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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 Vision Pro Hits Shelves on February 2, Pioneering Spatial Computing

K.C. Sabreena Basheer
K.C. Sabreena Basheer Last Updated : 10 Jan, 2024
3 min read

Apple has officially unveiled the release date for its much-anticipated Apple Vision Pro mixed reality headset, marking a significant leap forward in the tech world. Set to hit U.S. stores on February 2, the spatial computer, priced at $3,499, promises to redefine how people interact with digital content. This release marks Apple’s first major product introduction since the Apple Watch in 2015. The Vision Pro, also known to be Apple’s first 3D camera, is poised to set a new standard in spatial computing.

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

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

The Era of Spatial Computing

The Apple Vision Pro headset heralds the era of spatial computing, reshaping how individuals work, collaborate, and find entertainment. Its integration of digital and physical worlds, driven by users’ eyes, hands, and voice, positions it as a revolutionary consumer electronics device, as emphasized by Tim Cook, Apple’s CEO.

The Power of visionOS and Intuitive User Interface

At the core of the Vision Pro is visionOS, a three-dimensional user interface built on decades of engineering innovation. Intuitive gestures, controlled through users’ eyes, hands, and voice, redefine how individuals navigate and interact with apps. Environments, transporting users to dynamic landscapes, amplify immersive experiences beyond the confines of physical spaces.

Also Read: Car Windows Can Now Tell You What You See Outside; Thanks to VR Technology

Apple Vision Pro headset revolutionizes spatial computing with visionOS.

Extraordinary Experiences in Productivity, Entertainment, and Gaming

Boasting over 1 million compatible apps and a novel App Store, the Vision Pro offers an expansive canvas for productivity. Key collaboration apps seamlessly integrate, while the entertainment experience is elevated with ultra-high-resolution displays, HDR support, and an array of 3D titles. Gaming experiences are redefined, with Vision Pro supporting over 250 titles on Apple Arcade and introducing spatial games for unique gameplay.

Memories in Spatial Reality and Enhanced FaceTime

Apple Vision Pro brings memories to life through spatial photos and videos, offering an immersive experience enhanced by Spatial Audio. FaceTime undergoes a spatial transformation, presenting life-sized representations of participants and introducing Personas for authentic spatial interactions. The device’s breakthrough design ensures a comfortable fit, with personalized options and ZEISS Optical Inserts catering to vision correction needs.

Spatial Reality and enhanced FaceTime on Apple Vision Pro

Unrivaled Innovation, Privacy, and Security

The Vision Pro’s breakthrough display, advanced audio experiences, high-performance eye-tracking system, and dual-chip design with Apple silicon showcase Apple’s commitment to innovation. Meanwhile, privacy and security remain paramount. With features like Optic ID and EyeSight, Apple ensures user data remains secure. Moreover, the visionOS includes powerful accessibility features, catering to diverse user needs.

Also Read: WhiteRabbitNeo-33B: Revolutionizing Cybersecurity with Advanced AI Capabilities

Our Say

As anticipation builds toward the Vision Pro release date, the device’s potential to reshape human interaction with technology is palpable. The emphasis on spatial computing opens up new frontiers in productivity, entertainment, and communication. Apple has had a track record of introducing transformative products that often justify their price tags. This launch suggests that the Vision Pro headset aims to be the next evolution in personal computing. As spatial reality becomes tangible, the Vision Pro’s impact on the tech landscape seems to be groundbreaking.

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

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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