: It's better to know than to wonder about it until one ends up doing something stupid.

2.2. Yes. I'm talking about that. I can't look General Madine in the eye anymore.

2.3. It's still allowed for intrasquadron prank purposes. But you can't do it to the others, they freak out.

3. No posing. Yes, you pose. It's annoying and wrong. The rest of us are dirty and want to slump down into our own filth and sleep. We don't need to see you being all heroic.

4. No claiming "the will of the Force" when you do something weird. Yes it's saved all our lives more than once. Yes it's still offputting and just plain disturbing.

4.1. "The universe is telling me that..." is not an acceptable substitute.

4.2. "I have received an omen" is also out.

4.3. "The vibes speak to me" is funny but no.

4.4. "Hey guys, guess what came to me in a dream" might be okay but it was in the middle of combat. And you said it over the general comms. And then you did an unannounced microjump into actual hyperspace in the middle of actual combat. Admiral Ackbar nearly had a medical event.

5. If somebody wants to hold your lightsaber you should let them.

5.1. You're officially allowed to disregard that. Never give Janson your lightsaber again. We have no idea how he snuck that in in the first place.

5.2. If Princess Leia requests to inspect your ceremonial weapon (commonly known as a 'lightsaber'), you should let her. For reasons for legitimate cultural intrest and archeological research. And because as your superior, she has the right to inspect your weapon as set down in the Alliance Charter, section General Conduct, heading B4467, subheading BA561-33. By permission of Princess Leia. I approve of this. Luke give it to me for a second you get to have it all the time.

6. If you need to "have a conversation with a ghost", do it in a private place.

6.1. If you agree to have your ghost conversations in private, we promise to stop referring to our "private time" as "having a conversation with a ghost."

6.2. In fact, we could just stop announcing it altogether. It was funny the first time and it hasn't been funny since. Guys, I don't want to know.

6.3. But please don't talk to thin air in front of us.

7. Luke, you are a hero of the Alliance. We are also friends. You don't need to bow when you see me, even if I am technically royalty and your superior.

7.1. It's very sweet that you do it and I appreciate that you want to show your respect, but the new recruits are getting confused.

7.2. NO, ADMIRAL ACKBAR DIDN'T WRITE THAT. It was obviously me, Leia!

7.3. If you're doing this on purpose and hiding it behind your innocent farmboyishness, I'LL KILL YOU. I'll kill you until you're dead.

7.4. STOP BOWING STOP

8. Don't work on the Falcon unsupervised.

8.1. Me being in the general area isn't supervision.

8.2. Me being near you but working on a different part isn't supervision.

8.3. Apparently me looking over your shoulder isn't supervision either. Just don't do it, kid.

9. Cub. You are very small in comparison to other humans. If you are having trouble hunting I can do it for you.

9.1. Apologies. Han reminded me that you are an adult by the standards of your species. I travel with him and I am often confused that he is an adult. You understand.

9.2. No asking Chewie if his relationship with me is "kind of like adopting a tooka" for him. For one, no, and for two, everybody else already made that joke.

9.3. He is very much like a badly behaved tooka.