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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

Microsoft’s AI-Powered ‘Voice Clarity’ Revolutionizes Audio on Windows 11

K.C. Sabreena Basheer
K.C. Sabreena Basheer Last Updated : 02 Feb, 2024
2 min read

Microsoft has introduced an enhancement to Windows 11, unveiling its AI-backed ‘Voice Clarity’ feature. This innovation aims to transform the audio experience for users, particularly in virtual meetings and PC gaming. In this article, we delve into the functionality and impact of Voice Clarity, recently made available on all Windows machines, including those with ARM CPUs.

Also Read: Microsoft’s New GenAI Team Targets Smaller and Cheaper AI Models

Unveiling Voice Clarity for Windows 11

Microsoft’s latest Canary test build introduces the Voice Clarity feature, previously exclusive to Surface devices. Utilizing low-complexity AI models, Voice Clarity combats background noise, echoes, and reverberation in real time during virtual meetings and gaming sessions.

Microsoft's AI-Powered 'Voice Clarity' Revolutionizes Audio on Windows 11

How Voice Clarity Works its Magic

The Voice Clarity feature enhances audio quality by isolating the user’s voice from potential disruptions, such as background noise or echoes. By deploying AI algorithms, it ensures a crisp and clear transmission of voice, making it an invaluable asset for remote workers and students engaged in frequent online meetings.

Also Read: Create Your Own AI Voice With Voicemod

Beyond Surface Devices – Accessibility and Compatibility

Originally confined to Surface devices, Voice Clarity is now available on all Windows machines, providing a seamless experience for users with x64 and Arm64 CPUs. The feature is automatically activated for supported applications like WhatsApp and Phone Link, without the need for additional hardware.

Also Read: Microsoft Leaps into AI Integration with Upcoming Surface Pro 10 and Laptop 6

Expanding Features with the Canary Build

Apart from Voice Clarity, the Canary Build introduces several other enhancements. Users can now instantly access recent photos and screenshots from connected Android devices through the Snipping Tool. Additionally, support for the latest USB standard, USB 80Gbps, is included on select devices running on Intel Core 14th Gen HX-series mobile processors.

Canary Build for Microsoft Windows 11

Microsoft’s Ongoing AI Integration

Voice Clarity is just one facet of Microsoft’s AI integration strategy. The company has transformed its Edge browser into an ‘AI browser,’ integrated AI into tools like Microsoft Paint, and introduced Copilot to Windows 11. The continuous development in AI technology hints at more transformative features coming to Windows 11 in the near future.

Also Read: Cognizant and Microsoft Unveil AI-Driven Innovation Assistant

Our Say

Microsoft’s Voice Clarity feature represents a significant leap forward in enhancing user experience on Windows 11. By addressing common audio challenges in virtual communication and gaming, it underscores the company’s commitment to providing innovative solutions. As Microsoft continues its AI journey, users can anticipate a more immersive and seamless computing experience.

Microsoft’s integration of AI-powered Voice Clarity into Windows 11 signifies a commendable effort to refine audio quality in virtual interactions. This feature, coupled with other enhancements in the Canary Build, positions Windows 11 as a platform committed to staying at the forefront of technological advancements.

Follow us on Google News to stay updated with the latest innovations in the world of AI, Data Science, & GenAI.

AIArtificial IntelligenceaudioFeaturesintegrationMicrosoftnewsvoicewindowswindows 11
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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Windows 11 and Microsoft Copilot Announce Latest AI Updates
Windows 11 and Microsoft Copilot Announce Latest AI Updates
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