vigneshbalaji.dev @vigneshbaladev
Passionate software engineer @Walmart | Talks about Full stack development, AI & ML | Free coaching | Ex @EY_US with 8 years work experience vigneshbalaji.dev Bangalore Joined November 2015-
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medium.com/@vigneshwiki07ā¦ latest interview experience with workday
Just got a message from Mr. Salim a topper from his NIT, working in Bangalore with a ā¹43.5 LPA package laid off last month. The company handed him just 3 monthsā severance. Last year alone, he paid ā¹11.22 lakhs in income tax. In just 5 years, over ā¹30 lakhs gone into theā¦
š Preparing for a senior frontend architecture interview? Check out this insightful guide on what top companies ask! š» #Frontend #TechInterview #CareerTips medium.com/@vigneshwiki07ā¦
š Preparing for a senior frontend architecture interview? Check out this insightful guide on what top companies ask! š» #Frontend #TechInterview #CareerTips medium.com/@vigneshwiki07ā¦
š Preparing for a senior frontend architecture interview? Check out this insightful guide on what top companies ask! š» #Frontend #TechInterview #CareerTips medium.com/@vigneshwiki07ā¦
šš šš»š“š¶š»š²š²šæš¶š»š“ šš²š®šæš»š¶š»š“ š„š¼š®š±šŗš®š½. š In my latest Newsletter episode I go deep into my AI Engineering Learning Roadmap. It is created with beginners in mind but can be easily adapted if you are proficient in some of the areas already. Find theā¦
š Level up your TypeScript skills! š» Check out these advanced TypeScript interview questions to ace your next tech interview. Read now: medium.com/@vigneshwiki07ā¦ #TypeScript #WebDev #TechInterview
Truuu with respect to current situation
Truuu with respect to current situation
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Missing data in ML 1ļøā£ MCAR (Missing Completely at Random): No relationship with data truly random. 2ļøā£ MAR (Missing at Random): Missingness depends on observed data, not the missing value itself. 3ļøā£ MNAR (Missing Not at Random): Missingness depends on the u observed value itself
Missing values in data? ā Use imputation (mean, median, mode, or predictive models) ā Try āmissingā as its own category for categorical features ā Test if dropping the rows/columns impacts performance Missing data isnāt the endāitās a challenge to solve! #FeatureEngineering
Feature engineering is the secret sauce of machine learning. š The right features can turn a mediocre model into a game-changer. Itās not just data cleaning; itās about crafting insights the model can truly learn from. #MachineLearning #FeatureEngineering
AI agents are changing the game! From automating tasks to providing personalized experiences, they're transforming industries. #AI #ArtificialIntelligence #Technology #Innovation #Future
š§®Probabilistic Algorithms These are based on probability and inferential statistics. Examples: Bayesian Networks, Hidden Markov Models. #LearnAI #LearnMachineLearning #100DaysOfMLCode #AIResources #AIForBeginners
If someone needs to learn next js in 2 hrs with hands on use this link nextjs.org/learn/dashboarā¦ #nextjs #reactjs
āEnhancing UI with edge rendering brings clarity and depth to interfaces, making designs more intuitive and visually appealing. #UIDesign #EdgeRendering #UserExperienceā
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