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subair9 · 7 months ago
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ML Zoomcamp
Just completed the forth week of Machine Learning Zoomcamp.
The lessons covered include: 4.1 Evaluation metrics: session overview 4.2 Accuracy and dummy model 4.3 Confusion table 4.4 Precision and Recall 4.5 ROC Curves 4.6 ROC AUC 4.7 Cross-Validation
The link to the course is below: https://github.com/DataTalksClub/machine-learning-zoomcamp
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teguhteja · 9 months ago
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Unlocking the Power of Data Preprocessing: Mastering Normalization and Standardization
Dive into data preprocessing! Master normalization & standardization. Python & Pandas examples included. Boost your #MachineLearning models. Perfect for #DataScience enthusiasts. Learn how to transform your data for better results!
Data preprocessing techniques. Data scientists and machine learning enthusiasts often grapple with raw data that needs refining. Therefore, understanding data preprocessing techniques becomes crucial for success. In this blog post, we’ll dive deep into two essential methods: normalization and standardization. We’ll explore how these techniques can transform your data, particularly focusing on…
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juliebowie · 9 months ago
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Sklearn Cheat Sheet for Quick Machine Learning
A handy cheat sheet for using Sklearn, the popular machine learning library in Python. Find quick references to essential functions, algorithms, and workflows to accelerate your machine learning projects.
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bayesic-bitch · 1 year ago
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I will say that Python libraries have some extreme variation in quality. "Data science" libraries like seaborne and sklearn are absolute dog shit nightmares that assume you are too stupid to understand anything. I did not good experiences with PIL or Pillow. Matplotlib is too convoluted with the more complicated features, but at least the core stuff is clean and accessible. And numpy, pytorch, and Gym are just absolute masterpieces of clean and elegant design.
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joltai-showa · 5 months ago
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broooooo I HATE Decembers with fucking passion
good thing is I finished checking all the students' works during the weekend and that only took like 10 hours of my life that I'm not getting back (checking programming projects that also include students' writing about theoretical concepts is HELL because not only will they copypaste the paragraphs off the Internet/ChatGPT regardless if they got the results that the text they put in talks about, but they will also write something equivalent of eldritch horrors for DS majors instead of code and you need a bottle of vodka and 40 minutes to understand if they did things correctly and it's a bunch of bullshit thrown into the wall in hopes that something sticks. I've seen a guy calculate R2 for linear regression by the FUCKING DEFINITION. YOU ARE USING SKLEARN. r2_score IS A BASIC FUCKING FUNCTION IN THERE. BRO WHAT ARE YOU DOING)
bad thing is I went to a dentist appointment and had the wonderful experience of getting to hear a doctor giddily say "If you hadn't come with complaints about this, I wouldn't have ever believed that such thing could ever happen. I've never seen anything like that in my career😃" so now I gotta cough up quite the sum to fix something that should have been impossible to happen. that's on top of me spending quite the sum on the presents for my parents' birthday and just general New Year's stuff, so fuck you December for taking all of my hard earned money
and even worse thing is that my Chinese written exam is in two days and I just finished filling up my own personal vocabulary to train 汉字. It's got over 750 words. And I need to remember all of the grammar, especially the psychotic alchemy that are the comparison structures. I am cooked.
My friend is now back to sending me videos of that one girl who was studying to become a dentist and recorded her all-nighters before her finals and I guess I am indeed back to that exam/thesis preparation regiment when I drank at least 5 coffees per day cuz no way I am learning all that with a normal sleep schedule
anyway I liked the new Pure Fiction in HSR!
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I struggled to get 8-9 stars on the previous ones and finished it fully on the first try with this one😊honestly the buffs currently might be a bit too busted, but it's certainly a great improvement to how it used to be! Now my Jade actually feels like the T0 unit she is supposed to be in this mode😘
p.s. yeah no fanfiction work is being done rn I am fighting for my life here. hopefully by Friday I'll get some time to do the notes on "Eclipse"🙏
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Import Cygnus Oscuro
Summary: Creative Writing Final. It's a fedex humans are space orcs au. They're forced to be in the proximity of one another and it's fun for everyone except for those directly involved.
Word count : 5244
TW: one (1) swear word, auton (robot) racism including an in-universe slur (thanks, Fitz), absolutely incomprehensible worldbuilding (thanks, Squish)
Taglist (lmk if you want to be added/removed!): @stellar-lune @faggot-friday @kamikothe1and0nly @nyxpixels @florida-preposterously @poppinspop @uni-seahorse-572 @solreefs @i-loved-while-i-lied @rusted-phone-calls @when-wax-wings-melt @good-old-fashioned-lover-boy7 @dexter-dizzknees @abubble125 @hi-imgrapes @callum-hunt-is-bisexual @callas-pancake-tree @hi-my-name-is-awesome @katniss-elizabeth-chase @arson-anarchy-death @dizzeners @thefoxysnake @olivedumdum @loveution
On Ao3 or below the cut!
Bonus worldbuilding / q&a / suffering because I doubt any of this makes sense
    import pandas as pd
    import numpy as np
    import matplotlib.pyplot as plt
    from sklearn import datasets, model_selection, metrics
    from sklearn.model_selection import train_test_split, cross_val_score
    from sklearn.preprocessing import *
    from sklearn.neighbors import KNeighborsClassifier
    from sklearn.metrics import *
    “Once again, what do you mean by Eifelia? The planet itself or the system as a whole, including its moons?” Sophie asks, staring out the window at the receding planetary surface as their spaceship affectionately called the ‘Cygnus Oscuro’ lifts off the ground. 
    “Eifelia has only one confirmed inhabited moon, Batyrbai. Your home planet of Datson is the only satellite in the Telychian system to have more than one moon that is suitable for habitation. Supplies were acquired at the port of Darriwilian, located at 25.78, -80.21, on the planet of Eifelia itself,” Dex replies, reading off the coordinates from the corner of xor vision. 
    It’s very easy to read off coordinates when xor neural network is constantly searching for information that it thinks will be helpful to xem. It, more often than not, is entirely extraneous information, but it is difficult to discern when, exactly, it will be of assistance. 
    Dex continues, “Five crew members departed in preparation for Eifelia’s cyclical festival of Batyrbai appearing full in the sky from the dark side of the planet. In turn, three crew members embarked.”
    Dex’s fan freezes up. “One of these crew members is human, which hail from Earth, most accurately described as a ‘Death Planet’. It is located in the system of Sol, 40.3 light years away. Take care to avoid any and all possible conflicts.” 
    Sophie fixes Dex an unbelieving look. “They can’t be that bad.”
    Article after article scroll across Dex’s field of vision. “They’ve earned their infamous reputation and most are highly unaware of it. Did you know they have contests to see which one can suffer through the most capsaicin-induced pain? Then, to cool the pain, they consume a drink full of near-impossible-to-digest lactose sugar.”
    “Yeah, and you can bend titanium without even a second thought.”
    “I’m sure a dedicated enough one would figure out how to do that.” 
    Sophie rolls his eyes. “I’ll make sure to tell Keefe not to be an intergalactic space wyrm this week but I don’t think that’s going to be happening any time soon.” 
    Dex’s processor runs the numbers, and Sophie is correct for once. In any other situation, a correct prediction from him would be a thing to praise, but in this particular case, it’s more worrisome for Keefe’s safety.
    stars_df = pd.Dataframe(data=stars.data, columns=stars.feature_names)
    stars_df.iloc[39060]
    name                “Beta Pictoris c”
    distance_ly         60         # light years, 3*10^8 m/s
    yerkes_stellar_class    “A6V”
    mass                4658.44    # Eifelia masses, 4.13*10^24 kg
    orbital_period         197.55     # Eifelia years
    grav_accel         182.470    # m/s^2
    surface_temp        1250       # kelvin
    “Greetings,” Dex’s assigned partner says as Dex slides into the chair next to him. His voice is blanketed with a thick accent Dex’s processor is unable to place, though the circling loading sign in the top corner is certainly trying. Such is the curse of exploring new planets faster than xor updates are able to keep track of them. 
    Today’s mission is expected to make that problem worse, although only slightly.     
    “I’m Fitz,” he says, holding out a hand.    
    “I’m D3x+3r,” Dex replies, not actually pronouncing the numbers like numbers even if they should be pronounced like numbers because they are numbers. The loading wheel is still circling around itself. “Although most people call me Dex because apparently two syllables is too many. I don’t understand it either.” 
    Fitz’s hand falls into his lap. “Nice to meet you, Dex.” He pauses. “Unless you have anything else I’ve forgotten, I think we can probably get going down to the surface so that we can get back sooner than later.” 
    Dex pushes away the loading circle in favor of the small transport ship’s inventory list. “I believe we have everything. If that is a false presumption, the communication link with the Cygnus Oscuro is up and running.” 
    Fitz gently undocks from the Cygnus Oscuro and that’s when Dex’s processor finally decides to provide xem with any information. It’s odd how it’s so proficient with useless information and finally now that it’s relevant, it takes a suspiciously long time. 
    It apparently doesn’t think it’s a major priority to know that xe’s just been sealed into a very small shuttle with a human. No big deal. This is both fine and normal. It’s not like they’re documented to have very short tempers. 
    Now the accent makes sense. Humans have hundreds of different languages, owing to their incredibly diverse geographic distribution. Most other species, including the Eifelians, only exist in small pockets in the corners of their worlds. Humans looked at that and went ‘no, I don’t think I will.’ Any other species is almost immediately recognizable by their accent but humans. They live to be difficult. 
    Even if the accent hadn’t been atrociously obvious in hindsight, the lines streaking across his skin—Blaschko lines, Dex’s processor claims—should have given his heritage away. The even more entertaining part is that most humans don’t even know they have them. 
    Dex’s processor is able to pull up Fitz’s official file without too much difficulty, and that seems like a mostly safe conversation to have instead of stilted silence. “So, how long have you been part of Parallax?” 
    “Well, my parents have worked here since before I was born, so the answer I usually give is, ‘Yes.’ How about you?” 
    “I was built on Gzhelia roughly 250 Eifelia years ago.” Dex pauses, converting this to a unit hopefully a little more familiar to Fitz. “That’s a little more than 4 Earth years.” 
    Fitz’s brows draw together. “Built?”
    Dex’s fan pauses in such a way that it sounds like a sigh as xe pulls back the artificial skin away from xor wrist, revealing the wires twisting underneath. A green fiber optic cable shimmers in the artificial light of the shuttle. 
    “I am aware that I am running on slightly older hardware, but I promise that my software is as updated with the most current Parallax Dataframes an update cycle half an Eifelia year ago could provide. Again, for ease of conversion, that is about three Earth days.”
    “You can stop with that. The conversions. I’ve grown up around more Eifelia time than Earth time.” 
    “I apologize. I was simply trying to prevent any incidental miscommunication before there was an issue. I will refrain from it in the future.” 
    The table of conversions still floats in front of Dex’s vision like a temporary burn-in.    
    Dex and Fitz sit in a silence that even Dex’s emotion identifier that was deprecated two years ago can identify as uncomfortable. Xe really should get around to installing a new one. 
    Fitz is the one to break the silence. “How’d you know I was human? Your little CPU tell you?” 
    Dex nods slowly. “Yes, it did, along with installing several files explaining your species’ customs. I can feel one of them slowing down my SSD flash memory with its sheer size.” 
    “Yeah, yeah, we all get it. Humans are big and loud and dumb and there’s so many of us that you can’t be bothered to learn all of it.” 
    Fitz flicks a half-dozen switches, initiating the landing sequence of the shuttle now that it is within the last thousand kilometers of altitude. The reason that it has to be activated so early is due to Beta Pictoris C’s incredibly high gravitational acceleration, causing the shuttle to have a much higher velocity than if it were under the gravitational influence of most other planets. 
    In other, more numerical terms, gravitational acceleration on Beta Pictoris C at the surface is about 182.970 m/s2, while, for reference, Eifelia’s is 8.011 m/s2. Of course, they are still up in the air, meaning that their orbital radius is slightly larger than the planet’s radius, but that really is not that much of a difference due to the sheer scale of the planet. 
    It’s no wonder Parallax has chosen the two of them for this mission—they’re the most likely to not be crushed under the sheer weight of the surface gravity. Or, more accurately, their own weight due to the increased surface gravity. 
    Fitz touches down gently, one of the very few landings Dex has experienced without involving a significant amount of screaming. 
    “Are you ready to go find one amino acid and then leave?” he asks, standing up. 
    Searching for life on planets like these is, for lack of a better descriptor, a neural-network-numbing process involving taking a few dirt samples while trying to make sure that Dex’s zinc components don’t get instantaneously vaporized, among other problems. 
    A-type stars aren’t even the hottest ones out there, but they’re on the very edge of what is believed to be habitable due to their instability. Their scarcity in the universe also makes it much more unlikely for life to have the opportunity to form around one. 
    It’s nearly inhospitable to every life form currently described, leaving a few carbon-fiber autons to figure out how to sample things on stars-forsaken planets that are literally half the surface temperature of Eifelia’s home star, Telychia. 
    “It would probably be beneficial to don some protective clothing before doing that, even if Beta Pictoris C is nearing aphelion and we have landed on the night side. Do you happen to know if it is tidally locked?”
    “That’s not in your file system?”
    “I regrettably am unable to locate it if it is.” 
    Fitz rolls his eyes, muttering, “Turing incomplete,” under his breath. 
    It takes a few milliseconds for Dex’s processor to provide the context to that statement, and that context is not a flattering one. Its origins lie with both the first human theoretical computer scientist, Alan Turing, and it became popularized due to Earth’s history with artificial intelligence. 
    It’s…not a pleasant history. 
    “Do you believe that infinite memory is possible? Because everything is technically only Turing complete when it is assumed to have infinite amounts of memory, which is impossible to create in the real world. Thus, every device, including this shuttle and your knee replacement is Turing incomplete.
    “Yeah, but at least I can feel emotions.”
    Fitz slides the heat suit’s helmet over his head, obscuring his face.
    “Most of your emotions are induced by shifts in hormonal signals. The Floians don’t have hormones. Does that mean that they too are artificial because they do not experience emotions in the same way that you do?” 
    Fitz opens the shuttle door, pressing himself against the wall to avoid being blown away by both the swirling, windy atmosphere blowing dust into all of the delicate machinery of the shuttle and the zeroth law of thermodynamics. 
    Dex’s fan immediately kicks into its highest gear, and it will stay there as long as the door remains open, barring some catastrophic, friction-related disaster. 
    “The Floians had to figure out how to evolve on their own. That should be a reasonable enough distinction for you.”
    “That implies that genetically modified organisms don’t count as organisms. And then, most autons learn via a reinforcement algorithm that mimics how evolution works in order to train a neural network. That’s the thing that I have making decisions in my ‘little CPU’ and its trillion transistors. How many neurons do you have again?”
    Fitz steps out into the outside, his suit making him look like a large orange nebula. Hopefully the door doesn’t decide to close with its own artificial consciousness like last time. That was not a fun time. 
    “Why do you ask when you could just search through your files? I’m sure it’s in there.” 
    “The answer was 135 billion,” Dex says flatly. That would be a more relevant description if xe was able to inflect xor speech more, but xe has found the setting to make xor voice a specific frequency and uses it a touch more than xe probably should. 
    Fitz turns back to Dex. “What are you doing? The sooner we get these samples into your file system, the sooner I stop looking like the stay puft marshmallow man.”
    Dex smiles as the image flashes across xor vision. Xe follows Fitz down the ramp, revealing the expected vast desertlike landscape of Beta Pictoris C.
    It’s significantly too hot for water to remain liquid but—there’s something odd about the erosion patterns. Those might not just be wind erosion. Xe downloaded a whole library of algorithms a couple of months ago. 
    Ignoring Fitz’s demands to know where xe’s going, Dex approaches one of the striated, gray rock formations. 
    url = 'https://docs.google.com/spreadsheets/d/e/2PACX-1vTCZgoegOH a49SFXYU-ZZTdCkgTp0sn&single=true&output=csv'
    rocks_df = pd.read_csv(url)
    features = rocks[["depth", "width", "mohs_hardness"]]
    label = stars_df["class"]
    X_train, X_test, y_train, y_test = model_selection.train_test_split(features, label, test_size = 0.2, random_state = 42)
    model = KNearestNeighbor(n_neighbors = 53)
    model.fit(X_train, y_train)
    new_rock = pd.Dataframe([7,4,6.5])
    pred = model.predict(new_rock)
    A smile blossoms across Dex’s face. “We’ve got liquid erosion. It’s slightly less viscous than water, but liquid erosion nonetheless.” 
    Fitz stares at xem, waiting for an explanation that takes a long time to get there. 
    “I’m going to have to run some simulations on the ship because I don’t have enough RAM for the kind of resolution I want, but there’s potential that there used to be water here, and I’m sure you’re aware of how water and life are synonymous. Most of the time.” 
    Dex carefully scrapes off a corner of the ashy sandstone column for further study because xe, quite unfortunately, doesn’t have a built-in mass spectrometer. It’s also generally good practice to collect samples. 
    Another aspect of good practice is to look at more than one rock before drawing conclusions about an entire planet. 
    Dex traces into the dirt a simple sketch of Fitz in his marshmallow suit. He’s lucky to have all of his appendages attached, let alone proportional. Dex then takes a sample of the dirt. The mixing helps to paint a better picture of what the sand is like, rather than just the solar-radiation-exposed topsoil. 
    Suddenly, Fitz swears, pointing at something in the vial. That something is a little creature wiggling its way around the glass. 
    Dex nearly drops it, which would have been a less than ideal decision, as xe tries to find the little guy who is desperately trying to not be seen. 
    The little guy is a fairly standard arthropod-style body plan, with an exoskeleton, a number of legs that is larger than 2 and smaller than the number required for ‘burn it alive’ algorithms to kick in. So somewhere in the 6,8,10 range is probably pretty reasonable. 
    Although, to be fair, even numbers are more of a guideline than anything else. Once again, Earth is an exception to the rule with a three legged fish down in some of the deepest parts of its oceans. Also echinoderms with their five-fold radial symmetry. 
   ��“You, uh, might want to put him down,” Fitz suggests. “You don’t want to be charged with kidnapping should that little bug guy who I’m now going to be naming Fred turn out to have a consciousness.”
    Humans’ inclination to name creatures that have no way of communicating with them is a fairly large section in their file overview. It seems as though this can even occur with inanimate objects, which just links to a page advertising a pet rock, whatever that’s supposed to mean. 
    Dex pours the vial back onto the ground and attempts to take another sample without kidnapping another Fred. 
    Is that how human naming goes? Does it really matter? 
    The only reason this is a question is probably because It feels like all of Dex’s wires are currently being poached in the water designed to cool them. 
    There’s another one in the next vial. And the next. It’s almost like spontaneous generation but, like, not yet disproven by putting meat in a jar and covering it so maggots don’t get laid on it. 
    Yeah, that’s literally what the humans decided to do. Specifically one named Francesco Redi. Seems like a waste of calories for a species who needs to eat a lot of them to support their endothermic metabolisms. At least they figured it out in the end. 
    The fourth attempt seems to be safe as Dex only fills the vial halfway and shakes it extensively to avoid accidental kidnapping. Now the only possible complication could be microscopic creatures, but that’s past the point of reasonable care. 
    Fitz spends another few minutes gallivanting around, likely wandering around for more interesting samples, even if the entire report is already writing itself in the back of Dex’s processor. 
    He returns with a half dozen more samples of varying mineral compositions which get stored in his marshmallow suit’s pockets. “I saw another guy. Sorry I couldn’t get a picture, but he kind of looked like a scorpion. If you know what those are.”
    Dex nods, projecting a picture of one onto the first rock ledge just to prove that xe has image files stored in xor drive. 
    “Yeah, he looked kind of like that.”
    Dex switches the picture to a different one, one that isn’t necessarily a true scorpion. That doesn’t stop Eurypterus from colloquially being called a sea scorpion. It also doesn’t stop them from being extinct on Earth for around 252 million of its own years. 
    Fitz repeats, louder this time, “Yeah, he looked kind of like that.” 
    Fitz’s new best friend the Beta Pictoris C scorpion, who notably has yet to be blessed with a name, hops up onto the rock ledge, and it’s remarkable how similar they look, albeit the hologram being significantly larger. Blue swirls across its hardened exterior, and its pincers look like they’re very ready to reduce the number of fingers Dex has. 
    A warning light flicks on in the corner of Dex’s vision, cutting off access to xor files. 
    “We should probably be getting back to the ship. I have the coordinates of our landing point so that a larger, more prepared team can conduct a more detailed study. And before you begin to state that we are that team, if I am to stay out here for much longer, I will probably end up shutting down, and that is a burden I would rather not impose upon you.”
    It’s kind of odd how Dex’s vision is able to start flickering as xor processor threatens to have enough for the day. One would think it would work the same as when it gets too cold, but no. One second, xe’s completely fine and the next, xe’s restarting after eighteen hours trapped in an avalanche. 
    This is a normal experience. It’s not Dex’s first time, and most other autons xe has communicated with have had similar ones. It’s a risk associated with the job, and xor data won’t be lost in anaerobic environments the same way that data in an biologically-designed brain will. 
    Unless that brain belongs to an obligate or facultative anaerobe, but the vast majority of intelligent species do require some form of a gas to function. Many use oxygen, but carbon dioxide, methane, hydrogen, and carbon monoxide are fairly common as well. 
    Dex and Fitz make their way back into the spaceship and make absolutely certain that the hatch is sealed before peeling off their marshmallow suits. Dex’s blinking temperature warning sign disappears, but xor fan still remains running at full speed. 
    Fitz collapses into the pilot’s chair, sweat streaking down his brow, and barely waits for Dex to sit down beside him before lifting off. 
    They once again sit in an uncomfortable silence, punctuated only by the sounds of Fitz flipping various switches on the shuttle’s control panels. 
    Dex makes half a note that xe should learn how to fly a ship at some point, although Sophie would rapidly abuse that particular ability. 
    Once xe’s back aboard the Cygnus Oscuro, xe locates the mass spectrometer in order to analyze the samples before Fitz starts telling everyone about the larger portion of their discovery, because then xe’s going to have to answer other people’s questions instead of xor own.
    url = ‘https://docs.google.com/spreadsheets/d/16lsnIQaP37r682gKuz CZp-YqLgCis-Ln4PSaDEpiAjw/edit#gid=0’
    mass_spec = pd.read_csv(url) 
    compounds = []
    for i in range(mass_spec.size()): 
        id = identify(mass_spec[i]) 
        compounds += id 
     It turns out to absolutely no one’s surprise that liquid water doesn’t exist inside of the rock samples, but tricobalt tetraoxide, Co3O4, is in there, and it is a liquid at the planet’s surface temperature. It’s certainly a choice for an electron donor, and it’s kind of a wonder the entire planet isn’t bright blue with the Cobalt (ii) ions. 
    Dex isn’t surprised to find out that by the time xe’s had enough time with the samples that the entire ship knows about the little arthropod that was found, even if they aren’t formally related to the Earthen order of arthropoda Fitz is comparing it to. 
    They look similar. It’s close enough. 
    What Dex is surprised to find is that everyone wants a tour to see them despite the fact that the vast majority of the crew would acquire heat stroke almost instantaneously. This is xor thirty-sixth mission to actually go down onto a planet for the first time—autons are cheaper to replace than biological organisms—and this is by far the biggest response to a new species. 
    It’s odd. Xe doesn’t like it. 
    Dex’s neural network wants to blame it on Fitz, and there really isn’t any data to contradict that particular hypothesis. It also makes it a very difficult hypothesis to test, which makes it significantly less useful as a hypothesis. 
    On the other hand, a useful hypothesis would be one relating to the actual little alien creatures that for some reason are able to live on a planet that’s more similar to a furnace than a habitable landscape. 
    And so, against all logical reasons surrounding the temperature of a planet known to be at least twice the temperature of the hottest previously confirmed life forms. Of course it’s on Earth. Hydrothermal vents don’t look like a place where organisms could live, and then they’re just down there chilling. That’s probably not the best choice of a descriptor. 
    When in doubt, the answer is more often than not ‘Earth is a weird planet.’ 
    The journey back down to the surface with Fitz passes with significantly less fanfare than the first, the beeping of the ship being obnoxiously loud in the deafening silence. 
    They touch down, Fitz not taking as much care as last time with making sure the landing has as little of a change in momentum as possible, which is to say that it’s nowhere near the gentle landing of the first trip. 
    Fitz leans back and sighs. “Do you have any commentary you’d like to provide or are you ready to go and collect data so we can finish our reports on this planet?”
    “I mean, I’m always collecting data, even if it's only a live feed of my precise coordinates getting thrown into a plaintext file never to be seen again, so the answer is closest to both of the above.” 
    That does not seem to be the answer Fitz wants as he takes one of his bags of human snacks—potato chips, according to what’s printed on the yellow label—and throws it into the garbage can in the corner. 
    “Wow.” Dex’s visual apertures widen. “I didn’t realize that throwing projectiles with accuracy was a human skill. I’ll make sure to add that to my files, as well as to the main system.”
    Fitz’s eyes flash, his features drawing into hard lines. “Are you physically incapable of not being condescending? I get it. I’m a human. I’m from a death planet. Humans are weirder than fucking dark energy. It doesn’t require that many comments about it to get your point across!”
    Dex pauses, letting xor neural network fully process Fitz’s statements before replying, “I don’t understand where I was being condescending.”
    “You just did it two sentences ago!”
    “I did not do anything two sentences ago. It was genuinely quite interesting how your species has evolved to throw objects with accuracy, even ones with high surface area to volume ratios such as that bag of chips, because it is not something that has been documented in any other intelligent species.” 
    “Oh, please. It’s a basic skill.”
    “Do remember that your species evolved in part to bring down large prey such as Mammuthus primigenius. Throwing spears at a wooly mammoth directly led to that ability being rewarded with a higher rate of nutrients, and thus resulted in the following generations being more able to throw spears as well.”
    “You know all of that but you didn’t figure out that throwing things is pathetically easy? Your little auton brain isn’t very good at drawing conclusions from data you have, is it?”
    “It is simply something I did not have cause to consider before now, though I do recognize that it would have been quite easy to identify without the inciting event.”
    “And you’ve also said that you have a very large file on humans. Most of our games are based around the concept of throwing a ball. Was that not enough information to extrapolate that maybe we’re good at it?”
    “Games of chance are common in many species. It follows that this could simply be a manifestation of that desire in humans, so games like your ‘basketball’ or ‘baseball’ do not provide sufficient evidence to draw conclusions such as the ones you’re suggesting.”
    Fitz rolls his eyes. “Why do I even bother? It’s not like you’re going to change your mind. You don’t have a mind to change.”
    Dex wants to explain that xor neural network is actually changing its dependence on its individual notes on a regular basis, but that doesn’t seem to be advantageous in this particular context.
    Fitz rolls his eyes, muttering in what is likely his native tongue—one which Dex has not downloaded the translation file of—as he gets into the marshmallow suit once again. 
    They go out, describe a half dozen new arthropod-esque species, each with more legs than the last, and return with more samples with as few words as possible. But nothing is ever allowed to be simple. 
    The hatch on the shuttle has decided today that leaving itself open in the blistering heat is not something it likes to do, and while Fitz and Dex are distracted, it shuts its doors. 
    In turn, it opens the floodgates for Dex to learn some new fun human swear words when Fitz notices what’s happened. 
    “No reason to worry,” Dex says, making xor way through the sand to open up the back emergency panel that exists for exactly this reason. 
    “Uh, I left the keys in there. There’s very much a reason to worry.”
    “And I’ve got admin privileges. It’s fine. Go back to looking for the next beetle you’re going to call your son.”
    “Don’t be rude to Benny like that. He’s not that replaceable.”
home@Cygnus-shuttle-3:~$sudo su
home@Cygnus-shuttle-3:~$******
root@Cygnus-shuttle-3:~$ufw disable 
    There’s no particular reason why the firewall sometimes decides to make the hatch close, and this is enough of a solution for Dex to not go searching for an answer. 
    As the door begins to open again, Fitz asks, “So, what’s the password?”
    “I’m the password.”
    “Yes, yes, I understand that you’re helpful. Now, what’s the password if this were to happen again and you aren’t around?”
    “I’m the password. It’s literally just my name. D3x+3r. It’s got an uppercase character, lowercase character, number, and a special character. My friend Sophie thought he was hilarious when he heard it, so now it’s my password for everything. Don’t tell anyone.”
    “I won’t. I don’t even know where the special characters are even if I wanted to.”
    “The ‘t’ is replaced with a plus. The ‘e’s are fairly obviously transliterated to ‘3’s. There’s nothing fancy going on here.”
    Fitz turns to walk away but stops himself. “The name Sophie feels a little familiar. Does he by any chance know a Keefe?”
    “Yes, actually. The two of them dated for a while. Although I’m not sure if that should be in the past tense. I stopped asking for updates a while ago.” 
    Fitz laughs. “Stars, I wish I could figure out how to do that. I’ve never escaped from them.”
    “Just kind of stare blankly into the distance and people will stop wanting to tell you things. They’re usually doing it because they want compliments on whatever it is they’re telling you, and by depriving them of that, they stop wanting to do that.”
    “Are you sure you’re an auton?”
    Now it’s Dex’s turn to laugh, a sound xe was very much not designed to make, so it sounds more like an out of tune record skipping. “Yeah, I think so. I’ve walked into too many door frames to have gone this long without getting a contusion, which is another thing your species doesn’t particularly care about getting.”
    “Case in point: I found one on my leg yesterday and I have no idea how I got it. It’s already green and I’m not sure how I hadn’t noticed it before. I guess that’s what I get for being from a death world.”
    Dex gestures widely to the rolling desert around xem. “I think Earth’s death world status may be a bit outdated. If this isn’t a death world, I don’t know what is, and, by comparison, I’m pretty sure Earth is an absolute paradise. You didn’t have to evolve to use tricobalt tetraoxide as an electron donor.”
    “We’ve also had five mass extinctions,” Fitz interjects. 
    “So has everybody else, including the Datsonians, even if their government would rather not admit that out loud. You’re not special.”
    Fitz snaps his fingers inside of the marshmallow suit, which does not work well with the thick padding of the gloves. “And that’s exactly what I wanted you to admit.”
    “Is that why you volunteered to come back down here?”
    “That was mostly a decision based on Parallax’s inability to find another poor sap that would be willing and able to come down here.”
    “Wouldn’t it be really funny if they send a Gzhelian in your place?”
    Fitz smiles, the sound of the air conditioners they use onboard the Cygnus Oscuro at a nice, toasty 200 kelvin having kept him from sleeping for nearly as many hours as Dex has wanted to disconnect xor audio input. 
    A beat of silence stretches in the space between them, but for the first time it isn’t immensely uncomfortable. 
    “We should probably be getting back inside the shuttle before it decides to close again,” Dex says, even if it would be very entertaining if they stood outside long enough for it to grow its own intelligence again. 
    After all, that’s kind of how xe got here. Xe’s going to get replaced by a shuttle door within the next couple of Eifelia years. 
    Xe’ll probably get assigned to, like, repairing the Cygnus Oscuro in all of the places the non-auton mechanics are unable to go, but at least xe’ll have discovered a wondrous new world before that happens. 
    while True: 
        # avoid getting hit by Fitz’s projectiles
# no, seriously, they’re dangerous
        update_coordinates()
        data_status = upload_data()
        if (data_status == True):
            break() 
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0x4468c7a6a728 · 1 year ago
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sklearn (as in scikit-learn) should be pronounced /sklɝn/
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antiquery · 1 year ago
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baby's first sklearn model! (predicting fanfic popularity on AO3)
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learnershub101 · 2 years ago
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25 Udemy Paid Courses for Free with Certification (Only for Limited Time)
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2023 Complete SQL Bootcamp from Zero to Hero in SQL
Become an expert in SQL by learning through concept & Hands-on coding :)
What you'll learn
Use SQL to query a database Be comfortable putting SQL on their resume Replicate real-world situations and query reports Use SQL to perform data analysis Learn to perform GROUP BY statements Model real-world data and generate reports using SQL Learn Oracle SQL by Professionally Designed Content Step by Step! Solve any SQL-related Problems by Yourself Creating Analytical Solutions! Write, Read and Analyze Any SQL Queries Easily and Learn How to Play with Data! Become a Job-Ready SQL Developer by Learning All the Skills You will Need! Write complex SQL statements to query the database and gain critical insight on data Transition from the Very Basics to a Point Where You can Effortlessly Work with Large SQL Queries Learn Advanced Querying Techniques Understand the difference between the INNER JOIN, LEFT/RIGHT OUTER JOIN, and FULL OUTER JOIN Complete SQL statements that use aggregate functions Using joins, return columns from multiple tables in the same query
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Python Programming Complete Beginners Course Bootcamp 2023
2023 Complete Python Bootcamp || Python Beginners to advanced || Python Master Class || Mega Course
What you'll learn
Basics in Python programming Control structures, Containers, Functions & Modules OOPS in Python How python is used in the Space Sciences Working with lists in python Working with strings in python Application of Python in Mars Rovers sent by NASA
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Learn PHP and MySQL for Web Application and Web Development
Unlock the Power of PHP and MySQL: Level Up Your Web Development Skills Today
What you'll learn
Use of PHP Function Use of PHP Variables Use of MySql Use of Database
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T-Shirt Design for Beginner to Advanced with Adobe Photoshop
Unleash Your Creativity: Master T-Shirt Design from Beginner to Advanced with Adobe Photoshop
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Complete Data Science BootCamp
Learn about Data Science, Machine Learning and Deep Learning and build 5 different projects.
What you'll learn
Learn about Libraries like Pandas and Numpy which are heavily used in Data Science. Build Impactful visualizations and charts using Matplotlib and Seaborn. Learn about Machine Learning LifeCycle and different ML algorithms and their implementation in sklearn. Learn about Deep Learning and Neural Networks with TensorFlow and Keras Build 5 complete projects based on the concepts covered in the course.
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Essentials User Experience Design Adobe XD UI UX Design
Learn UI Design, User Interface, User Experience design, UX design & Web Design
What you'll learn
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Build a Custom E-Commerce Site in React + JavaScript Basics
Build a Fully Customized E-Commerce Site with Product Categories, Shopping Cart, and Checkout Page in React.
What you'll learn
Introduction to the Document Object Model (DOM) The Foundations of JavaScript JavaScript Arithmetic Operations Working with Arrays, Functions, and Loops in JavaScript JavaScript Variables, Events, and Objects JavaScript Hands-On - Build a Photo Gallery and Background Color Changer Foundations of React How to Scaffold an Existing React Project Introduction to JSON Server Styling an E-Commerce Store in React and Building out the Shop Categories Introduction to Fetch API and React Router The concept of "Context" in React Building a Search Feature in React Validating Forms in React
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Complete Bootstrap & React Bootcamp with Hands-On Projects
Learn to Build Responsive, Interactive Web Apps using Bootstrap and React.
What you'll learn
Learn the Bootstrap Grid System Learn to work with Bootstrap Three Column Layouts Learn to Build Bootstrap Navigation Components Learn to Style Images using Bootstrap Build Advanced, Responsive Menus using Bootstrap Build Stunning Layouts using Bootstrap Themes Learn the Foundations of React Work with JSX, and Functional Components in React Build a Calculator in React Learn the React State Hook Debug React Projects Learn to Style React Components Build a Single and Multi-Player Connect-4 Clone with AI Learn React Lifecycle Events Learn React Conditional Rendering Build a Fully Custom E-Commerce Site in React Learn the Foundations of JSON Server Work with React Router
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Build an Amazon Affiliate E-Commerce Store from Scratch
Earn Passive Income by Building an Amazon Affiliate E-Commerce Store using WordPress, WooCommerce, WooZone, & Elementor
What you'll learn
Registering a Domain Name & Setting up Hosting Installing WordPress CMS on Your Hosting Account Navigating the WordPress Interface The Advantages of WordPress Securing a WordPress Installation with an SSL Certificate Installing Custom Themes for WordPress Installing WooCommerce, Elementor, & WooZone Plugins Creating an Amazon Affiliate Account Importing Products from Amazon to an E-Commerce Store using WooZone Plugin Building a Customized Shop with Menu's, Headers, Branding, & Sidebars Building WordPress Pages, such as Blogs, About Pages, and Contact Us Forms Customizing Product Pages on a WordPress Power E-Commerce Site Generating Traffic and Sales for Your Newly Published Amazon Affiliate Store
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The Complete Beginner Course to Optimizing ChatGPT for Work
Learn how to make the most of ChatGPT's capabilities in efficiently aiding you with your tasks.
What you'll learn
Learn how to harness ChatGPT's functionalities to efficiently assist you in various tasks, maximizing productivity and effectiveness. Delve into the captivating fusion of product development and SEO, discovering effective strategies to identify challenges, create innovative tools, and expertly Understand how ChatGPT is a technological leap, akin to the impact of iconic tools like Photoshop and Excel, and how it can revolutionize work methodologies thr Showcase your learning by creating a transformative project, optimizing your approach to work by identifying tasks that can be streamlined with artificial intel
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AWS, JavaScript, React | Deploy Web Apps on the Cloud
Cloud Computing | Linux Foundations | LAMP Stack | DBMS | Apache | NGINX | AWS IAM | Amazon EC2 | JavaScript | React
What you'll learn
Foundations of Cloud Computing on AWS and Linode Cloud Computing Service Models (IaaS, PaaS, SaaS) Deploying and Configuring a Virtual Instance on Linode and AWS Secure Remote Administration for Virtual Instances using SSH Working with SSH Key Pair Authentication The Foundations of Linux (Maintenance, Directory Commands, User Accounts, Filesystem) The Foundations of Web Servers (NGINX vs Apache) Foundations of Databases (SQL vs NoSQL), Database Transaction Standards (ACID vs CAP) Key Terminology for Full Stack Development and Cloud Administration Installing and Configuring LAMP Stack on Ubuntu (Linux, Apache, MariaDB, PHP) Server Security Foundations (Network vs Hosted Firewalls). Horizontal and Vertical Scaling of a virtual instance on Linode using NodeBalancers Creating Manual and Automated Server Images and Backups on Linode Understanding the Cloud Computing Phenomenon as Applicable to AWS The Characteristics of Cloud Computing as Applicable to AWS Cloud Deployment Models (Private, Community, Hybrid, VPC) Foundations of AWS (Registration, Global vs Regional Services, Billing Alerts, MFA) AWS Identity and Access Management (Mechanics, Users, Groups, Policies, Roles) Amazon Elastic Compute Cloud (EC2) - (AMIs, EC2 Users, Deployment, Elastic IP, Security Groups, Remote Admin) Foundations of the Document Object Model (DOM) Manipulating the DOM Foundations of JavaScript Coding (Variables, Objects, Functions, Loops, Arrays, Events) Foundations of ReactJS (Code Pen, JSX, Components, Props, Events, State Hook, Debugging) Intermediate React (Passing Props, Destrcuting, Styling, Key Property, AI, Conditional Rendering, Deployment) Building a Fully Customized E-Commerce Site in React Intermediate React Concepts (JSON Server, Fetch API, React Router, Styled Components, Refactoring, UseContext Hook, UseReducer, Form Validation)
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Run Multiple Sites on a Cloud Server: AWS & Digital Ocean
Server Deployment | Apache Configuration | MySQL | PHP | Virtual Hosts | NS Records | DNS | AWS Foundations | EC2
What you'll learn
A solid understanding of the fundamentals of remote server deployment and configuration, including network configuration and security. The ability to install and configure the LAMP stack, including the Apache web server, MySQL database server, and PHP scripting language. Expertise in hosting multiple domains on one virtual server, including setting up virtual hosts and managing domain names. Proficiency in virtual host file configuration, including creating and configuring virtual host files and understanding various directives and parameters. Mastery in DNS zone file configuration, including creating and managing DNS zone files and understanding various record types and their uses. A thorough understanding of AWS foundations, including the AWS global infrastructure, key AWS services, and features. A deep understanding of Amazon Elastic Compute Cloud (EC2) foundations, including creating and managing instances, configuring security groups, and networking. The ability to troubleshoot common issues related to remote server deployment, LAMP stack installation and configuration, virtual host file configuration, and D An understanding of best practices for remote server deployment and configuration, including security considerations and optimization for performance. Practical experience in working with remote servers and cloud-based solutions through hands-on labs and exercises. The ability to apply the knowledge gained from the course to real-world scenarios and challenges faced in the field of web hosting and cloud computing. A competitive edge in the job market, with the ability to pursue career opportunities in web hosting and cloud computing.
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Cloud-Powered Web App Development with AWS and PHP
AWS Foundations | IAM | Amazon EC2 | Load Balancing | Auto-Scaling Groups | Route 53 | PHP | MySQL | App Deployment
What you'll learn
Understanding of cloud computing and Amazon Web Services (AWS) Proficiency in creating and configuring AWS accounts and environments Knowledge of AWS pricing and billing models Mastery of Identity and Access Management (IAM) policies and permissions Ability to launch and configure Elastic Compute Cloud (EC2) instances Familiarity with security groups, key pairs, and Elastic IP addresses Competency in using AWS storage services, such as Elastic Block Store (EBS) and Simple Storage Service (S3) Expertise in creating and using Elastic Load Balancers (ELB) and Auto Scaling Groups (ASG) for load balancing and scaling web applications Knowledge of DNS management using Route 53 Proficiency in PHP programming language fundamentals Ability to interact with databases using PHP and execute SQL queries Understanding of PHP security best practices, including SQL injection prevention and user authentication Ability to design and implement a database schema for a web application Mastery of PHP scripting to interact with a database and implement user authentication using sessions and cookies Competency in creating a simple blog interface using HTML and CSS and protecting the blog content using PHP authentication. Students will gain practical experience in creating and deploying a member-only blog with user authentication using PHP and MySQL on AWS.
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CSS, Bootstrap, JavaScript And PHP Stack Complete Course
CSS, Bootstrap And JavaScript And PHP Complete Frontend and Backend Course
What you'll learn
Introduction to Frontend and Backend technologies Introduction to CSS, Bootstrap And JavaScript concepts, PHP Programming Language Practically Getting Started With CSS Styles, CSS 2D Transform, CSS 3D Transform Bootstrap Crash course with bootstrap concepts Bootstrap Grid system,Forms, Badges And Alerts Getting Started With Javascript Variables,Values and Data Types, Operators and Operands Write JavaScript scripts and Gain knowledge in regard to general javaScript programming concepts PHP Section Introduction to PHP, Various Operator types , PHP Arrays, PHP Conditional statements Getting Started with PHP Function Statements And PHP Decision Making PHP 7 concepts PHP CSPRNG And PHP Scalar Declaration
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Learn HTML - For Beginners
Lean how to create web pages using HTML
What you'll learn
How to Code in HTML Structure of an HTML Page Text Formatting in HTML Embedding Videos Creating Links Anchor Tags Tables & Nested Tables Building Forms Embedding Iframes Inserting Images
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Learn Bootstrap - For Beginners
Learn to create mobile-responsive web pages using Bootstrap
What you'll learn
Bootstrap Page Structure Bootstrap Grid System Bootstrap Layouts Bootstrap Typography Styling Images Bootstrap Tables, Buttons, Badges, & Progress Bars Bootstrap Pagination Bootstrap Panels Bootstrap Menus & Navigation Bars Bootstrap Carousel & Modals Bootstrap Scrollspy Bootstrap Themes
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JavaScript, Bootstrap, & PHP - Certification for Beginners
A Comprehensive Guide for Beginners interested in learning JavaScript, Bootstrap, & PHP
What you'll learn
Master Client-Side and Server-Side Interactivity using JavaScript, Bootstrap, & PHP Learn to create mobile responsive webpages using Bootstrap Learn to create client and server-side validated input forms Learn to interact with a MySQL Database using PHP
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Linode: Build and Deploy Responsive Websites on the Cloud
Cloud Computing | IaaS | Linux Foundations | Apache + DBMS | LAMP Stack | Server Security | Backups | HTML | CSS
What you'll learn
Understand the fundamental concepts and benefits of Cloud Computing and its service models. Learn how to create, configure, and manage virtual servers in the cloud using Linode. Understand the basic concepts of Linux operating system, including file system structure, command-line interface, and basic Linux commands. Learn how to manage users and permissions, configure network settings, and use package managers in Linux. Learn about the basic concepts of web servers, including Apache and Nginx, and databases such as MySQL and MariaDB. Learn how to install and configure web servers and databases on Linux servers. Learn how to install and configure LAMP stack to set up a web server and database for hosting dynamic websites and web applications. Understand server security concepts such as firewalls, access control, and SSL certificates. Learn how to secure servers using firewalls, manage user access, and configure SSL certificates for secure communication. Learn how to scale servers to handle increasing traffic and load. Learn about load balancing, clustering, and auto-scaling techniques. Learn how to create and manage server images. Understand the basic structure and syntax of HTML, including tags, attributes, and elements. Understand how to apply CSS styles to HTML elements, create layouts, and use CSS frameworks.
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PHP & MySQL - Certification Course for Beginners
Learn to Build Database Driven Web Applications using PHP & MySQL
What you'll learn
PHP Variables, Syntax, Variable Scope, Keywords Echo vs. Print and Data Output PHP Strings, Constants, Operators PHP Conditional Statements PHP Elseif, Switch, Statements PHP Loops - While, For PHP Functions PHP Arrays, Multidimensional Arrays, Sorting Arrays Working with Forms - Post vs. Get PHP Server Side - Form Validation Creating MySQL Databases Database Administration with PhpMyAdmin Administering Database Users, and Defining User Roles SQL Statements - Select, Where, And, Or, Insert, Get Last ID MySQL Prepared Statements and Multiple Record Insertion PHP Isset MySQL - Updating Records
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Linode: Deploy Scalable React Web Apps on the Cloud
Cloud Computing | IaaS | Server Configuration | Linux Foundations | Database Servers | LAMP Stack | Server Security
What you'll learn
Introduction to Cloud Computing Cloud Computing Service Models (IaaS, PaaS, SaaS) Cloud Server Deployment and Configuration (TFA, SSH) Linux Foundations (File System, Commands, User Accounts) Web Server Foundations (NGINX vs Apache, SQL vs NoSQL, Key Terms) LAMP Stack Installation and Configuration (Linux, Apache, MariaDB, PHP) Server Security (Software & Hardware Firewall Configuration) Server Scaling (Vertical vs Horizontal Scaling, IP Swaps, Load Balancers) React Foundations (Setup) Building a Calculator in React (Code Pen, JSX, Components, Props, Events, State Hook) Building a Connect-4 Clone in React (Passing Arguments, Styling, Callbacks, Key Property) Building an E-Commerce Site in React (JSON Server, Fetch API, Refactoring)
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Internet and Web Development Fundamentals
Learn how the Internet Works and Setup a Testing & Production Web Server
What you'll learn
How the Internet Works Internet Protocols (HTTP, HTTPS, SMTP) The Web Development Process Planning a Web Application Types of Web Hosting (Shared, Dedicated, VPS, Cloud) Domain Name Registration and Administration Nameserver Configuration Deploying a Testing Server using WAMP & MAMP Deploying a Production Server on Linode, Digital Ocean, or AWS Executing Server Commands through a Command Console Server Configuration on Ubuntu Remote Desktop Connection and VNC SSH Server Authentication FTP Client Installation FTP Uploading
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Linode: Web Server and Database Foundations
Cloud Computing | Instance Deployment and Config | Apache | NGINX | Database Management Systems (DBMS)
What you'll learn
Introduction to Cloud Computing (Cloud Service Models) Navigating the Linode Cloud Interface Remote Administration using PuTTY, Terminal, SSH Foundations of Web Servers (Apache vs. NGINX) SQL vs NoSQL Databases Database Transaction Standards (ACID vs. CAP Theorem) Key Terms relevant to Cloud Computing, Web Servers, and Database Systems
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Java Training Complete Course 2022
Learn Java Programming language with Java Complete Training Course 2022 for Beginners
What you'll learn
You will learn how to write a complete Java program that takes user input, processes and outputs the results You will learn OOPS concepts in Java You will learn java concepts such as console output, Java Variables and Data Types, Java Operators And more You will be able to use Java for Selenium in testing and development
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Learn To Create AI Assistant (JARVIS) With Python
How To Create AI Assistant (JARVIS) With Python Like the One from Marvel's Iron Man Movie
What you'll learn
how to create an personalized artificial intelligence assistant how to create JARVIS AI how to create ai assistant
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Keyword Research, Free Backlinks, Improve SEO -Long Tail Pro
LongTailPro is the keyword research service we at Coursenvy use for ALL our clients! In this course, find SEO keywords,
What you'll learn
Learn everything Long Tail Pro has to offer from A to Z! Optimize keywords in your page/post titles, meta descriptions, social media bios, article content, and more! Create content that caters to the NEW Search Engine Algorithms and find endless keywords to rank for in ALL the search engines! Learn how to use ALL of the top-rated Keyword Research software online! Master analyzing your COMPETITIONS Keywords! Get High-Quality Backlinks that will ACTUALLY Help your Page Rank!
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codingprolab · 7 days ago
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CS 422 - Data Mining Homework 2
1.1 Chapter 3 Exercises: 2,3,5,6,7,8,12 2 Practicum Problems These problems will primarily reference the lecture materials and the examples given in class using Python. It is suggested that a Jupyter/IPython notebook be used for the programmatic components. 2.1 Problem 1 Load the iris sample dataset from sklearn (load iris()) into Python using a Pandas dataframe. Induce a set of binary Decision…
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subair9 · 7 months ago
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ML Zoomcamp
Just completed the second week of Machine Learning Zoomcamp.
The lessons covered include: 1 Car price prediction project 2 Data preparation 3 Exploratory data analysis 4 Setting up the validation framework 5 Linear regression 6 Linear regression: vector form 7 Training linear regression: Normal equation 8 Baseline model for car price prediction project 9 Root mean squared error 10 Using RMSE on validation data 11 Feature engineering 12 Categorical variables 13 Regularization 14 Tuning the model 15 Using the model 16 Car price prediction project summary 17 Explore more
The link to the course is below: https://github.com/DataTalksClub/machine-learning-zoomcamp
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teguhteja · 9 months ago
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Unlocking the Power of Machine Learning with Sklearn: A Beginner's Guide
Unlock the power of Sklearn for machine learning. This beginner's guide covers data handling, preparation, and model structures. Start your AI journey now!
Demystifying Machine Learning and Sklearn Sklearn machine learning basics. First and foremost, let’s unpack what machine learning really means. Essentially, it’s a branch of artificial intelligence that enables computers to learn and improve without explicit programming. This technology powers many aspects of our daily lives, from the voice assistants we use to the recommendations we receive on…
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usaii · 19 days ago
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Scikit-Learn (Sklearn) - The No-Fluff Machine Learning Library | Infographic | USAII®
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Unravel Scikit-Learn Machine learning library, which aims for greater precision in your organizational systems. Succeed with the best machine learning courses now!
Read more: https://shorturl.at/szusu
Machine learning (ML) libraries, machine learning algorithms, Matplotlib, Auto-GPT, ML libraries, large language models (LLMs), machine learning engineer, machine learning certifications, best machine learning certifications, Top AI ML certification, machine learning course
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frentmeister · 27 days ago
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Metamorphic Testing für Machine Learning – Wie Du KI testest, ohne die richtige Antwort zu kennen
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Stell Dir vor, Du arbeitest an einem Machine-Learning-Modell, das Bilder klassifiziert. Du hast Pipelines, Modelle, Metriken – aber Du stellst Dir eine entscheidende Frage: „Woher weiß ich eigentlich, ob das Modell korrekt funktioniert, wenn ich gar nicht weiß, was das richtige Ergebnis sein soll?“ Willkommen im Orakelproblem. Und willkommen bei einer der elegantesten Lösungen dafür: Metamorphic Testing. https://www.youtube.com/watch?v=RdpHONoFsSs
Was ist Metamorphic Testing?
Zu Beginn wurden die Modelle des maschinellen Lernens als nicht testbar bezeichnet, da es kein Testorakel gibt. Man könnte jedoch einen Pseudo-Orakel-Ansatz verwenden, wie z. B. einige der folgenden, um die Modelle des maschinellen Lernens zu testen: Beim Metamorphic Testing geht es nicht darum, das richtige Ergebnis zu überprüfen – sondern zu prüfen, ob das System konsistent reagiert, wenn Du kontrollierte Änderungen am Input vornimmst. Ein Beispiel:Du gibst einem Klassifizierer ein Bild von einem Hund – das Modell sagt: „Hund“. Jetzt drehst Du das Bild leicht oder erhöhst die Helligkeit. Erwartung: Das Modell sagt immer noch „Hund“. Wenn es plötzlich „Kuh“ ruft, weißt Du: Das System ist anfällig für minimale Änderungen – und damit potenziell instabil im Produktiveinsatz. https://www.youtube.com/watch?v=IV3UY6k2qVg&pp=ygUTTWV0YW1vcnBoaWMgVGVzdGluZw
Warum gerade bei Machine Learning?
Im klassischen Testing weißt Du oft: Input A → Output B. Du kannst asserten, validieren, vergleichen.Bei Machine Learning ist das anders: Die Modelle sind probabilistisch Die Trainingsdaten enthalten Bias oder Noise Die Antwort auf einen neuen Input ist nicht immer eindeutig Aber was Du testen kannst, sind die Beziehungen zwischen Eingabe- und Ausgabevarianten. https://www.youtube.com/watch?v=H7T4Agtr_Nc&pp=ygUTTWV0YW1vcnBoaWMgVGVzdGluZ9IHCQl-CQGHKiGM7w
Metamorphe Beziehungen – der Schlüssel
Hier ein paar typische Beispiele:   Use Case Veränderung am Input Erwartetes Verhalten Bildklassifikation Helligkeit +10 %, kleine Rotation Gleiche Prediction Sentiment Analysis Groß-/Kleinschreibung ändern Gleiches Sentiment Recommender Liste umsortieren Gleiche Top-5 Items Named Entity Rec. Satz passiv statt aktiv formuliert Gleiche Entitäten   Diese Regeln kannst Du selbst definieren – sie sind oft domänenspezifisch, aber unfassbar mächtig.
So setzt Du Metamorphic Testing in Python um
Hier ein schnelles Beispiel mit sklearn und einem Digits-Datensatz: from sklearn.datasets import load_digits from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from skimage import exposure import numpy as np # Datensatz und Modell X, y = load_digits(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42) clf = LogisticRegression(max_iter=10000).fit(X_train, y_train) # Sample auswählen sample = X_test.reshape(1, -1) original_prediction = clf.predict(sample) # Helligkeit verändern (Gamma-Korrektur) sample_img = X_test.reshape(8, 8) bright_img = exposure.adjust_gamma(sample_img, gamma=0.8) bright_sample = bright_img.reshape(1, -1) new_prediction = clf.predict(bright_sample) print(f"Original: {original_prediction}, Nach Helligkeit: {new_prediction}")   Wie immer bleibe ich an dem Thema dran.......       Read the full article
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phoebewang · 1 month ago
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K-Means Cluster Analysis
The code and analysis are below. Please read, thank you!
Figure 1:
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Figure 2:
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Cluster sizes:
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Descriptive Statistics:
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Interpretations:
A k-means cluster analysis was conducted to identify subgroups based on patterns in 12 behavioral, demographic, and family-related variables that might be associated with drinking frequency. Clustering variables included demographic indicators such as age and sex, along with several items related to family structure (e.g., FMARITAL, ADULTCH, OTHREL), alcohol-related behaviors (e.g., S1Q7A1, S1Q7A2, S1Q7A3, S1Q7A8, S1Q7A9), and school-related items (e.g., S1Q24LB). Before the analysis, all clustering variables were normalized with a mean of 0 and a standard deviation of 1.
The dataset was randomly split into a training set (70%, N ≈ 29,165) and a test set (30%, N ≈ 12,493). K-means cluster analyses were performed on the training data using Euclidean distance, with solutions ranging from one to nine clusters. In Figure 1, t he average within-cluster distances were shown as an elbow curve. The elbow plot did not provide a clear point of inflection, but it did indicate that a 3-cluster solution may be a viable option for interpretation.
Canonical discriminant analysis was used to reduce the 12 clustering variables to two dimensions for visual purposes as shown in Figure 2. The scatterplot demonstrated that the three groups were slightly distinct in canonical space. Cluster 0 (the purple one) looked to be the biggest group, with more dense organizing, but clusters 1 and 2 (yellow and green) were smaller and more scattered, indicating more within-cluster variability.
Descriptive statistics showed the cluster sizes: Cluster 0 (N=22,998), Cluster 1 (N=5,977), and Cluster 2 (N=1,190). Cluster 1 was characterized by above-average values in S1Q7A1, indicating higher engagement in the “present situation including working full time (35+ hour per week).  and older age. Cluster 2 was differentiated by low values in S1Q7A8 and ADULTCH, suggesting more negative responses related to unemployed and permanently disabled present situation and adult child of respondent in the respondent’s household. Cluster 0 had mean values near the standardized average across most variables.
To externally validate the clusters, an ordinary least squares (OLS) regression was used to determine whether the clusters varied substantially in terms of drinking frequency (DRINKFREQ). The regression results suggested a statistically significant model (F(2, 17071) = 67.66, p < .0001), although the explained variance was modest (R² = 0.008). Post-hoc comparisons showed that Cluster 1 had a substantially greater drinking frequency (b = 0.0855, p <.0001) than Cluster 0, but Cluster 2 had a significantly lower drinking frequency (b = -0.0923, p <.0001). This validates the clusters' discriminant validity in terms of drinking habit.
These results indicates that self-reported drinking frequency varies according to different behavioral and demographic factors. Further investigation of the smallest cluster such as Cluster 2 may reveal protective variables or demographic traits associated with lower alcohol consumption.
Code:
from pandas import Series, DataFrame import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn import preprocessing from sklearn.cluster import KMeans from sklearn.decomposition import PCA from scipy.spatial.distance import cdist import statsmodels.formula.api as smf import statsmodels.stats.multicomp as multi
""""Load dataset""" data = pd.read_csv("_358bd6a81b9045d95c894acf255c696a_nesarc_pds.csv")
""""Uppercase column names""" data.columns = map(str.upper, data.columns)
""""Drop missing values""" data_clean = data.dropna()
""""Subset clustering variables""" cluster = data_clean[['AGE', 'SEX', 'S1Q7A11', 'FMARITAL', 'S1Q7A1', 'S1Q7A8', 'S1Q7A9', 'S1Q7A2', 'S1Q7A3', 'ADULTCH', 'OTHREL', 'S1Q24LB']]
""""tandardize clustering variables""" clustervar = cluster.copy() for col in clustervar.columns: clustervar[col] = preprocessing.scale(clustervar[col].astype('float64'))
""""Split into training and testing sets""" clus_train, clus_test = train_test_split(clustervar, test_size=0.3, random_state=123)
""""K-means clustering with k from 1 to 9 (Elbow method)""" clusters = range(1, 10) meandist = []
for k in clusters: model = KMeans(n_clusters=k, random_state=123) model.fit(clus_train) meandist.append( sum(np.min(cdist(clus_train, model.cluster_centers_, 'euclidean'), axis=1)) / clus_train.shape[0] )
""""Plot Elbow Curve""" plt.figure() plt.plot(clusters, meandist, marker='o') plt.xlabel('Number of Clusters (k)') plt.ylabel('Average Distance to Centroid') plt.title('Elbow Method for Optimal k') plt.grid(True) plt.show()
"""" Fit KMeans for 3 clusters""" model3 = KMeans(n_clusters=3, random_state=123) model3.fit(clus_train) clusassign = model3.predict(clus_train)
""""visualization""" pca_2 = PCA(2) plot_columns = pca_2.fit_transform(clus_train) plt.figure() plt.scatter(x=plot_columns[:, 0], y=plot_columns[:, 1], c=model3.labels_, cmap='viridis') plt.xlabel('Canonical Variable 1') plt.ylabel('Canonical Variable 2') plt.title('Scatterplot of Canonical Variables for 3 Clusters') plt.grid(True) plt.show()
""""Merge cluster assignment with clustering variables""" clus_train.reset_index(level=0, inplace=True) cluslist = list(clus_train['index']) labels = list(model3.labels_) newlist = dict(zip(cluslist, labels)) newclus = DataFrame.from_dict(newlist, orient='index') newclus.columns = ['cluster'] newclus.reset_index(level=0, inplace=True) merged_train = pd.merge(clus_train, newclus, on='index')
""""Cluster frequencies""" print("Cluster sizes:") print(merged_train['cluster'].value_counts())
""""Cluster means""" clustergrp = merged_train.groupby('cluster').mean() print("Clustering variable means by cluster") print(clustergrp)
""""External validation using S2AQ21B - drinking frequency"""
""""Replace blanks and invalid codes"""" data['S2AQ21B'] = data['S2AQ21B'].replace([' ', 98, 99, 'BL'], pd.NA)
""""Drop rows with missing target values"""" data = data.dropna(subset=['S2AQ21B'])
""""Convert to numeric and recode binary target"""" data['S2AQ21B'] = data['S2AQ21B'].astype(int) data['DRINKFREQ'] = data['S2AQ21B'].apply(lambda x: 1 if x in [1, 2, 3, 4] else 0)
drinkfreq_data = data[['DRINKFREQ']] drinkfreq_train, drinkfreq_test = train_test_split(drinkfreq_data, test_size=0.3, random_state=123) drinkfreq_train1 = pd.DataFrame(drinkfreq_train) drinkfreq_train1.reset_index(level=0, inplace=True)
""""Merge with clustering data"""" merged_train_all = pd.merge(drinkfreq_train1, merged_train, on='index') sub1 = merged_train_all[['DRINKFREQ', 'cluster']].dropna()
"""" ANOVA to test cluster differences in DRINKFREQ"""" drinkmod = smf.ols(formula='DRINKFREQ ~ C(cluster)', data=sub1).fit() print(drinkmod.summary())
""""Means and SDs for DRINKFREQ by cluster"""" print('Means for DRINKFREQ by cluster') print(sub1.groupby('cluster').mean())
print('\nStandard deviations for DRINKFREQ by cluster') print(sub1.groupby('cluster').std())
""""Tukey HSD post-hoc test"""" mc1 = multi.MultiComparison(sub1['DRINKFREQ'], sub1['cluster']) res1 = mc1.tukeyhsd() print(res1.summary())
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damilola-doodles · 1 month ago
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Predicting Employee Insights and Workforce Trends with Machine Learning
In this project, we’ll walk through a comprehensive machine learning pipeline that focuses on employee performance prediction, hiring trend forecasting, salary analysis, and much more—all from a single dataset. This tutorial is ideal for anyone who wants to: Gain hands-on experience with pandas, sklearn, and basic forecasting. Learn how to build pipelines and use models like Random Forests and…
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