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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

Rapid Rise of Artificial Intelligence Spells Job Losses: Thousands Affected in Tech Sector

K.C. Sabreena Basheer
K.C. Sabreena Basheer Last Updated : 12 Jun, 2023
3 min read

The tech industry is experiencing a significant upheaval as Artificial Intelligence (AI) takes center stage and replaces human workers. The recent surge in job cuts has seen thousands of employees lose their positions, with AI cited as one of the primary reasons for these dismissals. This emerging trend has left experts and employees grappling with the consequences of automation in the workforce. Let’s delve deeper into this rapidly evolving landscape, exploring the potential ramifications for individuals and the industry as a whole.

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The rapid rise of Artificial Intelligence has lead to unemployment in the tech sector, as thousands lose their jobs to AI.

AI Emerges as a Job-Cutting Factor

In a notable development, American companies have cited AI as contributing to a substantial increase in job cuts. A report from the Chicago-based outplacement firm Challenger, Gray & Christmas, says shows the elimination of 80,000 jobs in the tech sector last month, representing a 20% rise from April. The reasons for these cutbacks include market conditions, restructuring, buyouts, and the incorporation of AI into company operations.

Artificial Intelligence (AI) has been cited as a reason for unemployment in America.

Surprising Speed of Technological Impact

The report reveals that in May alone, approximately 3,900 individuals lost their jobs directly due to the implementation of AI. Experts expressed astonishment at the rapidity with which companies are citing technology as a cause of job losses. Andy Challenger, Senior Vice President of the outplacement firm, stated in an interview with Fortune magazine, “We do believe Artificial Intelligence will cause more job loss, though we are surprised how quickly technology was cited as a reason.”

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Widespread Concerns About AI’s Impact

A recent survey conducted by software company Krista highlights the public sentiment regarding the potential influence of AI on employment. The findings indicate that most individuals believe that AI will affect their jobs at some point in the future. While only 11% of employees in managerial positions expressed concern about the negative impact of AI on their work, this figure nearly doubled for rank-and-file workers. Moreover, a March report from Goldman Sachs highlighted that 18% of all jobs worldwide, and a quarter of all jobs in the United States and Europe, could be automated.

Also Read: Find Out How AI & ML Can Help HR Automation

White-Collar Jobs at Higher Risk

The survey also reveals that white-collar professions, such as those in the IT, tech, finance, administrative, and legal sectors, face a higher risk of being replaced by AI than blue-collar occupations involving physical labor. Automating tasks like coding, which typically require years of human expertise, leads companies to opt for AI as a cost-effective and efficient alternative to human employment.

Also Read: ChatGPT Outperforms Doctors in Providing Quality Medical Advice

White-collar jobs are more at the risk of being taken over by AI than blue-collar jobs.

Uncertainty Surrounding Future Job Losses

While the current wave of job cuts due to AI is alarming, the true extent of its impact remains uncertain. As Andy Challenger explains in Fortune magazine, “The uncertainty lies in whether companies will cite A.I. as a reason [for termination] going forward. How investors will react to that adoption that costs human jobs remains to be seen.”

Our Say

The rapid rise of AI in the tech sector is causing profound disruptions in the job market. It has resulted in significant job losses for thousands of workers worldwide. The increasing adoption of AI technology has raised concerns about its impact on employment across various industries. As automation continues to reshape the workforce, individuals and organizations must navigate this transformation proactively. The future of work is currently at a critical juncture. Finding a balance between technological advancements and human employment is crucial for sustainable growth and prosperity.

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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