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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

IIT Grad’s AI-Generated Cover Letter Leaves Everyone in Stitches

K.C. Sabreena Basheer
K.C. Sabreena Basheer Last Updated : 17 Jul, 2023
3 min read

In a comical turn of events, an IIT (Indian Institute of Technology) graduate’s attempt to leverage artificial intelligence for writing a cover letter resulted in an epic fail. The applicant, named Akhil, forgot to remove some technical jargon from the AI-generated letter before sending it out. The incident quickly made its way to Twitter, where users couldn’t help but laugh at the mishap. Let’s dive into this amusing story and see how the online community reacted to the unintentional blunder.

Also Read: How to Make a Winning Machine Learning Resume?

AI-Powered Cover Letter Fiasco: Akhil’s Hilarious Mishap

Akhil, an ambitious job seeker & IIT graduate, experimented with artificial intelligence to craft a cover letter to showcase his skills and expertise. Little did he know that this attempt would backfire hilariously. In his enthusiasm to impress potential employers, Akhil neglected to remove some technical words from the AI-generated cover letter, leading to a rather amusing blunder that garnered widespread attention on social media.

Also Read: How Machine Learning is Used on Social Media Platforms in 2023?

An IIT graduate forgot to remove some technical jargon from his AI-generated cover letter before sending it out, giving Twitter a field day!

The Twitter Share: Unveiling Akhil’s Techno-Gaffe

The incident came to light when a Twitter user stumbled upon Akhil’s AI-generated cover letter and couldn’t resist sharing the screenshot with the online community. The cover letter prominently displayed the words “[company name,fallback=]” — a clear sign of the technological mishap that had occurred. As the tweet went viral, netizens eagerly joined in to revel in the humorous mishap, making it a trending topic on various social media platforms.

A Peek into the AI-Generated Cover Letter

The IIT graduate’s AI-generated cover letter is intended to impress potential employers by highlighting his technical prowess. The letter boasted about his expertise in developing core microservices capable of handling an impressive 25,000 requests per minute. While the intention was undoubtedly to impress, the remnants of AI syntax left everyone amused. Despite the unintentional comedy, some users lauded Akhil’s initiative, suggesting that he was merely showcasing a “demo” of his skills.

Also Read: Google Announced “Help Me Write” Feature in Gmail – How to Use It?

Akhil's AI-generated cover letter.

Social Media’s Mixed Reactions: From Laughter to Reflection

The tweet showcasing Akhil’s AI-generated cover letter triggered a spectrum of responses from the online community. Many users found the situation amusing, with some even comparing Akhil’s attempt to a marketing expert using the wrong tools. However, others took a more empathetic view, suggesting that in certain industries with fierce competition, job seekers may resort to such methods to increase their chances of success. This sparked debates on the ethics of using automated application tools.

Also Read: Hollywood Writers Go on Strike Against AI Tools, Call It ‘Plagiarism Machine’

Twitter users respond to the funny cover letter generated by the IIT graduate using AI.

Ethical Dilemmas: The Rise of Automated Application Tools

Akhil’s hilarious cover letter fail brought attention to the growing trend of using AI-powered tools to automate the job application process. While such tools promise convenience and time-saving benefits, questions of ethics and authenticity arise. Is it fair to rely on automated cover letters that might inadvertently lead to embarrassing situations like Akhil’s? The incident has prompted job seekers and employers alike to reflect on the implications of this emerging trend.

Also Read: AI Discriminates Against Non-Native English Speakers

Our Say

Akhil’s adventure with an AI-generated cover letter undoubtedly provided a moment of comic relief for many on social media. However, it also highlighted the potential pitfalls of relying too heavily on automated application tools. As technology continues to shape the job market, job seekers and employers must balance innovation and authenticity, ensuring that their interactions remain effective and genuine. Akhil’s mishap serves as a humorous reminder to stay vigilant in a world where AI and human endeavors converge.

K.C. Sabreena Basheer
K.C. Sabreena Basheer

Sabreena Basheer is an architect-turned-writer who's passionate about documenting anything that interests her. She's currently exploring the world of AI and Data Science as a Content Manager at Analytics Vidhya.

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