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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

Apple’s Vision Pro Launched with 600 Apps: Here’s All You Need to Know

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

Apple’s much-anticipated Vision Pro headset has hit the market, boasting an array of features and applications designed to redefine the virtual and augmented reality experience. Priced at $3,499, the Vision Pro is set to cater to a diverse audience, blending entertainment, productivity, and spatial computing. In this article, we delve into the key aspects of the Vision Pro’s launch, from its app ecosystem to notable features and early reviews.

Also Read: Xreal Air 2 Ultra AR Glasses – The Affordable Version of Apple Vision Pro

Extensive App Ecosystem

Apple proudly announced that over 600 apps, ranging from productivity tools to gaming and entertainment services, are optimized for the Vision Pro at launch. The headset supports a variety of streaming apps, including those from cable providers like Charter, Spectrum, Comcast, Cox, Sling TV, and Verizon Fios. Additionally, sports enthusiasts can access the NBA app and PGA Tour Vision for real-time shot tracking on 3D models of golf courses. The Vision Pro also features more than 250 Apple Arcade games, offering users a diverse gaming experience.

Apple's Vision Pro Launched with 600 Apps

Productivity Apps and Work-focused Features

The Vision Pro aims to enhance productivity with a suite of apps, including Box for file sharing, MindNode for brainstorming, and OmniFocus and OmniPlan for task and project management. The device also supports work-focused apps such as Webex, Zoom, Microsoft Teams, Slack, Notion, and Todoist. Users can explore immersive spatial presentations with JigSpace and engage in virtual visits to places like the Natural History Museum using apps like Hold the World. For home design enthusiasts, Lowe’s Style Studio and Wayfair Decorify provide augmented reality experiences.

Also Read: Experience Realistic Virtual Meetings with Zoom on Apple Vision Pro

Entertainment and Streaming Experiences

Apple emphasizes entertainment with the Vision Pro, offering a 100-inch display experience for sports and streaming apps. While the headset lacks native support from major players like Netflix and YouTube, it compensates with an impressive lineup. For now, it includes Disney+, IMAX, Apple TV, Paramount+, Sling TV, NBC, CBS, Peacock, and more. Users can access 3D versions of movies and enjoy immersive content like HBO’s Max app, providing an Iron Throne Room environment for a unique viewing experience.

Apple's Vision Pro combines AR and VR experiences

Design and Features

The Vision Pro’s design, resembling high-end ski goggles, incorporates features such as a continuous front panel with a concealed fan for electronics cooling. The headset boasts two displays for each eye, totaling 23 million pixels, ensuring a sharp and clear visual experience. Apple collaborated with Zeiss to offer magnetic lenses for prescription wearers. The device incorporates 23 sensors, including cameras, lidar sensors, and microphones, contributing to a seamless and responsive user experience.

Design of Apple's Vision Pro headset

Notable Features and Future Prospects

The article touches on the Vision Pro’s EyeSight feature, projecting a virtual persona onto the external display, and the new biometric authentication system, Optic ID, utilizing iris recognition for unlocking and secure transactions. The spatial video and audio capabilities, powered by visionOS, promise immersive experiences. The Vision Pro’s battery life, with a separate pack providing up to two hours of use, and the software’s support for accessories and gestures are also discussed.

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

Our Say

As Apple steps into the realm of spatial computing with the Vision Pro, the diverse app ecosystem and immersive features undoubtedly make it a noteworthy entry into the augmented reality space. While early reviews acknowledge the device’s impressive visuals and interactive capabilities, critics point out some trade-offs, including its weight and the absence of native apps from major streaming services. The success of the Vision Pro will likely hinge on its integration with other Apple devices, creating a cohesive experience for users in the evolving landscape of augmented reality.

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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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