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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

Samsung to Unveil AI-Powered Kitchen at CES 2024

K.C. Sabreena Basheer
K.C. Sabreena Basheer Last Updated : 29 Dec, 2023
2 min read

Samsung Electronics is set to redefine kitchen experiences with its latest innovations at CES 2024. The showcase, featuring AI-powered kitchen products, applications, and features, demonstrates a commitment to creating a seamless and connected food ecosystem. Let’s dive into the details of these innovative technologies that promise to elevate the way we interact with our kitchens.

Also Read: LG to Launch Smart Home AI Agent for Household Management

AI-Enhanced Refrigeration

Samsung’s star product, the 2024 Bespoke 4-Door Flex™ Refrigerator with AI Family Hub™+, takes center stage. The refrigerator, initially launched in 2016 as the industry’s first IoT refrigerator, introduces the revolutionary AI Vision Inside feature. This smart internal camera recognizes items in and out of the fridge, with “Vision AI” identifying up to 33 fresh food items. Users can effortlessly manage their food inventory, set expiration dates, and receive timely alerts, all displayed on the 32” LCD screen of the Family Hub™+.

AI vision in Samsung's new AI-powered Bespoke 4-Door Flex™ Refrigerator

Intelligent Cooking Solutions

Complementing the smart refrigerator, Samsung introduces the Anyplace Induction Cooktop and Slide-in Range. Both equipped with 7” LCD screens, these appliances seamlessly integrate with the Family Hub™+. Users can access and follow recipes selected on the Family Hub™+ to simplify meal preparation. The Anyplace Induction Cooktop, boasting Samsung’s induction sheet coil technology, ensures efficient and borderless heating, while its Anti-Scratch Glass adds durability to the cooking surface.

Also Read: Is AI Taking Over Kitchen? McDonald’s Joins Hand with Google to Deploy GenAI

Samsung Food Application Upgrade

The Samsung Food application has undergone a significant upgrade, now accessible on the Family Hub™+, Anyplace Induction Cooktop, and Slide-in Range. Enhanced AI features include the “Personalize” function, allowing users to adapt recipes to specific dietary needs, from vegan to gluten-free. The “Image-to-Recipe” feature, powered by Vision AI, recognizes meals and ingredients from photographs, providing diverse recipe options. The application’s integration with Samsung Health profiles ensures personalized meal plans based on individual health needs.

Connected Kitchen Ecosystem

The Bespoke 4-Door Flex™ Refrigerator serves as the core of Samsung’s connected kitchen ecosystem. Linking with Samsung Health, the Samsung Food service suggests healthy recipes based on user health data. Tap View functionality enables users to mirror their Galaxy smartphones on the Family Hub™+ screen, offering convenience. Moreover, applications like YouTube and TikTok directly on the refrigerator screen provide users with both information and entertainment.

Also Read: Amazon’s Secret AI Robot for Home Can Do Everything and More

Samsung to Unveil AI-Powered Kitchen at CES 2024

Our Say

Samsung’s commitment to seamlessly connected, AI-based kitchen experiences reflects a forward-looking approach to the evolving needs of consumers. The integration of AI in every aspect of the kitchen, from refrigeration to cooking and meal planning, showcases the brand’s dedication to making daily culinary tasks more efficient and enjoyable. As consumers anticipate these innovations at CES 2024, Samsung continues to shape the future of smart living through enhanced kitchen experiences.

AIApplicationsArtificial IntelligenceelectronicsFeaturesintegrationIoTnewsSamsungSamsung AIsmart homeTechnology
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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