Beamery’s Talent Lifecycle Management platform empowers companies to understand the skills and capabilities they have, build more agile workforce plans, and attract, retain, upskill and redeploy their workforce. With Beamery, organizations are able to navigate change at speed, retain critical skill sets, foster DE&I and build the workforce of the future.

Beamery

Last month I participated in a panel discussion at event from . A lot of professionals from , , , were present at this event. Reviving London's Tech Scene London Tech Network Meta The Economist Osedea Beamery Our discussion focused on bridging the gap between technical and non-technical teams.
Here are my key takeaways from this discussion. Why is there a gap between technical and non-technical teams? To a certain extent, this gap is caused by a relatively low priority that soft skills are given among tech employees. I find this problematic because in my view soft skills are equally important to technical skills when you are building a digital product. According to my research, there are at least 13 categories of soft skills: adaptability, communication, compromise, creative thinking, dependability, leadership, listening, work ethic, teamwork, positivity, critical thinking, conflict resolution, and negotiation. As you can see, each category is related to interacting with different people (managers, stakeholders, teammates, junior developers, product owners, etc.) on different levels. In any case, if you want to positively impact your organisation and build high-quality products, you definitely should not underestimate the importance of soft skills. This is even more important if you are a Senior Software Engineer and want to grow your career and move to the next level. How to bridge this gap and improve soft skills So, you made the decision to improve your soft skills. It might sound daunting, and a lot of introverted software engineers I know struggle with this. However, there are proven techniques that you can employ to improve your communication skills. They can be broadly divided into those that you can do during working hours and outside working hours. Leverage your company resources My advice is to have regular 1:1 conversions with your line manager and look for ways of improving your soft skills together. I can share some personal examples from my current employer, Beamery. These are four activities that I find helpful for improving my soft skills. Mentoring Program Beamery has an internal Mentorship program that employees can participate in as mentors and mentees, and uses our internal product called Beamery Grow. Since I joined this program, I have been mentoring a Senior QA Engineer whose goal it is to become a Full Stack Engineer, in the format of weekly sessions. Being a mentor has been an enriching experience and helped me improve my communication, listening and mentoring skills. Internal events We host a lot of internal events at Beamery, in various formats and for various audiences. I particularly enjoy “Launch and Learn” sessions where engineers give presentations about some cool piece of technology or an interesting architecture proposal they have made at work. I think taking part in internal events like this can be a great way to improve your public speaking skills in a supportive environment. Publications If you have something to share with the tech community, you can write an article for your company blog. It might be on a technical or a non-technical topic. While writing, you will begin to pay more attention to word choice, sentence structure and tone. As with everything, the more you write, the more your writing skills will improve. Donut buddies I find the to be a great way to meet new people in Slack: this app regularly matches you with a random colleague and encourages you to make a connection and organise a chat. It helps break the ice by suggesting some initial topics for conversation. I usually have a few meetings organised by Donut each month, during which I’m matched to a non-technical person within the Customer Success department or product department. This is a great way to meet people you probably wouldn’t meet otherwise, learn about other parts of the business and improve your communication skills. And you don’t have to speak only about work! Donut app for Slack Outside of working hours There are plenty of opportunities to improve your soft skills outside the working hours as well. You can start speaking at meetups and conferences. You can also start volunteering, which I find very enjoyable. There are lots of communities where you can be a teacher or a coding mentor, which can be a huge boost to your soft skills: you will communicate with different people with different skills, from different cultures, etc. On top of that, your contribution will actually help people to start an engineering career! I currently mentor and teach people at , , and , which are all great organisations I can vouch for. Codebar Meet a Mentor Code Your Future Parting thoughts I hope you found this article interesting and motivating, and I hope it gave you some ideas for starting working on the soft skills. What is the most effective method for improving soft skills in your experience? Also published . here

Bridging the Gap Between Technical & Non-technical Teams

Getting stuff done is an art, getting a team to get stuff done is a symphony of egos, habits, and skillsets. The optimal system is usually right around the corner. But because of small mistakes, it can seem unreachable. These mistakes, however, can usually be solved without meetings or more than a few messages on slack. management A well-tuned project pipeline can be the difference between it getting completed in 10x the time, causing: large amounts of unproductive work and decreased quality of life at a personal level. Or, being completed early with: the least amount of work possible, a feeling of accomplishment throughout the process and a sense of belonging and pride. So I gathered a list of lessons learned and methods that I have seen work very well. Hope you learn something new! very summed up Things that do More Harm than Good Group Meetings for Task Reviews 🌪 These usually just result in a bigger mental burn than actual development, not covering all tasks and misinterpretations due to lack of time. A solution to this is a task pipeline with QA (quality assurance) steps, where tasks are reviewed independently when ready. Automating Everything ⛓ example: when a Pull Request has a specific keyword and/or a task ID the task is auto dragged into Review This, at first sight, seems great and is very appealing to developers, however, what we do when setting up these automations is: create hidden actions that must be taught, remembered and documented, increase project management complexity and become more dependent on the current management technology, making it less likely to ever switch. Sprint cycles by objectives, instead of periods🚦 Some companies deal with uncompleted sprints by delaying them into the following weeks.However, this can turn into a vicious cycle that results in big amounts of stress for the whole team. It’s much healthier to stick to the cycles on a per time basis. So if a print reaches the last day and some tasks are not complete they can be re-evaluated and moved (or not) into the next sprint. Having a Backlog 🗄 Continuously dumping feature requests and bugs into a single Backlog makes it very time consuming to consult and allows for these tasks to remain there for eternity, forgotten and burdening. A more effective approach is to have a process for: adding these tasks into the current or following sprints and to move or discard uncompleted tasks at the end of each sprint. Things that Usually Work Clear Reaction(s) for every Action ⚡️ Tasks should follow a clear cycle, where at each moment it’s clear who’s expected to interact with it. Sticking to this rule allows for: Since tasks are always owned by the person who’s expected to do specific action(s) with them in order to transfer ownership along the pipeline. Ownership By being clear what each person must do next allows for more mental capacity on the actual tasks. Clear personal pipelines By making it easier to identify issues along the pipeline early on and mitigating on them by talking directly with whoever is assigned at the time. Non-intrusive progress tracking Fibonacci estimations 📈 Estimating on how long tasks will take is incredibly prone to biased predictions. A system that I have seen greatly help with this is to assign a difficulty to each task based on a Fibonacci scale (a.k.a. Planning Poker). For example: : no action needed, 0 : < ½ hour, 1 : < 1 ½ hours, 2 : < ½ day, 3 : < 1 day, 5 : < 3 days, 8 : < 1 week 13 : < 2 weeks 20 Note: There shouldn’t be tasks over 8 or even 5 points. If any arise, they should be broken down into smaller tasks. Obsoleting the need for personal lists 📃 A golden indicator for when a management system is well integrated is when everyone uses it on a personal level for even the smallest tasks or side tasks. Of course, forcing people to use it is not how this is achieved. It’s by making sure the system is truly simple and convenient for personal use. Prioritization 🍱 It’s very common that the number of tasks that we want to fit into a sprint is overly ambitious, and that’s healthy if dealt with well.A very simple way to ensure that what’s important gets done is assigning priority to tasks. I have had a good experience with this system: : live bug, preventing core behaviour : live bug, not preventing core behaviour : needed for next release : needed the following release : nice to have Live 0 Live 1 Dev 1 Dev 2 Dev 3 Leader(s) / Delegator(s) 👨‍✈️ Leaders / Delegators are incredibly useful for: This allows developers to focus on their current task and not get distracted by the constant inflow of requests. Handling development requests and bug reports By helping developers pick the next task based on a number instead of it’s relevance to the business’s needs their deliverable increases. Prioritizing Tasks productivity **Helping Developers Understand the System**It’s normal and expected that developers will initially not understand the system and even deviate from using in afterwards.So, when leaders detect inconsistencies, they help the developer better align with the system or adapt the system to the developer. Predictable Communication 👁️‍🗨️ A common reason why people dislike management systems is that they have too much information. Very effective ways to reduce the noise are: **A pattern for creating and commenting on tasks**Having a common format makes texts much easier to absorb. example: when moving a task from done to QA a comment should be left explaining what should be tested. Expected comments on task state changes Concluding Effective project management results in: greater output, a collective feeling of accomplishment, and less stress. It suffers from: unproductive meetings, hidden automations, objective based sprint cycles and eternal Backlogs. And benefits from: task lifecycles with clear from at , expected actions specific people each stage protection from biases in estimations, being appealing to developers, leader(s) / delegator (s) and reducing noise during commutation. Keep in touch I write in the interest of meeting people with whom to have interesting conversations with. So I would like nothing more than the get a message from you in the comments or on twitter ( ). @esperancaJS A little Thank You to those who I learned from I would not have learned many of these topics without having been blessed with getting to collaborate with great companies such as , , , and more recently, . Thank you, , Kent, , and for putting up with me and for all your wisdom. Beamery Altar.io Innowave NearSt Michael Daniel André Adam

Code Project Management Quick but Huge Wins

Key Out of Time by Andre Alice Norton, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . Baldies Baldies "Ohhhh!" Karara clutched at Ross, her breath coming in little gasps, giving vent to her fear and horror. They had not known what might come from this plan; certainly neither had foreseen the present chaos in the lagoon. Perhaps the broadcast energy of the enemy whipped the already vicious-tempered salkars into this insane fury. But now the moonlit water was beaten into foam as the creatures fought there, attacking each other with a ferocity neither Terran had witnessed before. Lights gleamed along the shore where the alien invaders must have been drawn by the clamor of the fighting marine reptiles. Somewhere in the heights above the beach of the lagoon a picked band of Rovers should now be making their way from the opposite side of Kyn Add under strict orders not to go into attack unless signaled. Whether the independent sea warriors would hold to that command was a question which had worried Ross from the first. Tino-rau and Taua in the waters to the seaward of the reef, the two Terrans on that barrier itself, and between them and the shore the wild melee of maddened salkars. Ross started. The sonic warning which had been pulsing steadily against his skin cut off sharply. The broadcast in the bay had been silenced! This was the time to move, but no swimmer could last in the lagoon itself. "Along the reef," Karara said. That would be the long way round, Ross knew, but the only one possible. He studied the cluster of lights ashore. Two or three figures moved there. Seemingly the attention of the aliens was well centered upon the battle still in progress in the lagoon. "Stay here!" he ordered the girl. Adjusting his mask, Ross dropped into the water, cutting away from the reef and then turning to swim parallel with it. Tino-rau matched him as he went, guiding Ross to a second break in the reef, toward the shore some distance from where the conflict of the salkars still made a hideous din in the night. The Terran waded in the shallows, stripping off his flippers and snapping them to his belt, letting his mask swing free on his chest. He angled toward the beach where the aliens had been. At least he was better armed for this than he had been when he had fronted the Rovers with only a diver's knife. From the Time Agent supplies he had taken the single hand weapon he had long ago found in the armory of the derelict spaceship. This could only be used sparingly, since they did not know how it could be recharged, and the secret of its beam still remained secret as far as Terran technicians were concerned. Ross worked his way to a curtain of underbrush from which he had a free view of the beach and the aliens. Three of them he counted, and they were Baldies, all right—taller and thinner than his own species, their bald heads gray-white, the upper dome of their skulls overshadowing the features on their pointed chinned faces. They all wore the skintight blue-purple-green suits of the space voyagers—suits which Ross knew of old were insulated and protective for their wearers, as well as a medium for keeping in touch with one another. Just as he, wearing one, had once been trailed over miles of wilderness. To him, all three of the invaders looked enough alike to have been stamped out from one pattern. And their movements suggested that they worked or went into action with drilled precision. They all faced seaward, holding tubes aimed at the salkar-infested lagoon. There was no sound of any explosion, but green spears of light struck at the scaled bodies plunging in the water. And where those beams struck, flesh seared. Methodically the trio raked the basin. But, Ross noted, those beams which had been steady at his first sighting, were now interrupted by flickers. One of the Baldies upended his tube, rapped its butt against a rock as if trying to correct a jamming. When the alien went into action once again his weapon flashed and failed. Within a matter of moments the other two were also finished. The lighted rods pushed into the sand, giving a glow to the scene, darkened as a fire might sink to embers. Power fading? An ungainly shape floundered out of the churned water, lumbered over the shale of the beach, its supple neck outstretched, its horned nose down for a gore-threatening charge. Ross had not realized that the salkars could operate out of what he thought was their natural element, but this wild-eyed dragon was plainly bent on reaching its tormentors. For a moment or two the Baldies continued to front the creature, almost, Ross thought, as if they could not believe that their weapons had failed them. Then they broke and ran back to the fairing which they had taken with such contemptuous ease. The salkar plowed along in their wake, but its movements grew more labored the farther it advanced, until at last it lay with only its head upraised, darting it back and forth, its fanged jaws well agape, voicing a coughing howl. Its plaint was answered from the water as a second of its kind wallowed ashore. A terrible wound had torn skin and flesh just behind its neck; yet still it came on, hissing and bubbling a battle challenge. It did not attack its fellow; instead it dragged its bulk past the first comer, on its way after the Baldies. The salkars continued to come ashore, two more, a third, a fourth, mangled and torn—pulling themselves as far as they could up the beach. To lie, facing inland, their necks weaving, their horned heads bobbing, their cries a frightful din. What had drawn them out of their preoccupation of battle among themselves into this attempt to reach the aliens, Ross could not determine. Unless the intelligence of the beasts was such that they had been able to connect the searing beams which the Baldies had turned on them so tellingly with the men on the beach, and had responded by striving to reach a common enemy. But no desire could give them the necessary energy to pull far ashore. Almost helplessly beached, they continued to dig into the yielding sand with their flippers in a vain effort to pursue the aliens. Ross skirted the clamoring barrier of salkars and headed for the fairing. A neck snapped about; a head was lowered in his direction. He smelled the rank stench of reptile combined with burned flesh. The nearest of the brutes must have scented the Terran in turn, as it was now trying vainly to edge around to cut across Ross's path. But it was completely outclassed on land, and the man dodged it easily. Three Baldies had fled this way. Yet Jazia had reported five had come out of the sea to take Kyn Add. Two were missing. Where? Had they remained in the fairing? Were they now in the sub? And that sub—what had happened to it? The broadcast had been cut off; he had seen the failure of the weapons and the shore lights. Might the sub have suffered from salkar attack? Though Ross could hardly believe that the beasts could wreck it. The Terran was traveling blindly, keeping well under cover of such brush as he could, knowing only that he must head inland. Under his feet the ground was rising, and he recalled the nature of this territory as Torgul and Jazia had pictured it for him. This had to be part of the ridge wall of the valley in which lay the buildings of the fairing. In these heights was the Shrine of Phutka where Jazia had hidden out. To the west now lay the Rover village, so he had to work his way left, downhill, in order to reach the hole where the Baldies had gone to ground. Ross made that progress with the stealth of a trained scout. Hawaika's moon, triple in size to Terra's companion, was up, and the landscape was sharply clear, with shadows well defined. The glow, weird to Terran eyes, added to the effect of being abroad in a nightmare, and the bellowing of the grounded salkars continued a devils' chorus. When the Rovers had put up the buildings of their fairing, they had cleared a series of small fields radiating outward from those structures. All of these were now covered with crops almost ready to harvest. The grain, if that Terran term could be applied to this Hawaikan product, was housed in long pods which dipped from shoulder-high bushes. And the pods were well equipped with horny projections which tore. A single try at making his way into one of those fields convinced Ross of the folly of such an advance. He sat back to nurse his scratched hands and survey the landscape. To go down a very tempting lane would be making himself a clear target for anyone in those buildings ahead. He had seen the flamers of the Baldies fail on the beach, but that did not mean the aliens were now weaponless. His best chance, Ross decided, was to circle north, come back down along the bed of a stream. And he was at the edge of that watercourse when a faint sound brought him to a frozen halt, weapon ready. "Rosss—" "Loketh!" "And Torgul and Vistur." This was the party from the opposite side of the island, gone expertly to earth. In the moonlight Ross could detect no sign of their presence, yet their voices sounded almost beside him. "They are in there, in the great hall." That was Torgul. "But no longer are there any lights." "Now—" An urgent exclamation drew their attention. Light below. But not the glow of the rods Ross had seen on the beach. This was the warm yellow-red of honest fire, bursting up, the flames growing higher as if being fed with frantic haste. Three figures were moving down there. Ross began to believe that there were only this trio ashore. He could sight no weapons in their hands, which did not necessarily mean they were unarmed. But the stream ran close behind the rear wall of one of the buildings, and Ross thought its bed could provide cover for a man who knew what he was doing. He pointed out as much to Torgul. "And if their magic works and you are drawn out to be killed?" The Rover captain came directly to the point. "That is a chance to be taken. But remember ... the magic of the Foanna at the sea gate did not work against me. Perhaps this won't either. Once, earlier, I won against it." "Have you then another hand to give to the fire as your defense?" That was Vistur. "But no man has the right to order another's battle challenge." "Just so," returned Ross sharply. "And this is a thing I have long been trained to do." He slid down into the stream bed. Approaching from this angle, the structures of the fairing were between him and the fire. So screened he reached a log wall, got to his feet, and edged along it. Then he witnessed a wild scene. The fire raged in great, sky-touching tongues. And already the roof of one of the Rover buildings smoldered. Why the aliens had built up such a conflagration, Ross could not guess. A signal designed to reach some distance? He did not doubt there was some urgent purpose. For the three were dragging in fuel with almost frenzied haste, bringing out of the Rover buildings bales of cloth to be ripped apart and whirled into the devouring flames, furniture, everything movable which would burn. There was one satisfaction. The Baldies were so intent upon this destruction that they kept no watch save that now and then one of them would run to the head of the path leading to the lagoon and listen as if he expected a salkar to come pounding up the slope. "They're ... they're rattled!" Ross could hardly believe it. The Baldies who had always occupied his mind and memory as practically invincible supermen were acting like badly frightened primitives! And when the enemy was so off balance you pushed—you pushed hard. Ross thumbed the button on the grip of the strange weapon. He sighted with deliberation and fired. The blue figure at the top of the path wilted, and for a long moment neither of his companions noted his collapse. Then one of them whirled and started for the limp body, his colleague running after him. Ross allowed them to reach his first victim before he fired the second and third time. All three lay quiet, but still Ross did not venture forth until he had counted off a dozen Terran seconds. Then he slipped forward keeping to cover until he came up to the bodies. The blue-clad shoulder had a flaccid feel under his hand as if the muscles could not control the flesh about them. Ross rolled the alien over, looked down in the bright light of the fire into the Baldy's wide-open eyes. Amazement—the Terran thought he could read that in the dead stare which answered his intent gaze—and then anger, a cold and deadly anger which chilled into ice. "Kill!" Ross slewed around, still down on one knee, to face the charge of a Rover. In the firelight the Hawaikan's eyes were blazing with fanatical hatred. He had his hooked sword ready to deliver a finishing stroke. The Terran blocked with a shoulder to meet the Rover's knees, threw him back. Then Ross landed on top of the fighting crewman, trying to pin the fellow to earth and avoid that recklessly slashing blade. "Loketh! Vistur!" Ross shouted as he struggled. More of the Rovers appeared from between the buildings, bearing down on the limp aliens and the two fighting men. Ross recognized the limping gait of Loketh using a branch to aid him into a running scuttle across the open. "Loketh—here!" The Hawaikan covered the last few feet in a dive which carried him into Ross and the Rover. "Hold him," the Terran ordered and had just time enough to throw himself between the Baldies and the rest of the crew. There was a snarling from the Rovers; and Ross, knowing their temper, was afraid he could not save the captives which they considered, fairly, their legitimate prey. He must depend upon the hope that there were one or two cooler heads among them with enough authority to restrain the would-be avengers. Otherwise he would have to beam them into helplessness. "Torgul!" he shouted. There was a break in the line of runners speeding for him. The big man lunging straight across could only be Vistur; the other, yelling orders, was Torgul. It would depend upon how much control the Captain had over his men. Ross scrambled to his feet. He had clicked on the beamer to its lowest frequency. It would not kill, but would render its victim temporarily paralyzed; and how long that state would continue Ross had no way of knowing. Tried on Terran laboratory animals, the time had varied from days to weeks. Vistur used the flat side of his war ax, clapping it against the foremost runners, setting his own bulk to impose a barrier. And now Torgul's orders appeared to be getting through, more and more of the men slacked, leaving a trio of hotheads, two of whom Vistur sent reeling with his fists. The Captain came up to Ross. "They are alive then?" He leaned over to inspect the Baldy the Terran had rolled on his back, assessing the alien's frozen stare with thoughtful measurement. "Yes, but they can not move." "Well enough." Torgul nodded. "They shall meet the Justice of Phutka after the Law. I think they will wish that they had been left to the boarding axes of angry men." "They are worth more alive than dead, Captain. Do you not wish to know why they have carried war to your people, how many of them there may yet be to attack—and other things? Also—" Ross nodded at the fire now catching the second building, "why have they built up that blaze? Is it a signal to others of their kind?" "Very well said. Yes, it would be well for us to learn such things. Nor will Phutka be jealous of the time we take to ask questions and get answers, many answers." He prodded the Baldy with the toe of his sea boot. "How long will they remain so? Your magic has a bite in it." Ross smiled. "Not my magic, Captain. This weapon was taken from one of their own ships. As to how long they will remain so—that I do not know." "Very well, we can take precautions." Under Torgul's orders the aliens were draped with capture nets like those Ross and Loketh had worn. The sea-grown plant adhered instantly, wet strands knitting in perfect restrainers as long as it was uncut. Having seen to that, Torgul ordered the excavation of Kyn Add. "As you say," he remarked to Ross, "that fire may well be a signal to bring down more of their kind. I think we have had the Favor of Phutka in this matter, but the prudent man stretches no favor of that kind too far. Also," he looked about him—"we have given to Phutka and the Shades our dead; there is nothing for us here now but hate and sorrow. In one day we have been broken from a clan of pride and ships to a handful of standardless men." "You will join some other clan?" Karara had come with Jazia to stand on the stone ledge chipped to form a base for a column bearing a strange, brooding-eyed head looking seaward. The Rover woman was superintending the freeing of the head from the column. At the Terran girl's question the Captain gazed down into the dreadful chaos of the valley. They could yet hear the roars of the dying salkars. The reptiles that had made their way to land had not withdrawn but still lay, some dead now, some with weaving heads reaching inland. And the whole of the fairing was ablaze with fire. "We are now blood-sworn men, Sea Maid. For such there is no clan. There is only the hunting and the kill. With the magic of Phutka perhaps we shall have a short hunt and a good kill." "There ... now ... so...." Jazia stepped back. The head which had faced the sea was lowered carefully to a wide strip of crimson-and-gold stuff she had brought from Torgul's ship. With her one usable hand the Rover woman drew the fabric about the carving, muffling it except for the eyes. Those were large ovals deeply carved, and in them Ross saw a glitter. Jewels set there? Yet, he had a queer, shivery feeling that something more than gems occupied those sockets—that he had actually been regarded for an instant of time, assessed and dismissed. "We go now." Jazia waved and Torgul sent men forward. They lifted the wrapped carving to a board carried between them and started downslope. Karara cried out and Ross looked around. The pillar which had supported the head was crumbling away, breaking into a rubble which cascaded across the stone ledge. Ross blinked—this must be an illusion, but he was too tired to be more than dully amazed as he became one of the procession returning to the ships. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Andre Alice Norton (2006). Key Out of Time. Urbana, Illinois: Project Gutenberg. Retrieved October 2022 https://www.gutenberg.org/cache/epub/19651/pg19651-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html .

Baldies

Photo by on Unsplash Drew Beamer What We’ll be Building: We will be building a machine learning model that will predict whether a candidate will or will not be hired based on his or her credentials. This is part one out of a three-part series we will be developing. This part is only concerned with developing the machine learning model. Introduction Prediction algorithms have evolved into a profitable source of solutions to our modern-day challenges as a result of thorough development processes, phases, and time. These machine learning algorithms have been a tremendous boost in dealing with various challenges in our timeline. The human resources (HR) department inside organizations and businesses definitely will appreciate these improvements. With a machine learning model trained to identify worthy candidates for a job, a huge chunk of errors and losses can be avoided by an organization. Machine learning prediction algorithms have come to stay and with more data, algorithms, and strategies being developed and refined I believe the future of business is a lot brighter. Prerequisite To properly digest this tutorial, a good understanding of the following tools is required. Python Anaconda What is a Predictive Machine Learning Algorithm? Firstly, let's discuss what machine learning is. To simply state, machine learning is a sub-field in the area of artificial intelligence saddled with the responsibility of making a machine intelligent through training on datasets. . This procedure is based upon some mathematical formulas and equations. These mathematical-based algorithms are geared to learn patterns and statistics from a well-defined dataset. Secondly, an algorithm is a step-by-step computational instruction designed to solve a problem Lastly, prediction is the ability to forecast outcomes. It's kind of what a prophet will do, however, while a prophet predicts by inspirations, a machine learning algorithm predicts by historic data. Building a Predictive Machine Learning Model To build a machine learning model, a machine learning algorithm must be used to learn the statistics and patterns buried within the dataset. Choosing a Development Environment To proceed with building a machine learning model, the appropriate development environment must be put in place. Like before the building of a house, a good environment that will allow your machine learning code to run is required. Choosing a machine learning development environment is predicated on your familiarity with a machine learning programing language. The two most popular programming languages for doing this is and . Python R For this article, we'll be using the Python programing language, however, choosing a programing language alone is not enough, a development environment is also needed for this task. Anaconda does this very well. Anaconda is a data science and machine learning development package. It comes shipped with all the necessary libraries, programs, and configurations that will get you developing your model in a few minutes. For installation guidance, visit their and documentation page, or you can visit my for the installation procedure. website Git Repo To set up the environment using the following steps: 1. Head to [Anaconda](https://www.anaconda.com/products/individual) Site
2. On the page download Anaconda for your operating system.
3. Install the Anaconda package to your computer.
4. Visit [https://downgit.github.io/](https://downgit.github.io/).
5. Paste `https://github.com/Daltonic/predictive/tree/main/model` in the field thereon and click on the download button.
6. Unzip **model.zip** and place contents in a unique folder. You should have something like this. You should have a structure such as the one in the image below. Next, Open Anaconda Navigator and lunch Spyder from the options. You should have a structure such as the one in the image below. Good, let’s proceed to import the libraries we will need. Importing Libraries There are different kinds of Python libraries available on the web and each has its use and area of applicability. For what we're building, we will just need three libraries namely , , and . Pandas Numpy Scikit-learn # Importing Libraries
import pandas as pd
import numpy as np Now, create a folder called and within it create another folder called and save the code on the Spyder Editor in the folder you last created . “machine-learning” “predictive” (predictive) The rest of the libraries we will be using will be imported as we go. Let’s proceed to import the dataset for this model. Copy the file named within the file to the folder called . See the image below for guidance. hireable.csv model.zip “predictive” Once you have it in the folder named predictive, proceed by pasting the code snippet below in the opened Spyder Editor. # Importing Dataset
dataset = pd.read_csv('hirable.csv') Here we used the Pandas library to read our CSV file into the program. By checking the variable explorer, here is how our dataset currently looks. We don’t need all the columns, we only need are the following columns. gender degree_p mba_p workex etest_p status We will proceed by doing some clean-up on the dataset. Paste the code snippet below in your Spyder code editor to have your data cleaned up. Cleaning up the Dataset # Cleaning up dataset
dataset = dataset.drop([
    "sl_no",
    "ssc_p",
    "ssc_b",
    "hsc_p",
    "hsc_b",
    "hsc_s",
    "specialisation",
    "salary",
    "degree_t"
], axis=1)
dataset = dataset.rename(columns = {'degree_p': 'bsc', 'mba_p': 'msc'})
dataset['gender'] = dataset.gender.replace(['M', 'F'], [1, 2])
dataset['workex'] = dataset.workex.replace(['Yes', 'No'], [1, 0])
dataset['status'] = dataset.status.replace(['Placed', 'Not Placed'], [1, 0]) The above codes will drop most of the unnecessary columns, rename some hard-to-read columns and apply the suiting values to each row of the column. The and scores are not in the format required for this model. We want to use systems and not percentages for those two columns. Let’s create a function to downscale these numbers to the appropriate values using the snippet below. BSc MSc CGPA # Downscalling Method For BSc & MSc grades
def downscale(score):
    return score/10/2
degrees = ['bsc', 'msc']
for col in degrees:
    dataset[col] = downscale(dataset[col]) Great work, now let’s proceed to separate the dataset into dependent and independent variables. Separating Dataset into Variables This is a crucial step in developing a predictive machine learning model. We want to see how can tell us about . The objective in separating the dataset into and variables is to see the relationship and how affects the outcome of a, b, c, d z X(n…) Y X(n…) Y. While is the independent variable containing one or many columns of data, is an independent variable and it contains one column which is the outcome. X (n…) Y The above explanation can be implemented in Python using the Pandas package as seen in the code snippet below. # Separating into dependent and independent variables
X = dataset.drop(['status'], axis=1)
y = dataset.status Now we have X and Y variables containing the following columns. gender bsc workex etest_p msc Terrific, we have successfully done the separation, let’s go a step further to splitting these variables into training and testing sets. Splitting Variables into Training and Testing Sets This step is very important if we must build and train our model to learn from our dataset. We split data into training and testing sets so that our model can learn the statistics and patterns. Afterward, it will be subjected to testing by feeding it with the test dataset. This will tell us the extent of learning our model has attained from the training dataset. We will be using the to achieve this. The split will be in the ratio of , where of the dataset will be for training and will be for testing. See the codes below. Scikit-learn library splitter method 80% - 20% 80% 20% # Splitting dataset into trainig and testing
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y,train_size=0.8,random_state=1) What’s happening here is that is an entire module and it contains classes and methods. We use the method found in the module to split our variables. sklearn train_test_split model_selection Fitting Training Variables to a Machine Learning Algorithm Here is the part we give breath to our machine learning model. We are using the machine learning algorithm called “ ” of the sklearn library. RandomForestClassifier Without explaining much of the math behind this algorithm, this algorithm will learn from the training dataset and be able to perform classification and prediction based on the intelligence it has gathered from the training dataset. Observe the code snippet below. # Fitting with random forest model
from sklearn.ensemble import RandomForestClassifier
model=RandomForestClassifier(n_estimators=100)
model.fit(X_train,y_train) Impressive, we are almost done with building our model, let’s test this just created model and see how it’s performing. Model Prediction and Testing We can’t be so sure of the performance of our predictive machine learning model until it undergoes some testing. We will be using the and method of the library to check the accuracy report of our model just after we have tested it. The code block below implements this operation. classification_report metrics sklearn # Prediction and testing
y_pred=model.predict(X_test)

# Report and Accuracy Score
from sklearn import metrics
from sklearn.metrics import classification_report
print("Accuracy:",metrics.accuracy_score(y_test, y_pred))
print("Classification Report RF:\n",classification_report(y_test,y_pred)) By running the above code block, you should have a score similar to mine, see the image below. Our model has an accuracy score of about It ****was able to predict instances of of variable and instances of for variable . 86%. 76% truly negative values Y 92% truly positive values Y We can also proceed further to test it on an entirely new dataset with the following codes. # Model testing on new data
# [[gender, bsc, workex, etest_p, msc]]

# Sample 1
sample = np.array([[0, 2.9, 1, 78.50, 3.7]])
model.predict(sample)

# Sample 2
sample = np.array([[0, 2.9, 1, 78.50, 3.7]])
model.predict(sample) The above code block will produce the result showcased in the image below. As you can see from the above outputs, the first candidate when subjected to the model, predicted that he wouldn’t be hired due to his credentials, whereas the second candidate was hired. Now let’s finish up by saving our model for other usages. Saving Your Model To preserve your model for future use such as deploying to a production server we must save the model to a file. To store your model in a file, we use a package called . The below code snippet describes how to implement it. “Pickle” # Saving model
import pickle
pickle.dump(model, open('hireable.pkl', 'wb')) You can use the codes below to load up your data. This is not important for this tutorial but it will be vital for part two of this article which is to develop an API for communicating with this model. loaded_model = pickle.load(open('hireable.pkl', 'rb'))
result = loaded_model.score(X_test, y_test)
print(result) Congratulations!!! You have just completed one out of three-part series on building a machine learning predictive site with React and Python. You can get the source code for this project on my Git Repo . here Conclusion To conclude, handling the process of hiring an employee manually can be tedious, time-consuming, and error-prone. However, with predictive machine learning models trained to handle this sort of task, the job of hiring a new employee will be greatly simplified. As time goes, more companies and businesses will increasingly utilize more predictive machine learning models to solve their business problems such as the model we just built. About Author Gospel Darlington is a remote web developer, prolific with technologies such as , , , and . He takes a huge interest in the development of high-grade and responsive web applications. Fullstack VueJs Angular ReactJs API development Gospel Darlington currently works as a that teach other developers how to integrate software products into their personal projects. freelancer developing apps and writing tutorials He spends his free time on how to be successful in life. His hobbies include inventing new recipes, book writing, songwriting, and singing. You can reach me on , , , , or for any discussion. coaching young people Website LinkedIn Twitter Facebook GitHub Also published here.

Model Development: Build a Predictive Machine Learning Site With React and Python (Part 1)

We have equipped some of our with fixed beamers and more will follow. From time to time the desire is expressed to see whether you can ‘simply’ transmit a picture / video or the whole screen wirelessly from your smartphone or private laptop. Up to now we have provided a VGA / HDMI cable for our own devices. This usually works quite well, but this excludes smartphones, for example. That’s why we have been investigating and testing solutions for wireless presentation to a projector and screen for some time now, and whether they really are better and more reliable than a wired solution. classrooms Requirements Our requirements are as follows: Support of all current operating systems (Windows, macOS, Linux, Android, iOS) Possibility to mirror the screen easy to use reliable in everyday school life “Hijacking”, i. e. the screens / projectors should not be taken over by third parties (e. g. students) There are many HDMI wireless dongles that promise wireless presentation. In addition, there are the many different standards: Google’s Chromecast, Apples Airplay, Microsoft’s Wireless Display Adapter (Miracast). If only one operating system had to be supported, the selection would be quite large. I know schools that have equipped their with Apple TVs and give each colleague an iPad. This works quite well within this ecosystem, but we don’t have this budget 🙂 For this reason, there are very few options if you want to support Linux. classrooms Chromecast The is HDMI stick, which supports a large number of devices, actually everything a Chrome / Chromium Browser runs on. You can easily transfer single tabs, the whole screen or content from certain apps (e.g. Youtube App) to the projector or a screen. Chromecast Advantages Price (approx. 40$) Support of all major operating systems Connection with Ethernet adapter possible Disadvantages supports only WPA2 Personal, no WPA Enterprise no management platform to manage many Chromecasts no screen mirroring on some mobile platforms Generally speaking, it can be said that the Chromecast is designed for home use and therefore lacks some of the features that might be useful in a school or university network. Airtame is another HDMI dongle that promises very easy handling and has been specially designed for schools, companies and universities. As far as I know, Fedora is running on the stick itself. Airtame Advantages Support of all major operating systems Connection with Ethernet adapter possible real mirroring of the screen, no “casting”, i. e. Youtube videos are transferred by the computer in contrast to Chromecast, which downloads them from the Internet itself. supports all WLAN standards (also WPA Enterprise) Central platform to manage many Airtames Option to connect via PIN Disadvantages Conclusion Chromecast and Airtame are two options for wireless presentation. Airtame is actually the device you want for a school, but it has its price. Chromecast, on the other hand, is much cheaper, but does not offer some features that would be “nice-to-have”. We are currently testing the Chromecast and seeing how good and reliable it is in everyday school life and “multi-user operation”. Especially the simplicity of operation has to be tested, e.g. how this differs on the individual platforms. Which device do you use for wireless presentation? Originally published at openschoolsolutions.org . Sign up to our newsletter to get access to a FREE PDF with great open source apps for your classroom or follow @OpenSchoolZ on Twitter.

Wireless Presentation — What Are The Options?

It is incredible how far we’ve come in the coverage of events. In the past, I recorded my own talks as audio as not many conferences offered video recordings (and in the ). These days I find myself having not having to do this as most conferences and even meetups record talks. Faster upload speeds and simple, free hosting on and others made that possible. wrote about this as a good idea developer evangelism handbook YouTube And yet it is expensive and a lot of work to record and publish videos of your . And if you don’t make any money with them it is a bit of advertising for your event with a lot of overhead. That’s why it is a shame to see just how low the viewing numbers of some great conference videos are. When talking to conference organisers, I heard some astonishingly low numbers. The only thing to boost them seems to be to deliver them one after the other with dedicated social media promotion. Which, again, is a lot of extra effort. conference In order to see what would make people watch more conference talks, I took a quick Twitter poll: Here are the quick results of 344 votes: 29% chose shorter videos without Q&A 37% would like transcripts with timestamps 22% would like to have videos offline 12% wanted captions. I watch them offline, on an iPod in the gym or on planes and trains. Basically when I am not able to do anything else, they are a great way to spend your time and learn something. There are a few things to consider to make this worth my while though: I love watching conference talk videos. The talk needs to make sense to watch on a small screen. Lots of live code in a terminal with a 12px font isn’t. That is not to say these aren’t good talks. They are just not working as a video. The talk needs to be available offline (I use to download YouTube videos, some publishers on Vimeo offer downloads, Channel 9 always has the videos to download) YouTube DL The talk needs to be contained in itself — it is frustrating to hear references to things I should know or bits that happened at the same event I wasn’t part of. It is even more annoying to see a Q&A session where you wait for the mic to arrive for ages or the presenter answering without me knowing what the question is. I’ve written about the and I strongly believe that cutting a standard Q&A will result in more viewers and happier presenters. For starters, the videos will be shorter and it feels like less of an effort to watch the talk when it is 25 minutes instead of 45–50. Q&A part of videos in detail before At technical events I am OK with some of these annoyances. After all, it is more important to entertain the audience at the event. And it is amazing when presenters take the time and effort to see other talks and reference them. However, there is a lot of benefit to consider the quality and consumption of a recording, too. Having recordings of conference talks is an amazing gift to the community. People who can’t afford to go to events or even can’t afford to travel can still stay up-to-date and learn about topics to deep dive into by watching videos. Easy to consume, short and to the point videos can be a great way to increase the diversity of our market. “You are here to talk to the online audience” When I spoke at some TEDx events, this was the advice of the speaking coaches and organisers. TED is a known brand to have high quality online content. And it is almost unaffordable to go to the main TED events. Which makes this advice kind of odd, but their success online shows that there are on to something. TED talks are much shorter than the average conference talk. They are more performance than presentation. And they come with transcripts and are downloadable. Now, we can’t have only these kind of talks at events. But maybe it is a good plan to do some editing on the recordings and turn them into more of an experience than a record of what happened on stage that gets delivered as soon as possible. This means extra work and is some overhead, for sure. But I wonder how much of it could be automated already. In addition to the poll results there were some other good points in the comments on Twitter and . Facebook Less of the speaker upper body and more of the slides. Or slides to download. Also, speakers who pace themselves to sound good at 1.5x speed. It seems to be pretty common by people who spend time watching talks to speed them up. This is an interesting concept. Good editing between slides and presenter was a wish a lot of people had. It shouldn’t be hard to publish the slides along with the video, and something presenters should consider doing more. Not on the list but “editing” plus a solid couple of paragraphs of what the talk covers and why I should or shouldn’t watch it. This is another easy thing for presenters to do. We’re always asked to offer a description and title of the talk that should zing and get people excited. Providing a second one that is more descriptive to use with the video isn’t that much overhead. For English spokers, most of the conferences, no problem. But for non English spokers, massive failure. Reading is really more easy trying to listen and understand. Some guys speaks really fast. So I can’t understand talks. This is a common problem and a presenter skill to work on. Being understandable by non-native speakers is a huge opportunity. So, some pacing and avoiding slang references are always a good idea. The possibility to download the videos on tablets, smartphones and laptops so I can see them during commuting time Offering videos to download should be not too hard. If you’re not planning to sell them anyways, why not? Also offline availabilty with chapter marks/timestamps. I’ll vote for transcripts for skim reading to get to the gold nuggets. But sometimes a good speaker is an enjoyable 50min experience. I’d rather read transcripts. I never get blocks of quiet. Links to slides to follow along, or (even better) closed captions so I can play them muted If they were shorter and had a PowerPoint with main points to download after This, of course, is the big one. A lot of people asked for transcripts, chapters and time stamps. Either for accessibility reasons or just because it is easier to skim and jump to what is important to you. This costs time and effort. And here we have a Catch-22: if not many people watch the recordings of an event, conference organisers and companies don’t want to spend that time and effort. Manual transcription, editing and captioning isn’t cheap. The good news is that automated transcription has gone leaps and bounds in the last years. With the need to have voice commands on mobiles and home appliances a lot of companies concentrated on getting this much better than it used to be. YouTube has for free. Most cloud providers offer video insights. automated captions with editing functionality One service that blew me away is . VideoIndexer (Yes, this is by the company I work for, but it came as a surprise to me that this offer brings together many machine learning APIs in a simple interface.) Using VideoIndexer, you not only generate an editable time-stamped transcript, but you also get emotion recognition, image to text conversion of video content, speaker recognition and keyword extraction. That way you to offer an interface that allows people to jump where they want to without having to scrub through the video. I’d love to see more offers like these and I am sure there are quite a few out there already in use by big TV companies and sports broadcasters. If you know of any good ones, please comment here. Summary All in all I am grateful to have the opportunity to watch talks of events I couldn’t be at and I’m making an effort to be a better online citizen by providing better descriptions and be more aware of how what I am saying can be consumed as a video afterwards. My favourite quote in the comments was from Tessa Mero: Would be fun watching it with someone so we can discuss the content during/after the video. Need social engagement to make learning more fun. Videos of talks are a great opportunity to learn something and have fun with your colleagues in the office. Pick a room, set up a machine connected to the beamer, get some snacks in, watch the talk and discuss how it applies to your work afterwards. Conference organisers spend a lot of effort to record talks, presenters put a lot in to make the talk exciting and educational. And you can benefit from all of that for free. Permalink: https://christianheilmann.com/2017/07/26/how-can-we-make-…onference-videos/ ‎

BEAMERY

beamery.com

201-500 employees

Since 2014

COMPANY RANKING

BEAMERY

EVERGREEN INDEX #2185

Representing this company? Click here to claim and customize this page!

Beamery WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!

BEAMERY

beamery.com

201-500 employees

Since 2014

COMPANY RANKING

BEAMERY

EVERGREEN INDEX #2185

RELATED COMPANIESRANK

STORIES ABOUT BEAMERY

Representing this company? Click here to claim and customize this page!

Beamery WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!