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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

METAVERSE: A World of Imagination that Takes you Through Reality!

A
Aryan Last Updated : 22 Mar, 2022
7 min read

Have you ever thought like: I wish I can get this thing or I have a desire that I want to be at this place or I love anime and I wish I have these powers. If yes, then my friend you are at the right place.

Introduction

Imagine having the power to get anything that you have ever wanted. Wouldn’t it be interesting, we can get anything like clothes, space tours, even superpowers…. literally anything. It is kinda feeling like the ALL MIGHTY!

Metaverse
Source: Fin docs

But always remember with great powers comes great responsibilities. What’s the use of a good quotation if you can’t change it 😂. Anyway apart from all the jokes, always remember that when you have this much power you have to be cautious and respect other people’s privacy.

But How?

Now, you might be thinking how can this be possible?

Don’t worry my friend! Everything is possible in this world…

The answer to this question is very simple i.e., METAVERSE

Metaverse

Source: Techidence

What is Metaverse?

For beginners, explaining METAVERSE is difficult to ask for, but we can all agree that there’s been plenty of Blah! Blah! about it, but at the end of the day, would you be able to explain the concept of METAVERSE to your father or even grand-father as it is not exactly in existence, which is why it can be a little confusing, but still, a lot of people/companies are working in this direction, for example, Facebook, which is now known as Meta. They’re working really hard on it, and they recently employed 10,000 people to help them meet their goal. There is still a lot of research and trial & error going on. Let’s not overcomplicate things – In layman’s terms, METAVERSE is a virtual place that can be shared with a large number of people at the same time, anywhere, at any time, and it is hyper-realistic, immersive, and interactive. METAVERSE is achievable thanks to AR (Augmented Reality) and VR (Virtual Reality).

 

Metaverse

Source: CNET

There may be several virtual worlds in a metaverse where people can engage in a range of activities that are both real and digital. It won’t be like what you’re doing right now — instead of looking at a computer, you’ll be able to fully immerse yourself in all of your online activities, including shopping, seeing friends and family, watching a performance, and even completing official paperwork.

 

Is it Good or Bad?

 

Metaverse

Source: UX Planet

 Every coin has two phases. Similarly, it also has a few advantages as well as a few limitations:

Feature 1 – Ability to log in to second life

It has more clear audible instructions. It transforms stereo information into visual information. It gives an opportunity for research into the design of more scaling virtual worlds. The limitations here are sound issues, limited capabilities of handling a bog number of avatars.

Feature 2 – Interfaces

It has more customizable interfaces. Better design of avatar and animations, linked images, that inform the user about their content. Limitations here are there might be difficulties in 3D object rotation.

Feature 3 – Search Button Design

It has more clearly indicated buttons. Bigger buttons, larger fonts, higher display, Depth, contrast, etc. The limitation here is that there might be difficulty in search buttons usage that have the same name and indications and could be mixed up.

Feature 4 – Online Communication

There are different designs of communication with other users inside the 3D environment. More simple and accessible online communication. The limitation here is that there might be limited functionalities of the VR space that are bulged by senior physical and cultural limitations.

These are just a few examples of the pros and cons of METAVERSE. There is not a single number of advantages and disadvantages as of now.

Characteristics of Metaverse

Speaking of the METAVERSE, let’s understand its basic characteristics:

Characteristics of Metaverse

Source: Sensorium

 

Get Ready, The Next Station is Metaverse!

 

Source: TechRepublic

Now, after reading all about metaverse it’s common to ask a question – “Where is METAVERSE? And How can I enter into it?”

Imagine if I will give you two pills – A red and A blue

Red Pill – It will allow you to go to the future and experience the METAVERSE

Blue Pill – It will allow you to experience it now.

I can definitely guess your choice i.e., BLUE PILL (Being able to feel it now). Isn’t it correct? HAHA! I know what you were thinking….

Well, initially you need hardware. The best part of this is you actually don’t need to go out and buy an expensive VR to experience it. Your smartphone is just enough to enter the world of METAVERSE. To experience it you can visit – METAVERSE. The one and only caveat being that you might not receive much of that meta experience after all, since you’ll be losing out on the metaverse’s immersiveness, which is what makes it all so wonderful.

A phone is fine for a sneak-peek, but if you want the whole experience, you should invest in a VR headset or, if you don’t want to go that far, a pair of AR smart glasses.

The META (Facebook) company is working very hard o develop this and they are also producing high-quality VR(s).

Source: IDC 2021

The sales of virtual reality headsets are forecasted in this manner. VRs are selling at a rapid rate, as can be seen. After a decade of anticipation, it looks that technology and high-quality VR services are catching up, not only in gaming. A boom in demand has resulted from applications appearing in a range of industries, including fashion, music, education, sports, and others. This trend is predicted to continue as more systems switch to virtual reality and first enter the metaverse. However, whatever your area of interest, the METAVERSE is nearly guaranteed to have what you’re searching for. Because there are so many people engaged, the possibilities of VR/AR are limited only by the imagination.

Source: Sensorium

Want to Enter the Metaverse? But not Sure How?

It might take years, if not a decade, to build a full-fledged metaverse. Many technological challenges remain, starting with the fact that the world lacks an internet infrastructure capable of supporting millions (or potentially billions) of people accessing the metaverse at the same time. Furthermore, since a glitchy, ‘loading’ virtual world situation is a long cry from what a real metaverse should resemble, a constant and consistent internet connection is a vital component of the metaverse. 5G and edge processing are still in their early stages of development and are currently unable to meet the requirements of a contemporary framework, such as the metaverse. There are also a variety of concerns concerning data security and protection. When accessing the primary authentic metaverse becomes reality, the truth will emerge – and when it happens, I’ll be there to greet you.

Relation Between Metaverse and Data Science

Data scientists, at their most basic level, extract information from data in order to address real-world issues; machine learning is only one tool in their toolbox. From data pretreatment through solution implementation, we’re witnessing automation at every level of the data science lifecycle.

Is there Artificial Intelligence in the Metaverse?

Metaverse employs artificial intelligence and blockchain technology to build a digital virtual environment in which you may securely and freely engage in social and economic activities that go beyond the boundaries of the actual world, and the adoption of these cutting-edge technologies will be expedited. With the help of data science, one can develop this with help of the 3D Modelling technique and then one has to use a few techniques of Artificial Intelligence like CNN and ANN techniques. These are only a few examples there can be more!

ANN is Artificial Neural Network. Artificial neural networks, commonly referred to as “neural networks,” are computer systems based on the biological neural networks that make up brains. Artificial neurons are a set of linked units or nodes in an ANN that loosely replicate the neurons in a biological brain.

CNN is Convolution Neural Network. A convolutional neural network (CNN) is a form of artificial neural network that is specially intended to analyse pixel input and is used in image recognition and processing. A neural network is a hardware and/or software system modelled after the way neurons in the human brain operate.

Key Takeaways

  • Metaverse can help you to do anything that you can imagine.
  • It’s not yet in complete function due to internet stability in the world.
  • Meta is working hard to make it a reality.
  • Metaverse uses AI and ML technology.
  • Metaverse uses AR and VR technology.
  • Metaverse is a good concept but one should respect others’ privacy.
  • Metaverse is Boundless.
  • Metaverse is persistence.
  • Metaverse is Immersive.
  • Metaverse is Decentralized.
  • Metaverse is an Economic System.
  • Metaverse is a Social Experience.
  • Metaverse Players are Meta, Sandbox, Minecraft, Fortnite, etc.

At last, enjoy the Metaverse functioning and respect the privacy.

Have Fun!

About the Author

 

A 3rd-year (6th Semester) Student at CHRIST University, Lavasa, Pune Campus. Currently Pursuing BBA (BUSINESS ANALYTICS). If you have any questions or suggestions on what my next article should be about, please write to me at [email protected]. If you want to keep updated with my latest articles and projects, follow me on Medium and connect with me on LinkedIn.

 

Head onto our blog to read the latest articles.

 

The media shown in this article is not owned by Analytics Vidhya and are used at the Author’s discretion. 

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A
Aryan

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Well Done on Completing Your Learning Journey. Stay curious and keep exploring!
Metaverse Technology: Definition, Features and Use Cases
Metaverse Technology: Definition, Features and Use Cases
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