What to Do When Youre Caught Complaining About Your Boss on Facebook - CareerEnlightenment.com

Your manager has the weight of evidence. On the off chance that your manager affirms that you are going about as a solitary wolf to decry or insult another representative, your chief or the organization or brand, they should demonstrate their claims before you can be charged.You can't be arraigned for lawful online exercises you attempt while not on the job. On the off chance that posts you make during non-work hours are legitimate and are not identified with your work obligations, your manager can't hold you liable.Your business can't arraign you for expansive brush offenses. The NLRB has stood up against organizations that make too-wide online life use arrangements. On the off chance that your boss' web based life strategy incorporates such expansive language as to preclude most sorts of business related online discussions, their case may not stand up in court.Your state decides if your boss approaches your internet based life accounts. Six states have now made it illicit for manag ers or selection representatives to approach workers or possibility for their online life account passwords. Make certain to know your state's strategy on this issue before giving over your passwords to your employer.What to Do if Your Employer ProsecutesRegardless of whether your manager has a case or not, this may not keep them from getting a claim against you respect to your online life exercises. In the event that you do end up in a lawful circumstance that originates from business related discussions on the web, here is the thing that you ought to do.Contact the NLRB and disclose the circumstance to discover how solid your manager's case is.Speak with your boss to see whether there is an approach to determine the issue out of court.If something other than yourself is named, assemble with influenced others to talk about gathering representation.Locate a certified lawyer and recruit portrayal of your own to battle the charges.Ultimately, reacting speedily and conclusively to any internet based life related legitimate charges exacted against you by your boss can demonstrate honest intentions exertion to determine the circumstance rapidly and for everybody's benefit.Andy Chung utilizes his lords degree in management from Catholic University to head up the HR office at a moderate sized innovation organization. He likewise drives month to month lunch-and-learn workshops for representatives via web-based networking media manners and best practices.

Lookup by TalentBin Puts the Worlds Most Powerful People Search Engine on Your iPhone - Workology

Lookup by TalentBin Puts the World’s Most Powerful People Search Engine on Your iPhone New Mobile Application for iPhone Provides Access to Anyone’s Complete Real-Time Social Media Profile for Business Meetings, Events, or Social Gatherings San Francisco, CA â€" September 13, 2012 â€" TalentBin, the talent search engine that turbo-charges talent discovery across the web, today released Lookup, a free people search application for iPhone. Based on TalentBin’s people search engine technology widely used by corporate recruiters and hiring managers, Lookup allows consumers and business users to instantly look up and review publicly available online information about friends, colleagues, and business contacts in a single, easy-to-navigate social profile. With Lookup, users can discover more about the people they interact with on a daily basis or quickly learn about the professional and social interests of new contacts â€" based on timely and relevant online activity across social networks and online communities. “A complete social profile is like a personality â€" it is inherently multi-layered and reveals the true essence of a person. Looking at someone’s social media activity on a single site, like LinkedIn, for instance, only portrays a limited view of a person’s online personality. And digging through dozens of Google results is really tough on a mobile device,” explained Pete Kazanjy, co-founder of TalentBin. Lookup brings all the layers together, providing a more genuine and comprehensive profile â€" spanning both business and personal interests. Lookup by TalentBin provides an intuitive interface, which neatly aggregates a contact’s professional and social interests, schooling, and resume culled from their online posts and public profiles. This includes quick links to individual social media profiles on Facebook, Twitter, LinkedIn, Google Plus, Quora, Meetup, and even the US Patent Database. So whether you are on your way to a business meeting and need some more pertinent talking points, or you want to learn more about what piques someones interests, the Lookup app has you covered. “Our people search engine is roaring among corporate recruiters, due to its ability to create dynamic web resumes with social activity that would be laborious to find on their own. So with Lookup, we wanted to extend this same power to help business professionals and consumers alike, who could benefit from quick access to full social profiles in their daily lives,” Kazanjy added. About TalentBin Based in San Francisco, TalentBin is focused on turbo-charging talent acquisition for recruiters and hiring managers with its talent search engine, turning the web into a virtually endless talent sourcing database. By crawling targeted professional networking sites to pull together composite web resumes adding vital skills and expertise to traditionally sparse profiles â€" it helps make fast and definitive job matches. TalentBin has aggregated over 200 million professional profiles to date, and provides user access from anywhere as a web application, as a browser plug-in for Google Chrome, or within existing Recruiting CRM, ATS, or HRIS system through an API. With staff hailing from VMWare, eBay, and LinkedIn, TalentBin is funded by First Round Capital, Charles River Ventures, and Ron Conway’s SV Angel. For more, visit TalentBin.com.

The 9 Best Media and Entertainment Companies for Women

The 9 Best Media and Entertainment Companies for Women While media and diversion may appear to be male-overwhelmed, there are associations in the business endeavoring to change the norm. From supporting influential ladies in authority to offering the advantages new mothers need, these nine organizations are founding female-accommodating arrangements that improve media for ladies. Peruse on to perceive any reason why ladies love working for each organization and to get within scoop on their worker advantages. 1. Turner BroadcastingTheres parts for ladies to adore about working at Turner Broadcasting. Analysts on Fairygodboss state its an incredible organization that is very worker amicable, offering adaptable working alternatives and an easygoing climate. The organization has an abundance of advantages from a coordinating 401(k) to liberal PTO and parental leave and puts resources into the self-awareness and health of its kin. Turner has Make You Matter Weeks arrangement of classes, workshops and uncommon occasions for vocation advancemen t. Besides, Turner is an individual from Time Warners Fit Nation program, which urges representatives to accomplish their own wellness objectives through free or limited preparing and events.2. PlayStationWomen cannot get enough of working at PlayStation. Indeed, even inside the male-commanded gaming industry, analysts rate PlayStation profoundly for its solid female-accommodating projects. With regards to family benefits, PlayStation offers paid parental leave, appropriation help programs, senior consideration instructing, back-up childcare and liberal PTO. The organization likewise offers an abundance of well known budgetary advantages including a 4.5% 401(k) match and understudy advance reimbursement help. What's more, if youre a gamer yourself, representatives get limits on Sony Products and month to month PlayStation Network vouchers.3. The Walt Disney CompanyWorking at Disney is a fantasy for some ladies and all things considered. The Walt Disney Company gets rave audits on Fa irygodboss, with ladies saying they feel engaged by authority and get incredible resistance for family-related things. Past serious protection bundles and took care of time, Disney offers a large group of advantages to enables its workers to meet their own and expert objectives. For example, the organization pays 100% of educational cost for full-and low maintenance representatives at Disney Aspire arrange schools. It offers retirement plans and worker investment opportunities, alongside suburbanite help programs and on location childcare. Besides, the organization motivates representatives to give back with Disney VoluntEARS and props the enchantment up with complimentary amusement park admission.4. ViacomYouve unquestionably viewed a Viacom channel on TV, yet that is by all account not the only explanation its an incredible work environment. Ladies on Fairygodboss love that Viacom is family well disposed and differing, with a lot of ladies in initiative to support others. Alongsid e a steady domain, the organization offers a far reaching benefits bundle that assists everybody with performing at their best. On health programs, serious protection bundles and a 401(k) plan that would energize anybody, Viacom offers some stunning work-life balance programs. These incorporate appropriation benefits, back-up childcare, pet protection, educational cost repayment and more!5. NetflixNetflix is an energizing open door for ladies for such a large number of motivations to check. While analysts love the way of life of uniformity and adaptable workstyle that has been encouraged at the media organization, its help for female representatives doesnt stop there. Netflix offers a few family-accommodating advantages, including 100% paid parental leave, 10 days of back-up care, family shaping advantages that aid your richness, surrogacy or reception venture, and boundless downtime for full-time, salaried representatives. The organization offers other important advantages, as well . Salaried representatives get free drives among home and work by means of Uber, Lyft, or some other rideshare administration as frequently as they need. Additionally, Netflix offers a 401(k) boss match of somewhere in the range of 3.5 and 4%, and a special investment opportunity program.6. NBCUniversal Media, LLC.One Fairygodboss analyst stated: At NBCU, theres a solid, family-accommodating society and a very close network that rises above occupation classifications and verticals. What's more, that is clear from the companys duty to offering benefits that assist ladies with prevailing at work. NBCU offers four months of paid parental leave, alongside kid and grown-up care projects, and ripeness and appropriation help. It additionally offers worker benefits, 401(k) coordinating up to 6% of your qualified compensation, serious protection bundles and educational cost repayment. Additionally, if youre searching for an organization that organizes your wellbeing, you presumably need to i nvestigate NBCU; It has individual directing, nearby wellness places, unwinding spaces and adaptable work spaces.7. Revelation, inc.Women on Fairygodboss consider Discovery Communications an extraordinary, family-situated organization. Furthermore, its advantages clarify why. Analysts love the companys adaptable working alternatives, liberal PTO and youngster and senior consideration through BrightHorizons. Besides, Discoverys far reaching protection plans, 401(k) coordinating and other money related advantages give representatives another motivation to adore their work. All things considered, theyre very well compensated.8. Sony Pictures EntertainmentIts not simply the energizing undertakings that make Sony Pictures an incredible work environment. The organization is committed to cultivating an assorted and comprehensive organization culture, total with the assets workers need to succeed. Workers at Sony Pictures get thorough clinical and dental plans, representative help projects, learning and improvement openings, and cool advantages like film screenings and rec center memberships.9. XO Group, inc.Whether youre an ardent peruser of The Knot and The Bump or not, you ought to consider a vocation at XO Group. Ladies on Fairygodboss call it a positive and tolerating condition and an incredible work environment, however the advantages at XO Group go a long ways past its extraordinary organization culture. XO offers adaptable work courses of action, far reaching telecommuting days, exhaustive protection bundles and wellbeing programs. Furthermore, representatives at XO have the chance to demand help with the expense of their instructive costs.

Artificial Intelligence Algorithms For Beginners

Artificial Intelligence Algorithms For Beginners Artificial Intelligence Algorithms: All you need to know Back Home Categories Online Courses Mock Interviews Webinars NEW Community Write for Us Categories Artificial Intelligence AI vs Machine Learning vs Deep LearningMachine Learning AlgorithmsArtificial Intelligence TutorialWhat is Deep LearningDeep Learning TutorialInstall TensorFlowDeep Learning with PythonBackpropagationTensorFlow TutorialConvolutional Neural Network TutorialVIEW ALL BI and Visualization What is TableauTableau TutorialTableau Interview QuestionsWhat is InformaticaInformatica Interview QuestionsPower BI TutorialPower BI Interview QuestionsOLTP vs OLAPQlikView TutorialAdvanced Excel Formulas TutorialVIEW ALL Big Data What is HadoopHadoop ArchitectureHadoop TutorialHadoop Interview QuestionsHadoop EcosystemData Science vs Big Data vs Data AnalyticsWhat is Big DataMapReduce TutorialPig TutorialSpark TutorialSpark Interview QuestionsBig Data TutorialHive TutorialVIEW ALL Blockchain Blockchain TutorialWhat is BlockchainHyperledger FabricWhat Is EthereumEthereum TutorialB lockchain ApplicationsSolidity TutorialBlockchain ProgrammingHow Blockchain WorksVIEW ALL Cloud Computing What is AWSAWS TutorialAWS CertificationAzure Interview QuestionsAzure TutorialWhat Is Cloud ComputingWhat Is SalesforceIoT TutorialSalesforce TutorialSalesforce Interview QuestionsVIEW ALL Cyber Security Cloud SecurityWhat is CryptographyNmap TutorialSQL Injection AttacksHow To Install Kali LinuxHow to become an Ethical Hacker?Footprinting in Ethical HackingNetwork Scanning for Ethical HackingARP SpoofingApplication SecurityVIEW ALL Data Science Python Pandas TutorialWhat is Machine LearningMachine Learning TutorialMachine Learning ProjectsMachine Learning Interview QuestionsWhat Is Data ScienceSAS TutorialR TutorialData Science ProjectsHow to become a data scientistData Science Interview QuestionsData Scientist SalaryVIEW ALL Data Warehousing and ETL What is Data WarehouseDimension Table in Data WarehousingData Warehousing Interview QuestionsData warehouse architectureTalend T utorialTalend ETL ToolTalend Interview QuestionsFact Table and its TypesInformatica TransformationsInformatica TutorialVIEW ALL Databases What is MySQLMySQL Data TypesSQL JoinsSQL Data TypesWhat is MongoDBMongoDB Interview QuestionsMySQL TutorialSQL Interview QuestionsSQL CommandsMySQL Interview QuestionsVIEW ALL DevOps What is DevOpsDevOps vs AgileDevOps ToolsDevOps TutorialHow To Become A DevOps EngineerDevOps Interview QuestionsWhat Is DockerDocker TutorialDocker Interview QuestionsWhat Is ChefWhat Is KubernetesKubernetes TutorialVIEW ALL Front End Web Development What is JavaScript â€" All You Need To Know About JavaScriptJavaScript TutorialJavaScript Interview QuestionsJavaScript FrameworksAngular TutorialAngular Interview QuestionsWhat is REST API?React TutorialReact vs AngularjQuery TutorialNode TutorialReact Interview QuestionsVIEW ALL Mobile Development Android TutorialAndroid Interview QuestionsAndroid ArchitectureAndroid SQLite DatabaseProgramming Frameworks you need to knowAI vs Machine Learning vs Deep LearningA Comprehensive Guide To Artificial Intelligence With Python Introduction to Deep Learning What is Deep Learning? Getting Started With Deep LearningDeep Learning with Python : Beginners Guide to Deep LearningWhat Is A Neural Network? Introduction To Artificial Neural NetworksDeep Learning Tutorial : Artificial Intelligence Using Deep LearningPyTorch vs TensorFlow: Which Is The Better Framework? Neural Networks Deep Learning : Perceptron Learning AlgorithmNeural Network Tutorial â€" Multi Layer PerceptronBackpropagation â€" Algorithm For Training A Neural Network Tensorflow A Step By Step Guide to Install TensorFlowTensorFlow Tutorial â€" Deep Learning Using TensorFlowConvolutional Neural Network Tutorial (CNN) â€" Developing An Image Classifier In Python Using TensorFlowCapsule Neural Networks â€" Set of Nested Neural LayersObject Detection Tutorial in TensorFlow: Real-Time Object DetectionTensorFlow Image Classification : All you nee d to know about Building ClassifiersRecurrent Neural Networks (RNN) Tutorial | Analyzing Sequential Data Using TensorFlow In Python Dimensionality Reduction Autoencoders Tutorial : A Beginner's Guide to AutoencodersRestricted Boltzmann Machine Tutorial â€" Introduction to Deep Learning Concepts Most Frequently Asked Artificial Intelligence Interview Questions Artificial Intelligence Topics CoveredMachine Learning with Mahout (7 Blogs)TensorFlow Certification Training (39 Blogs)Artificial Intelligence and Machine Learning (19 Blogs)SEE MORE Artificial Intelligence Algorithms: All you need to know Last updated on May 14,2020 14.4K Views edureka Bookmark 3 / 12 Blog from Introduction to Artificial Intelligence Become a Certified Professional We can all agree that Artificial Intelligence has created a huge impact on the worlds economy and will continue to do so since were aiding its growth by producing an immeasurable amount of data. Thanks to the advancement in Artificial Int elligence Algorithms we can deal with such humungous data. In this blog post, you will understand the different Artificial Intelligence Algorithms and how they can be used to solve real-world problems.To get in-depth knowledge of Artificial Intelligence and Machine Learning, you can enroll for liveMachine Learning Engineer Master Programby Edureka with 24/7 support and lifetime access.Heres a list of topics that will be covered in this post:What Is Artificial Intelligence?What Is Machine Learning?Types Of Machine LearningTypes Of Problems Solved Using Artificial Intelligence AlgorithmsArtificial Intelligence AlgorithmsClassification AlgorithmsRegression AlgorithmsClustering AlgorithmsEnsemble Learning AlgorithmsWhat Is Artificial Intelligence?To simply put it, Artificial Intelligence is the science of getting machines to think and make decisions like human beings do.Since the development of complex Artificial Intelligence Algorithms, it has been able to accomplish this by creating m achines and robots that are applied in a wide range of fields including agriculture, healthcare, robotics, marketing, business analytics and many more.Before we move any further lets try to understand what Machine Learning is and how does it is related to AI.What Is Machine Learning?Generally, an algorithm takes some input and uses mathematics and logic to produce the output. In stark contrast, an Artificial Intelligence Algorithm takes a combination of both inputs and outputs simultaneously in order to learn the data and produce outputs when given new inputs. This process of making machines learn from data is what we call MachineLearning.Artificial Intelligence Algorithm Artificial Intelligence Algorithms EdurekaMachine Learning is a sub-field of Artificial Intelligence, where we try to bring AI into the equation by learning the input data.If youre curious to learn more about Machine Learning, give the following blogs a read:Introduction To Machine Learning: All You Need To Know About Machine LearningMachine Learning Tutorial for BeginnersMachine Learning AlgorithmsMachines can follow different approaches to learn depending on the data set and the problem that is being solved. In the below section well understand the different ways in which machines can learn.Types Of Machine LearningMachine Learning can be done in the following ways:Supervised LearningUnsupervised LearningReinforcement LearningEnsemble LearningLets briefly understand the idea behind each type of Machine Learning.What Is Supervised Learning?In Supervised Learning, as the name rightly suggests, it involves making the algorithm learn the data while providing the correct answers or the labels to the data. This essentially means that the classes or the values to be predicted are known and well defined for the algorithm from the very beginning.What Is Unsupervised Learning?The other class falls under Unsupervised Learning, where, unlike supervised methods the algorithm doesnt have correct answe rs or any answers at all, it is up to the algorithms discretion to bring together similar data and understand it.What Is Reinforcement Learning?Along with these two prominent classes, we also have a third class, called Reinforcement Learning. Just as children are generally reinforced certain ideas, principles by either rewarding them when doing the right thing or punishing upon doing something wrong, in Reinforcement Learning, there are rewards given to the algorithm upon every correct prediction thus driving the accuracy higher up.Heres a short video recorded by our Machine Learning experts. This will help you understand the difference between Supervised, Unsupervised and Reinforcement learning.Supervised vs Unsupervised vs Reinforcement LearningThe differences between Supervised vs Unsupervised vs Reinforcement learning are discussed in this video.What Is Ensemble Learning?While the above three classes cover most fields comprehensively, we sometimes still land into the issue of ha ving to bump up the performance of our model. In such cases it might make sense, to use ensemble methods (explained later) to get the accuracy higher up.Now lets understand how Artificial Intelligence algorithms can be used to solve different types of problems.Types Of Problems Solved Using Artificial Intelligence AlgorithmsAlgorithms in each category, in essence, perform the same task of predicting outputs given unknown inputs, however, here data is the key driver when it comes to picking the right algorithm.What follows is an outline of categories of Machine Learning problems with a brief overview of the same:ClassificationRegressionClusteringHeres a table that effectively differentiates each of these categories of problems.Type Of Problems Solved Using AI Artificial Intelligence Algorithms EdurekaFor each category of tasks, we can use specific algorithms. In the below section youll understand how a category of algorithms can be used as a solution to complex problems.Artificial Intelligence AlgorithmsAs mentioned above, different Artificial Intelligence algorithms can be used to solve a category of problems. In the below section well see the different types of algorithms that fall under Classification, Regression and Clustering problems.Classification AlgorithmsClassification, as the name suggests is the act of dividing the dependent variable (the one we try to predict) into classes and then predict a class for a given input. It falls into the category of Supervised Machine Learning, where the data set needs to have the classes, to begin with. Thus, classification comes into play at any place where we need to predict an outcome, from a set number of fixed, predefined outcomes.Classification uses an array of algorithms, a few of them listed belowNaive BayesDecision TreeRandom ForestLogistic RegressionSupport Vector MachinesK Nearest NeighboursLet us break them down and see where they fit in when it comes to application.Naive BayesNaive Bayes algorithm follo ws the Bayes theorem, which unlike all the other algorithms in this list, follows a probabilistic approach. This essentially means, that instead of jumping straight into the data, the algorithm has a set of prior probabilities set for each of the classes for your target.Once you feed in the data, the algorithm updates these prior probabilities to form something known as the posterior probability. Hence this can be extremely useful in cases where you need to predict whether your input belongs to either a given list of n classes or does it not belong to any of them. This can be possible using a probabilistic approach mainly because the probabilities thrown for all the n classes will be quite low.Let us try to understand this with an example, of a person playing golf, depending on factors like the weather outside.We first try to generate the frequencies with which certain events occur, in this case, we try to find frequencies of the person playing golf if its sunny, rainy, etc outside. Naive Bayes Artificial Intelligence Algorithms EdurekaUsing these frequencies we generate our apriori or initial probabilities (eg, the probability of overcast is 0.29 while the general probability of playing is 0.64)Next up, we generate the posterior probabilities, where we try to answer questions like what would be the probability of it being sunny outside and the person would play golf?We use the Bayesian formula here,P(Yes | Sunny) = P( Sunny | Yes) * P(Yes) / P (Sunny) Here we have P (Sunny |Yes) = 3/9 = 0.33, P(Sunny) = 5/14 = 0.36, P( Yes)= 9/14 = 0.64You can go through this A Comprehensive Guide To Naive Bayes blog to help you understand the math behind Naive Bayes.Decision TreeThe Decision Tree can essentially be summarized as a flowchart-like tree structure where each external node denotes a test on an attribute and each branch represents the outcome of that test. The leaf nodes contain the actual predicted labels. We start from the root of the tree and keep comparing at tribute values until we reach a leaf node.Decision Trees Artificial Intelligence Algorithms EdurekaWe use this classifier when handling high dimensional data and when little time has been spent behind data preparation. However, a word of caution they tend to overfit and are prone to change drastically even with slight nuances in the training data.You can through these blogs to learn more about Decision Trees:A Complete Guide On Decision Tree AlgorithmDecision Tree: How To Create A Perfect Decision Tree?Random ForestThink of this as a committee of Decision Trees, where each decision tree has been fed a subset of the attributes of data and predicts on the basis of that subset. The average of the votes of all decision trees are taken into account and the answer is given.An advantage of using Random Forest is that it alleviates the problem of overfitting which was present in a standalone decision tree, leading to a much more robust and accurate classifier.Random Forest Artificial In telligence Algorithms EdurekaAs we can see in the above image, we have 5 decision trees trying to classify a color. Here 3 of these 5 decision trees predict blue and two have different outputs, namely green and red. In this case, we take the average of all the outputs, which gives blue as the highest weightage.Heres a blog on Random Forest Classifier that will help you understand the working of Random forest algorithm and how it can be used to solve real-world problems.Logistic RegressionIts a go-to method mainly for binary classification tasks. The term logistic comes from the logit function that is used in this method of classification. The logistic function, also called as the sigmoid function is an S-shaped curve that can take any real-valued number and map it between 0 and 1 but never exactly at those limits.Logistic Regression Artificial Intelligence Algorithms EdurekaLets assume that your little brother is trying to get into grad school, and you want to predict whether hel l get admitted in his dream establishment. So, based on his CGPA and the past data, you can use Logistic Regression to foresee the outcome.Logistic Regression allows you to analyze a set of variables and predict a categorical outcome. Since here we need to predict whether he will get into the school or not, which is a classification problem, logistic regression would be ideal.Logistic Regression is used to predict house values, customer lifetime value in the insurance sector, etc.Support Vector MachineAn SVM is unique, in the sense that it tries to sort the data with the margins between two classes as far apart as possible. This is called maximum margin separation.Another thing to take note of here is the fact that SVMs take into account only the support vectors while plotting the hyperplane, unlike linear regression which uses the entire dataset for that purpose. This makes SVMs quite useful in situations when data is in high dimensions.Lets try to understand this with an example. In the below figure we have to classify data points into two different classes (squares and triangles).Support Vector Machine Artificial Intelligence Algorithms EdurekaSo, you start off by drawing a random hyperplane and then you check the distance between the hyperplane and the closest data points from each class. These closest data points to the hyperplane are known as Support vectors. And thats where the name comes from, Support Vector Machine.The hyperplane is drawn based on these support vectors and an optimum hyperplane will have a maximum distance from each of the support vectors. And this distance between the hyperplane and the support vectors is known as the margin.To sum it up, SVM is used to classify data by using a hyperplane, such that the distance between the hyperplane and the support vectors is maximum.To learn more about SVM, you can go through this, Using SVM To Predict Heart Diseases blog.K Nearest NeighborsKNN is a non-parametric (here non-parametric is just a fancy term which essentially means that KNN does not make any assumptions on the underlying data distribution), lazy learning algorithm (here lazy means that the training phase is fairly short).Its purpose is to use a whole bunch of data points separated into several classes to predict the classification of a new sample point.The following points serve as an overview of the general working of the algorithm:A positive integer N is specified, along with a new sampleWe select the N entries in our database which are closest to the new sampleWe find the most common classification of these entriesThis is the classification we give to the new sampleHowever, there are some downsides to using KNN. These downsides mainly revolve around the fact that KNN works on storing the entire dataset and comparing new points to existing ones. This means that the storage space increases as our training set increases. This also means that the estimation time increases in proportion to the number of trainin g points.The following blogs will help you understand how the KNN algorithm works in depth:A Practical Implementation Of KNN Algorithm In RK-Nearest Neighbors Algorithm Using PythonNow lets understand how regression problems can be solved by using regression algorithms.Regression AlgorithmsIn the case of regression problems, the output is a continuous quantity. Meaning that we can use regression algorithms in cases where the target variable is a continuous variable. It falls into the category of Supervised Machine Learning, where the data set needs to have the labels, to begin with.Linear RegressionLinear Regression is the most simple and effective regression algorithm. It is utilized to gauge genuine qualities (cost of houses, number of calls, all out deals and so forth.) in view of the consistent variable(s). Here, we build up a connection between free and ward factors by fitting the best line. This best fit line is known as regression line and spoken to by a direct condition Y= a *X + b.Linear Regression Artificial Intelligence Algorithms EdurekaLet us take a simple example here to understand linear regression.Consider that you are given the challenge to estimate an unknown persons weight by just looking at them. With no other values in hand, this might look like a fairly difficult task, however using your past experience you know that generally speaking the taller someone is, the heavier they are compared to a shorter person of the same build. This is linear regression, in actuality!However, linear regression is best used in approaches involving a low number of dimensions. Also, not every problem is linearly separable.Some of the most popular applications of Linear regression are in financial portfolio prediction, salary forecasting, real estate predictions and in traffic in arriving at ETAsNow lets discuss how clustering problems can be solved by using the K-means algorithm. Before that, lets understand what clustering is.Clustering AlgorithmsThe basic idea behind clustering is to assign the input into two or more clusters based on feature similarity. It falls into the category of Unsupervised Machine Learning, where the algorithm learns the patterns and useful insights from data without any guidance (labeled data set).For example, clustering viewers into similar groups based on their interests, age, geography, etc can be done by using Unsupervised Learning algorithms like K-Means Clustering.K-Means ClusteringK-means is probably the simplest unsupervised learning approach. The idea here is to gather similar data points together and bind them together in the form of a cluster. It does this by calculating the centroid of the group of data points.To carry out effective clustering, k-means evaluates the distance between each point from the centroid of the cluster. Depending on the distance between the data point and the centroid, the data is assigned to the closest cluster. The goal of clustering is to determine the intrinsic grouping in a set of unlabelled data.K-means Artificial Intelligence Algorithms EdurekaThe K in K-means stands for the number of clusters formed. The number of clusters (basically the number of classes in which your new instances of data can fall into) is determined by the user.K-means is used majorly in cases where the data set has points which are distinct and well separated from each other, otherwise, the clusters wont be far apart, rendering them inaccurate. Also, K-means should be avoided in cases where the data set contains a high amount of outliers or the data set is non-linear.So that was a brief about K-means algorithm, to learn more you can go through this content recorded by our Machine Learning experts.K Means Clustering Algorithm | EdurekaIn this video, you learn the concepts of K-Means clustering and its implementation using python.Ensemble Learning AlgorithmsIn cases where data is of abundance and prediction precision is of high value, boosting algorithms come into the pict ure.Consider the scenario, you have a decision tree trained on a data set along with a whole bunch of hyperparameter tuning already performed, however, the final accuracy is still slightly off than youd like. In this case, while it might seem that you have run out of possible things to try, ensemble learning comes to the rescue.Ensemble Learning Artificial Intelligence Algorithms EdurekaYou have two different ways in which you can use ensemble learning, in this case, to bump up your accuracy. Let us say your decision tree was failing on a set of input test values, what you do now is, to train a new decision tree model and give a higher weighting to those input test values that your previous model struggled with. This is also called as Boosting, where our initial tree can be formally stated as a weak learner, and the mistakes caused by that model pave way for a better and stronger model.Another way to approach this is by simply training a whole bunch of trees at once (this can be d one fairly quickly and in a parallel fashion) and then taking outputs from each tree and averaging them out. So this way, if after training 10 trees, lets say 6 trees reply positive to input and 4 trees reply negative, the output you consider is positive. This is formally known as Bagging.They are used to reduce the bias and variance in supervised learning techniques. There are a host of boosting algorithms available, a few of them discussed below:Gradient BoostingGradient Boosting is a boosting algorithm used when we deal with plenty of data to make a prediction with high prediction power. It combines multiple weak or average predictors to build strong predictor. These boosting algorithms are heavily used to refine the models in data science competitions.Here, we consider an optimal or best model, so essentially our model is at some distance from that optimal model. What we now do is, use gradient mathematics and try to get our model closer to the optimal space.XGBoostPertaining to its extremely high predictive power, XGBoost is one of the go-to algorithms when it comes to increasing accuracy as it contains both linear tree learning algorithms making it 10 times faster than most boosting techniques.It is the holy grail algorithm when it comes to hackathons, it is no wonder CERN used it in the model for classification of signals from the Large Hadron Collider.If you want to learn more about Boosting Machine Learning, you can go through this, Comprehensive Guide To Boosting Machine Learning Algorithms blog.So with this, we come to an end of this Artificial Intelligence Algorithms blog. If you wish to learn more about Artificial Intelligence, you can give these blogs a read:Artificial Intelligence What It Is And How Is It Useful?A Comprehensive Guide To Artificial Intelligence With PythonArtificial Intelligence Tutorial: All you need to know about AIAI vs Machine Learning vs Deep LearningAI Applications: Top 10 Real World Artificial Intelligence ApplicationsIf you wish to enroll for a complete course on Artificial Intelligence and Machine Learning, Edureka has a specially curatedMachine Learning Engineer Master Programthat will make you proficient in techniques like Supervised Learning, Unsupervised Learning, and Natural Language Processing. It includes training on the latest advancements and technical approaches in Artificial Intelligence Machine Learning such as Deep Learning, Graphical Models and Reinforcement Learning.Recommended videos for you Introduction to Mahout Watch Now Deep Learning Tutorial Deep Learning With TensorFlow Watch Now What Is Deep Learning Deep Learning Simplified Watch NowRecommended blogs for you What is Production System in Artificial Intelligence? Read Article Deep Learning Tutorial : Artificial Intelligence Using Deep Learning Read Article Top 10 Benefits Of Artificial Intelligence Read Article A Step By Step Guide to Install TensorFlow Read Article Most Frequently Asked Artificial Intelligence Interview Questions Read Article Artificial Intelligence Algorithms: All you need to know Read Article Theano vs TensorFlow : A Quick Comparision of Frameworks Read Article Fuzzy K-Means Clustering in Mahout Read Article PyTorch Tutorial Implementing Deep Neural Networks Using PyTorch Read Article What is the A* Algorithm and How does it work? Read Article What Are The Prerequisites For Machine Learning? 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Professional Resume Writing 101-11

Professional Resume Writing 101-11 Professional Resume Writing Time Gaps Most professional resume styles include achronological list of jobs beginningwith the most recent and progressing backwards over a reasonable number of years. Employment dates are prominent soit is important that there are no time gaps between positions. Some minor time gaps will not need to be addressed as you will not specify the month on your resume, only the year.For those time gaps that cannot be ignored,an explanation will be required to fill in the dateson your resume.Do not think you can hide it or that it will be ignored.It will be recognized asa questionable, unexplained time gap.It may be reason enough for the decision maker to eliminate you from the competition. Filling in the time gap with an explanation willbe receivedmore positively as shown in the following two examples: 1. You were laid off from a position and decided to take that opportunity to renovate your home.Be positive!You made a decision and stuck to it even though your husbandwantedyou back to work and your children were not happy with the major intrusionto their living quarters. You displayed the drive, confidence and talents necessary to successfully complete the job. Home Renovations, Contractor 2002 -2003 Negotiated contracts withall subcontractors to renovate and restore 50 year old, 4 bedroom home. Monitored schedules tomeet all time lines with plumbers and electricians. Completed all design work, purchasing of all materials and participated full time with painting and minor renovations. Saved$50,000by independently subcontracting thework. 2. After having a second baby you decided to take time out of your job searchto stay homeand raise your children on a full time basis. Be positive!This was your decision to make and despite pressure fromsome of your friends who chose to work full time and raise their children,you stood by your decision.You provided exceptional care for your children andbecameinvolved in a variety ofeducational programs and volunteer work. Home, Full time Mother 2004 2007 Provided full time care for my two children. Participated in a variety of educational programs for under school age children. Volunteered time to support many community initiatives. Recognized by the community for exceptional fund raising efforts to support the purchase of new playground equipment. Saved$40,000 in full-time day care costs by taking time out of the workforce. If you are concerned about too many time gaps orare an obvious job-hopper, you may want to decide against a chronological resume as it will only accentuate theemployment dates.By choosing a functional style resume, you will be able to focus onyour skills rather than the vast number of individual jobs. Do you need professional resume advice? Would you be interested in a free resume critique froma Certified Professional Resume Writer?Call 905-825-0490 oremail your resume.