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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

Revolutionizing Restaurants: The Power of AI in Food Service

Yana Khare
Yana Khare Last Updated : 04 Jul, 2023
3 min read

The restaurant industry has been embracing the power of artificial intelligence (AI) to streamline operations, enhance customer experiences, and adapt to the challenges posed by the COVID-19 pandemic. With automation technologies at the forefront, the McJob of the past may soon be replaced by a new era of efficiency and innovation. Let’s delve into the remarkable ways AI is transforming food service industry and restaurants across the globe, revolutionizing everything from food preparation to customer interactions.

Table of contents

Automating Repetitive Tasks with Robotics

Robots are no longer a sci-fi fantasy; they have entered real-world restaurants. In the picturesque town of Rapallo, Italy, waiter robots gracefully bring dishes to customers, exemplifying the fusion of technology and gastronomy. By leveraging robotics and AI, restaurants can optimize repetitive tasks like food delivery and kitchen operations. These robots are adept at making chips or emptying fry baskets, reducing manual labor and increasing efficiency.

Also Read: Zomato Embarks on Groundbreaking Artificial Intelligence

Streamlining Customer Interactions

Streamlining Customer Interactions | AI in restaurants

The pandemic led to staffing shortages and increased demand for contactless experiences, driving the adoption of AI-powered customer interactions. Restaurants now utilize AI to automate processes like drive-thru and phone ordering. This automation improves order accuracy and helps alleviate staffing shortages. With AI in place, restaurants can enhance their operational capabilities, ensuring a seamless and personalized experience for patrons.

AI’s Role in Menu Personalization and Upselling

The iconic Golden Arches of McDonald’s have also embraced AI technology. McDonald’s began investing in AI and machine learning in 2019, revolutionizing the ordering process. Touch-screen kiosks inside the restaurant allow customers to conveniently place their orders, while drive-thru digital menu boards adjust dynamically based on times of day, weather, and restaurant traffic. AI algorithms suggest complementary items, automating the upselling process and improving the dining experience.

Bridging Labor Shortages with Delivery Robots

The persistent labor shortages in the restaurant industry have spurred the rise of autonomous delivery robots. These robotic helpers undertake short deliveries, easing the pressure caused by the shortage of human labor. College campuses and urban areas are prime locations for these delivery robots, and several establishments have reported increased sales since their introduction. From Grubhub using robots on college campuses to Uber Eats testing delivery robots on sidewalks, the potential of autonomous delivery is vast.

Also Read: Robotics and Automation from Machine Learning Perspective

Virtual Assistants and Phone Ordering

Virtual Assistants and Phone Ordering | AI in food service industry

Ordering food over the phone has also received an AI-powered upgrade. Wingstop, a renowned restaurant chain, has piloted virtual assistants to take phone orders. This technology replicates human-like conversations and tailors recommendations based on customer preferences. By employing virtual assistants, Wingstop aims to reduce hold times and enable employees to focus on cooking and serving customers. Human assistance is still available for customers who prefer the traditional ordering experience.

Our Say

The integration of AI in the food service industry is propelling an era of increased efficiency, enhanced customer experiences, and optimized operations. From robotic waiters to personalized menu recommendations, AI technology is transforming every aspect of the dining experience. As restaurants continue to adapt and innovate, AI’s possibilities become increasingly exciting. The future of dining is here, powered by artificial intelligence.

AI in food serviceAI in restaurantsRobots
Yana Khare
Yana Khare

A 23-year-old, pursuing her Master's in English, an avid reader, and a melophile. My all-time favorite quote is by Albus Dumbledore - "Happiness can be found even in the darkest of times if one remembers to turn on the light."

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