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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

Experience Realistic Virtual Meetings with Zoom on Apple Vision Pro

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

In a revolutionary move set to transform virtual meetings, Zoom is set to launch its innovative app tailored for Apple Vision Pro today. The new application promises to elevate the virtual meeting experience, incorporating augmented reality and virtual reality features. Zoom on Apple’s headset aims to bridge the gap between remote interactions and in-person connections.

Also Read: AI Set to Drive Virtual & Augmented Reality Market Growth: The Synergy of Future Tech

Unveiling Apple Vision Pro Integration

Zoom’s collaboration with Apple introduces a dedicated app for the Vision Pro headset, promising a transformative experience for users. The app leverages Apple’s Persona technology, creating digital avatars for participants that mirror real-time facial expressions and hand movements. This integration is poised to make virtual meetings more lifelike and engaging.

Experience Realistic Virtual Meetings with Zoom on Apple Vision Pro

Spatial Zoom Experience

A standout feature, the “Spatial Zoom Experience,” allows users to adjust the virtual meeting environment, providing a customizable and immersive atmosphere. The goal is to replicate the dynamics of face-to-face meetings, offering a more comfortable and collaborative space without the need for additional physical equipment.

Also Read: Exploring Social VR: Going Beyond the Basics of Virtual Reality

Exciting Features for Enhanced Collaboration

Zoom introduces three upcoming features to enhance collaboration on Apple Vision Pro. 3D Object Sharing enables users to share and work on 3D models, catering to artists and designers seeking a more interactive platform. Zoom Team Chat, integrated seamlessly into the app, offers a quick and easy way for users to communicate with colleagues during virtual meetings. Real-world Pinning allows users to position up to five meeting participants in their physical space, creating a sense of presence and connection.

Apple Vision Pro Pricing and Availability

As the highly anticipated Apple Vision Pro hits the market today, Zoom’s app will be available for download simultaneously. The headset, priced at $3,499, boasts over 600 dedicated apps. This includes social media apps and video streaming services, although notable names like Netflix and YouTube are absent.

Also Read: Apple Vision Pro Hits Shelves on February 2, Pioneering Spatial Computing

Apple Vision Pro headset combines augmented and virtual realities.

Our Say

Zoom’s integration with Apple Vision Pro marks a significant step towards transforming remote collaborations. The amalgamation of Persona technology and Spatial Zoom Experience places Zoom as a frontrunner in providing an immersive & engaging virtual meeting experience. The upcoming features like 3D Object Sharing and Team Chat further concrete Zoom’s position in this regard.

As the demand for innovative solutions grows, Zoom’s commitment to seamless communication shines through in this exciting collaboration with Apple Vision Pro. Get ready to redefine your virtual meetings with Zoom’s groundbreaking features on Apple’s cutting-edge headset.

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

AppleApple VisionApple Vision ProARaugmented realityFeaturesintegrationnewsTechnologyTransformvirtual realityVRzoom
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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Apple’s Vision Pro Launched with 600 Apps: Here’s All You Need to Know
Apple’s Vision Pro Launched with 600 Apps: Here’s All You Need to Know
Apple Vision Pro Hits Shelves on February 2, Pioneering Spatial Computing
Apple Vision Pro Hits Shelves on February 2, Pioneering Spatial Computing

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