Trending December 2023 # 10 Most Popular Guest Authors On Analytics Vidhya In 2023 # Suggested January 2024 # Top 19 Popular

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Overview

Writing what you learn is at the core of Analytics Vidhya and we regularly engage with the community and encourage them to be guest authors on our blog

Here is a list of top 10 Guest Authors on Analytics Vidhya this year

Introduction

Analytics Vidhya has always prided itself on sharing high-quality comprehensive articles on a variety of topics related to ML, DS, and AI.

Be it articles dealing with the implementations of latest models, or dealing with covering the basic concepts, or the latest trends, our writers have always been at the forefront sharing our knowledge, and experience. The year 2023 was no different. We published over 500 articles and this number includes plentiful articles written by our Guest Authors.

In this article, I highlight the top 10 Guest Authors on our blog this year. These are in alphabetical order by their username.

We start off with an author who has written a couple of articles on different tools as well as how to start your DS career. All of Arka Ghosh articles combined have over 30K views!

What is the difference between Artificial Intelligence, Machine Learning, and Deep Learning? Which are the various roles in the DS industry? What are some tools and resources to start my DS career?

I am sure most of you who want to start their DS journey have these questions. This article by the author answers these questions and more in detail, using an example and easy-to-understand graphics. This article serves as a one-stop guide to starting your ML journey: How to Build a Career in Data Science and Machine Learning?

While we usually stick to either scikit-learn and pandas for our machine learning models. However, there are other libraries we can use to complete specific tasks more efficiently than the generic ones above.

His 2nd article deals with implementing Linear Regression. Too simple? Well, what makes it different is that the author implements linear regression using H2O’s AutoML tool and demonstrates the end-to-end process from loading the data to presenting the predictions. You can also learn about how to build AutoML models in this article: Exploring Linear Regression with H20 AutoML(Automated Machine Learning)

The final article by this author covers the Anomalize library in R for anomaly detection. The author has not only explained using this library but taken up an entire case-study to explain and implement anomaly detection in Time Series: A Case Study To Detect Anomalies In Time Series Using Anomalize Package In R

For instance, her article on Image Classification not only covers the basic code for using pre-trained models for the task but also to develop an Android app that uses such a model. This comprehensive article covers scraping images from the internet, preparing the image data, building a deep learning model based on VGG-16, and integrating a custom-built UI into an Android App: Build an End to End Image Classification/Recognition Application

The next article by this author deals with Stock price Prediction. While we come across various ways of dealing with Time Series tasks right from regression to ARIMA and to deep learning models like LSTMs, this article uses Reinforcement learning for the same. This is a totally new approach to solving a time series forecasting problem that is seldom used with comparable performance to the above methods. Check out this article here along with the Python code to implement it: Predicting Stock Prices using Reinforcement Learning (with Python Code!)

While the above articles were on Computer Vision and Reinforcement Learning, her latest article is from the NLP domain. Implementing the task of text summarization, the author now takes up scraping a Wikipedia Page(on reinforcement learning, no less) and using the nltk library to summarize this page. In the process, the article implements web scraping, text preprocessing, and summarisation: Tired of Reading Long Articles? Text Summarization will make your task easier!

The net author is one of our regular writers for our blogathons. His articles always cover interesting problems and provide easy and short to-do guides on how to get the problem solved.

His 1st article deals with one of the most time-consuming tasks in the DS process- data exploration. No matter how large your dataset is, this is one task one simply cannot ignore and have to perform to get an idea of the data provided. However, using just the basic tools of pandas, numpy and a couple of visualization libraries is too tedious and inefficient. What if there were a tool to make data exploration easier and one that we could integrate with Jupyter Notebooks as well? Well, this is what the ‘dtale’ library does. The article takes up a dataset and explains how to use the ‘dtale’ library in your Notebook for interactive data exploration: Data Exploration with the dtale Library in Python

While there are blogs and articles aplenty dealing with how to build machine learning and deep learning models but have you ever wondered what comes before and after model building? In the industry, datasets are not provided to us on a plate, ready to build our models on. Oftentimes, we have to collect the data ourselves. This is especially applicable when you want image data. There are not many image datasets available that you can use to practice your Computer Vision tasks. However, the internet is a treasure trove of images, and we can leverage this huge resource to get the images we want ourselves. Thus, this article by the author implements image scraping using the popular Selenium library in Python: Web Scraping Iron_Man Using Selenium in Python

Similarly, it is not merely enough to build a model and generate predictions on a test dataset. The important part is serving the prediction results in ways that can be further used to make decisions such as web apps. However, if you have a model ready, you need not consult a web developer to build a web application for it. The latest article by Kavish Jain demonstrates how we can use Streamlit, a popular Python framework to deploy our model: Streamlit Web API for NLP: Tweet Sentiment Analysis

The next Guest Author in this list also covers a wide range of topics in his articles catering to different audiences.

For beginners and professionals who want to become data scientists, it is often much easier to handle roles of data analyst or business analyst to get used to dealing with different types of data. However, there various opinions on how to become a data analyst. This comprehensive article covers all aspects of becoming a successful data analyst with useful resources to get you started: A Quick Guide to Become a Data Analyst.

What if you could create your own version of Alexa or your own personal digital assistant? Don’t worry, it is not as daunting as it sounds. This article provides the complete step-by-code to create your own desktop assistant powered by your voice: Build Your Own Desktop Voice Assistant in Python

So the next time you check the weather on your mobile, ask your own voice-based assistant first!

The importance of model deployment in the machine learning process can be gauged by the number of articles showing how to put your model into production using different tools and libraries is much needed in the domain of computer vision where you can show whether the image you uploaded is that of a dog or cat instantly. This article uses the fastai(v2) library and iPython widgets to classify x-ray images into Covid Positive or Covid-Negative: Develop and Deploy an Image Classifier App Using Fastai

One of our most prolific authors, Maanvi is fast becoming one of our regular contributors to the blogathons. For instance, Maanvi has submitted 5 well-researched and diverse articles over the last couple of months with each article exploring different concepts in R and Python.

Starting with the fundamentals of statistics, this article explains statistical modelling in great detail along with the definitions and key concepts of building statistical models: All about Statistical Modeling

It is a common misconception that machine learning involves collecting the data, cleaning it, feature engineering, model building, and prediction. However, that is not the case. There are quite a few statistical steps involved in between as well, such as Power Analysis. Power Analysis is a 4-step process that helps you perform a sanity-check on your data. This article illustrates this concept using Python: Statistics for Beginners: Power of “Power Analysis”

Are you more a proponent of Bayesian statistics over the frequentist approach? If so, conditional probabilities and Bayesian networks are your friends. More specifically, Probabilistic Graphical Models(PGM) form the base of making predictions using graphs and networks. This article covers PGM in great detail along with R code: Complete R Tutorial To Build Probabilistic Graphical Models!

Preparing your data before extracting and building features from it is one of the most vital tasks in the ML process. Occasionally, the data that we have collected is spread out across different files containing different features. It is not merely enough to just load these files and combine them into a single dataframe – we need to keep it consistent and remove the redundancy as well. This article demonstrates data preparation when it is split into different files: Tutorial to data preparation for training machine learning model

For beginners who have just started learning Python, data exploration can be a challenging task. It can be difficult to recollect and use a huge number of available functions to deal with different types of data. This article describes data exploration using Python on a dataset in easy-to-understand language and code: A Comprehensive Guide to Learn Data Exploration in Python!

Another of our highest contributors, he regularly churns out high-quality articles dealing with deep learning and statistics.

For instance check out his article on Data Exploration: Interpreting P-Value and R Squared Score on Real-Time Data – Statistical Data Exploration

While it may look like just another run-of-the-mill articles on data exploration, the author particularly highlights how to use statistical measures and techniques at this stage. In fact, we can derive the majority of insights at this step itself without even proceeding to build a predictive model on the data.

Have you thought of applying Python to improve call centers? It can be extremely helpful for customer service representatives who have to deal with thousands of calls every day, sometimes on the same issues but worded differently. This article uses plain Python code without any complicated model-building to create an extremely efficient log of issues and their corresponding resolutions by parsing large files for the critical baking sector: Modernize Support Logs Using Simple Python Commands

A continuation of the previous article, the author now uses NLP techniques to look up similar issues being raised and how to identify high-priority issues: NLP Applications in Support Call Centers

It is really fascinating to study how ML professionals use a combination of ML and DL models in their decision-making process.

If using NLP wasn’t impressive enough, the author now ventures into transfer learning and Generative Adversarial Networks(GANs). This article explains the fundamental concepts behind GANs, but also uses them to generate new images from a custom image dataset: Training StyleGAN using Transfer learning on a custom dataset in google colaboratory

Moving on to one of the most interesting articles in this list – dealing with Unsupervised Deep Learning. The concept of Autoencoders is a compelling one. They basically ‘learn’ to encode data in an unsupervised manner and thus are typically used for extracting and reducing features in a dataset. This article demonstrates using Autoencoders for the same purposes in a real-life energy sector problem: Deep Unsupervised Learning in Energy Sector – Autoencoders in Action

Like a few authors in this list, Sagnik Banerjee is also all about the tools. This author’s straightforward style of explaining practical concepts has been well-received by our community.

Time Series is one of the most common problems we come across in hackathons and in the industry. In fact, it is even a part of interview questions for ML roles across the board. There are various techniques to deal with time series forecasting like ARIMA, RNNs, etc. However, it is Facebook’s Prophet library that is fast becoming the preferred tool to forecast time series. This article provides a clear and concise implementation of the Prophet library using Python: Time Series Forecasting using Facebook Prophet library in Python

Similar to other articles that you will find in this list, Sagnik Banerjee’s next article also deals with Machine Learning model deployment. However, this time it is using the popular Microsoft Azure framework and Flask. The idea is to create a web application that can run 24 x 7 to generate predictions: How to Deploy Machine Learning models in Azure Cloud with the help of Python and Flask?

Feature engineering is one of the most essential steps in the ML process. You simply cannot skip this step and it is imperative to get the best possible features out of the data we have to get the best possible predictive model. Feature selection is one of the steps in feature engineering where we select the best possible combination of features. This excellent article describes the popular types of features selection techniques that filter out the redundant features: Most Common Feature Selection Filter Based Techniques used in Machine Learning in Python

Here we have an author who takes up the building blocks of machine learning models and explains them meticulously using examples and code each step of the way.

Generalized Linear Models(GLMs) are widely used in the industry despite the dee learning boom. Cheaper to build and easy to explain, they are the go-to models for building benchmarks in academia as well. In the case of classification, logistic regression, though popular can be quite intimidating for beginners. This article provides a complete guide to logistic regression along with the math behind it and also Python code: Demystification of Logistic Regression

Similarly, Entropy is one such term that we hear day-in and day-out in the case of decision trees and tree-based models. But what is Entropy and what is the intuition behind using it in tree models? This article provides a thorough, yet an easily understandable guide to Entropy and its usage: Entropy – A Key Concept for All Data Science Beginners

Dimensionality reduction is a much-needed step when we are dealing with large datasets. Working with thousands of features without reducing them can lead to poor-fitting models. Thus, using as few features as possible without losing too much information is essential for the ML process. Principal Component Analysis(PCA) is one of the most widely-used techniques for Dimensionality reduction. This comprehensive article explains PCA using interesting examples and clear explanations: An end-to-end comprehensive guide for PCA

I really like the topics Sreenath chooses to write his articles on. They are always engaging, dealing with new concepts and tools.

For instance, how many of you were acquainted with the idea of reservoir sampling? I am pretty sure that not many of us knew this simple technique of obtaining smaller chunks of datasets from a huge ‘reservoir’ of big data. It uses statistical techniques of accomplishing this. This article by Sreenath provides a comprehensive guide to Reservoir Sampling and provides the statistical intuition behind it: Big Data to Small Data – Welcome to the World of Reservoir Sampling

From statistics, let us move on to math. Most of the time when we are using linear regression, we hardly use the barebones model. More often than not, we combine regularisation with linear regression to get the best-fit equation for our data. Amongst regularisation, lasso and ridge regularisation are the most common types. However, what is the difference between the two? This article explains the math behind the two techniques and how they actually perform feature selection: Lasso Regression causes sparsity while Ridge Regression doesn’t! – Unfolding the math

Once we obtain the benchmark results for our model, the next step is Hyperparameter Tuning. It is Hyperparameter tuning that pushes our accuracy from 90% to 98% or our RMSE from 0.1 to 0.002. This finetuning of parameters can be a very tedious step when we are dealing with when we are using models with a large number of parameters like the Random Forest. It is simply impossible to try out various combinations of parameters by trial and error here. This article explores Optuna, a recent and lesser-known library that automatically generates the best possible combinations of parameters and also generates plots to visualize them: Hyperparameter Tuning using Optuna

Didn’t I mention earlier the diversity of topics that Sreenath chooses to write on? His most recent article tackles reinforcement learning. This concise article provides a really simple and intuitive explanation of reinforcement learning and how to use the Markov mathematical model to represent reinforcement learning: Getting to Grips with Reinforcement Learning via Markov Decision Process

Finally, I would like to include one of the most well-received authors among our readers. Vetrivel PS is one of the few authors who has won 2 awards in the same blogathon! An experience DS professional who is also a Kaggle Expert published 2 popular articles with us.

Participating in Hackathons is one of the most crucial prerequisites for building your DS profile. Achieving a good rank in hackathons could be the difference in your resume among hundreds of other resumes that could give you a boost.

However, it can be intimidating for beginners to start participating in hackathons and Vetrivel PS addresses this very issue in his awesome article on how achieved Rank 4 out of 3000 submissions in one of our hackathons: Ultimate Beginners Guide to Breaking into the Top 10% in Machine Learning Hackathons

Not only this, his other articles deals specifically with classification problems in hackathons and shares his approach of featured in the top 10% amongst 20,000 participants in Hackathon involving a classification problem: Ultimate Beginner’s Guide to Win Classification Hackathons with a Data Science Use Case

End Notes

Who was your favorite Guest Author of 2023? Share your feedback and answers below!

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These Were The Most Popular Shows, Episodes And Creators On Apple Podcasts In 2023

Apple Podcasts has shared annual rankings and charts that cover this year’s best and most popular podcasts shows, individual episodes, networks and creators.

HIGHLIGHTS:

Apple’s editorial teams have chosen the best podcasts of 2023

The list only includes the content found on Apple Podcasts

You can browse the new charts on Apple Podcasts

Introducing Apple Podcasts Best of 2023

Apple’s criteria for picking the best podcast shows of 2023 includes requirements like:

Exceptional content

Unique ability to engage audiences

Innovation in production, presentation, sound design

Best Show of the Year is “A Slight Change of Plans” with Maya Shankar from Pushkin Industries which revolves around stories about all sorts of change. One of the episodes discusses how Tiffany Haddish navigated the foster care system and discovered that she had a rare gift that would change her life. Another one focuses on John Elder Robison, who underwent experimental brain treatment to try and increase his emotional sensitivity.

Meanwhile, from WBUR and Futuro Studios, “Anything for Selena” with queer Chicana journalist Maria Garcia has been named Newcomer of the Year. The show explores what it means to belong through her relationship with artist Selena Quintanilla.

Apple Podcasts: The best shows of 2023

The following ten shows on Apple Podcasts defined 2023, according to Apple’s editors:

“A Kids Book About: The Podcast” with Matthew Winner, for taking a thoughtful, friendly and considered approach to explaining the big things in life—fear, failure and divorce, for instance—but also activism, sharing and money, to kids.

“Anything Goes with Emma Chamberlain,” for making listeners feel as though they’re her best friend, helping them through doubt and sadness with her unique frankness, keen observations and genuine affection.

“Good Inside with Dr. Becky,” for Becky Kennedy’s calming, validating voice to parents everywhere, acknowledging that if this time (and parenting generally) feels hard, that’s because it is.

“Las Culturistas with Matt Rogers and Bowen Yang” from Will Ferrell’s Big Money Players Network, for a joyfully escapist experience that takes listeners on a quirky, hilarious and unforgettable journey into the beating heart of culture.

“Pantsuit Politics” with Sarah Stewart Holland and Beth Silvers, for offering a unique approach to the news and politics through grace-filled conversations that unpack the valid, complicated, hard differences that persist in this moment.

“Teenager Therapy” with Gael Aitor, Kayla Suarez, Mark Hugo and Thomas Pham, for reminding everyone that direct, vulnerable conversations among friends—no matter the issue—is the best medicine.

“The Experiment” from The Atlantic and WNYC Studios with Julia Longoria, for elucidating the notion that countries—like people—are unfinished works in progress, and facilitating a dialogue about what it means to be a citizen.

“The Midnight Miracle” from Luminary with Talib Kweli, yasiin bey and Dave Chappelle, for a completely original experience that transports listeners into the room with remarkable energy from its hosts and very special guests

“This Land” from Crooked Media with Rebecca Nagle, for investigating and explaining the experiences of Native Americans to recontextualize America’s understanding of its own history.

“U Up?” with Jordana Abraham and Jared Fried, for exploring the very real—often hilarious—concerns of trying to find a partner, with banter that keeps listeners coming back no matter their relationship status.

Apple Podcasts: The Best episodes of 2023

Aside from the best shows, Apple’s editorial team has selected the following episodes as being the most popular in 2023:

“A Friendly Ghost Story,” about a painful, personal ghosting experience that explores the complexity of human relationships, from “Invisibilia” with Yowei Shaw and Kia Miakka Natisse by NPR.

“Bubba Wallace,” from Club Shay Shay by FOX Sports, which sees host Shannon Sharpe and Bubba Wallace, a Daytona 500 runner-up and the first African American driver to win Rookie of the Year in a NASCAR series, discuss the intersection of sports, politics, entertainment and humanity.

“Glorious Basterds,” about a chance encounter with Paul Rudd at a movie theater that causes a formerly devout Jehovah’s Witness to rethink her future and embark on a new life, from “Storytime with Seth Rogen” by Earwolf.

“How Do I Love Someone?” a nonfiction rom-com about love during the pandemic, from “WILD” with Megan Tan by LAist Studios and KPCC.

“My Parents, Ellen and Tom,” a clear-eyed gem of an episode that sees host Ian Coss examine divorce by interviewing his parents about how and why their marriage ended without anger or recrimination, from “Forever is a Long Time.”

“The Body Mass Index,” about the complicated history of the BMI and the “obesity epidemic,” from “Maintenance Phase” with Michael Hobbes.

“The People in the Neighborhood,” which examines the murder of George Floyd through the neighbors who bore witness to it, from “Still Processing” with Jenna Wortham and Wesley Morris by the New York Times.

“The Symphony,” a mesmerizing, lyrical trip featuring Kevin Hart, Questlove, Mo Amer, Bill Burr, Pras, Michelle Wolf and Jon Stewart, from “The Midnight Miracle” with Talib Kweli, yasiin bey and Dave Chappelle, by Luminary.

“The Unwritten Rules of Black TV,” which traces the cyclical, uneven history of Black representation on television, from “The Experiment” with Hannah Giorgis by The Atlantic and WNYC Studios.

“This Strange Story,” about people who were completely cut off from the world when 9/11 happened and how they processed it, from “9/12” with Dan Taberski by Wondery and Pineapple Street Studios.

Apple Podcasts: 2023 charts

The Cupertino company also created the following charts to recognize the most popular new shows, free channels and individual shows and channels with subscriptions that launched in the US in 2023.

Top 10 Most Popular Chatbots In Different Sectors To Help Businesses

Today, 1.4 billion people use chatbots. Organisations deploy their top AI chatbots to have 1:1 conversations with customers and employees. Chatbots powered by artificial intelligence are also capable of automating various tasks, such as client assistance and sales.

We’ve collected the top 10 most popular chatbots in different sectors to help businesses of all sizes and industries find the best.

1. Netomi

Netomi’s AI platform helps companies to resolve client support tickets via email, voice, or talk. Because of its NLU motor, it has the highest level of natural language understanding (NLU). This chatbot can provide client support with unparalleled precision.

Netomi can therefore resolve more than 70% client queries without the need for human intervention and focuses extensively on AI client experience.

Also read: Best ecommerce platform in 2023

2. atSpoke

AtSpoke makes it easy for workers to access the knowledge they need. It is an interior tagging system with inherent Artificial Intelligence. It allows interior groups (IT help area, HR and other tasks groups) to quickly note 40% of all solicitations, which makes it 5x faster to reach their goals.

AI responds to worker queries by surface information base substances. Workers can easily get refreshed via the channels they use every day, such as Slack and Google Drive, Confluence and Microsoft Teams.

3. WP Chatbot

WP-Chatbot, the most popular chatbot within the WordPress environment is WP-Chatbot. It allows for live chat and site visits.

WP-Chatbot integrates a Facebook Business Page and powers live and automatic connections on a WordPress website through a Messenger talk gadget. It only takes one tick to set up the plugin.

Also read: How to choose The Perfect Domain Name

4. Microsoft Bot Framework

The Microsoft Bot Framework provides a comprehensive structure to build conversational AI encounters.

The Bot Framework composer is an open-source visual authoring tool for engineers and multidisciplinary teams to design and construct conversational encounters using language understanding, QnAmaker, and bot responses.

Microsoft’s bot framework allows clients to use a wide-ranging open-source SDK, apparatuses, and tools to seamlessly interface with a bot to existing channels and gadgets.

5. Alexa for Business

Are you ready to connect with 83.1 million smart speakers owners? Amazon holds 70% of the market and has the best AI chatbot software to voice assistants.

With Alexa for Business IT teams can build custom skills to answer customer questions. In just three years, Amazon has grown from 130 skills to more than 100,000 skills by September 2023.

Also read: No Plan? Sitting Ideal…No Problem! 50+ Cool Websites To Visit

6. Zendesk Answer Bot

Zendesk is close to your Zendesk help group, so you can answer any client queries immediately. To provide clients with the information they require immediately, the Answer Bot pulls relevant articles from your Zendesk knowledge database.

You can add more innovation to your Zendesk chatbot, or you can let the Zendesk To Answer bot fly on its own on your site, in portable applications, or within inner groups on Slack.

7. CSML

CSML is an open-source language for programming and chatbot engines that aims to create interoperable chatbots. CSML is a chatbot engine that allows designers to communicate and build chatbots with their expressive punctuation.

Also read: 10 Best Android Development Tools that Every Developer should know

8. Dasha AI

Dasha is a platform for conversational AI. It provides developers with the tools to create conversational AI applications that are human-like and profoundly conversational.

These applications can be used to substitute call center specialists, to text talk or to add conversational voices interfaces to flexible applications or IoT gadgets. Dasha was named a Gartner Cool vendor in Conversational AI 2023.

Dasha does not require any knowledge of AI or ML to work with, and any engineer who has basic JavaScript knowledge will be completely at ease.

9. SurveySparrow

SurveySparrow allows you to conduct conversational studies and build structures. This stage includes consumer loyalty reviews, such as Net Promoter Score, Customer Satisfaction Score, or Customer Effort Score, and overviews of employee experience (i.e. Recruitment and Pre-enlistment, Employee 360 Assessments, Employee Check-in and Employee Exit Interviews).

Also read: Top 9 WordPress Lead Generation Plugins in 2023

10. ManyChat

In one year, Facebook Messenger will be used by 2.4 billion people. ManyChat is a great alternative if you are looking for an efficient way to send a simple chatbot to book items, sell products, request updates, share coupons, and make requests on Facebook Messenger.

You can choose from industry-specific formats or create your own interface. This allows you to dispatch a bot in minutes with no coding.

It is easy to interface with eCommerce tools such as Shopify, PayPal and Stripe, ActiveCampaign and Google Sheets. There are also 1,500+ additional applications available through Zapier or Integromat.

Top 10 Most Promising Cryptocurrencies In September 2023

There are more than a thousand cryptocurrencies in the market, at the moment, and the most popular one out of the lot is Bitcoin. With frequent market volatility, choosing the right cryptocurrency, apart from the ever-expensive Bitcoin, for investment becomes an overwhelming task. To help you make smart investments, here are the best cryptocurrencies with the most growth potential in September.

1. Cardano

Cardano is touted for its proof-of-stake validation, which reduces transaction time and uses less energy. Because environmentally friendly coins have become the latest hot topic, Cardano serves the purpose. It also has many use cases as it enables smart contracts and decentralized applications. Compared to other cryptos of its kind, Cardano sees less market volatility.

2. XRP

XRP is a token created by Ripple, a digital technology and payments processing company. To enable the exchange of other cryptocurrencies on the network, XRP can be traded for traditional currencies as well. XRP has seen massive growth over the years and now several banks are using this blockchain network for their modern banking functions.

3. Binance Coin

Binance Coin is used to trade other cryptocurrencies and pay fees on Binance, one of the biggest cryptocurrency exchanges in the world. It was launched in 2023 and can now be used for many functions like even booking travel arrangements. If you are going to invest in cryptocurrencies for the first time, it’s best to invest in Binance first and then trade it for other cryptocurrencies.

4. Dogecoin

While there is not much hype around Dogecoin at the moment, this digital coin still attracts many investors. Cryptocurrencies like Bitcoin come with a limited coin supply, but Dogecoin has no limit. What was started as a joke in 2013 is now seeing a myriad of supporters, from billionaires to celebrities?

5. Tether

Tether is a unique cryptocurrency as it is a stable coin. Stable coins are backed by fiat currencies like the US dollar or the Euro, which means anyone who buys 1 Tether coin will be guaranteed the value of one fiat currency. Theoretically, this means Tether’s value will be more stable than other cryptocurrencies amidst market volatility.

6. USD Coin

USD Coin is also a stable coin with its value pegged to the US dollar. For every USD Coin bought, the investor will be assured the value of US$1. This coin is powered by Ethereum which means it can complete transactions on a global scale.

7. Landshare

Landshare is created to leverage the real estate industry. Based on the Binance Smart Chain and DeFi principles, users can use Landshare for house flipping projects, and make passive income via rents. Launched in August this year, Landshare is gradually getting traction. Initially, it was priced at US$3.6, and at the time of writing, it is trading at US$2.67.

8. Polkadot

There are more than 7000 cryptocurrencies in the market that use various blockchain networks. Polkadot’s aim is to integrate them all by creating a cryptocurrency network that connects all the blockchains to work in sync. This ambitious mission has attracted many experienced investors, booming Polkadot’s growth.

9. Ethereum

Ethereum is the network that powers the token Ether. Ethereum is a developer’s favorite platform as it supports smart contracts that allow them to create apps based on the network. Ethereum has also seen massive growth over the years. Second to Bitcoin in market cap, it is now receiving more attention as the network announced its new upgrade Ethereum 2.0 that brings changes to this blockchain network and makes the token more environmentally friendly.

10. Uniswap

Most Influential Ai Voices On Linkedin In 2023

Most influential AI voices on LinkedIn are present to guide you through the latest innovations in AI

Millions of dollars are being invested in Artificial Intelligence technologies which are increasing the rate of competition in the business world. A diverse range of news is being produced from the vast field of AI across the world in a short span of time on a daily basis. There is a huge gap between the large-scale audience interested in AI and the hi-tech companies who continuously change the world with creative innovations. Who will be the bridge between the gaps? The AI influencers who have millions of followers on the professional networking site, LinkedIn! Let’s go through their profiles who reveal more opportunities and recent possibilities in the field of AI.

Keep up with Artificial Intelligence with the most influential voices on LinkedIn Bernard Marr— 1,465,526 followers

LinkedIn has already awarded Bernard Marr as one of the world’s top 5 influencers in the AI world. He is the founder of the world-leading company, Bernard Marr & Co., which provides core services in the areas of Strategy & Business Performance, Big Data Analytics, AI & ML, Performance Management and many more. His professional statements on AI and its innovations are being mentioned on popular TV, newspapers and radio channels like BBC, Sky, The Times, The Financial Times, The Wall Street Journal and so on. He is considered the most highly respected expert in the world’s best-known firms such as Accenture, Barclays, HSBC, IBM, Ministry of Defence, Microsoft, the United Nations, Walmart, Vodafone and the list goes on. He is the author of eighteen books as well as hundreds of articles and reports out of which many are his international bestsellers with translation into over twenty languages. At present, Bernard Marr is a teacher at the Irish Management Institute, Oxford University, BPP, Warwick Business School and ICAEW.

Allie K. Miller— 1,091,820 followers

Allie K. Miller is known as the AI Business Leader and International Speaker in San Francisco. She is influencing the audience with what it means to build and scale a business in the Artificial Intelligence world. Miller is the founder of the AI Pipeline and co-founder of Girls of the Future. She holds a renaissance approach to apply creativity as well as humanisation to the technical problems in the business. She has worked on conversational AI, computer vision and data. Allie Miller is the most popular name in top VC firms, European Commission and several tech seminars and conferences and is awarded several times. At present, she is the Global Head of ML Business Development, Startups and Venture Capital at AWS and a Brand Ambassador for AWIP (Advancing Women In Product).

DJ Patil— 742,347 followers

DJ Patil is currently a board member of Devoted Health at the Harvard Kennedy School. He has established new healthcare programmes such as the Precision Medicine Initiative and the Cancer Moonshot which covers over 94 million Americans. He has worked as a data scientist in various popular firms like Harvard’s Belfare Centre, Biden-Harris Transition Team, The White House, Relate IQ, Greylock, LinkedIn, e-Bay, PayPal, Skype and many more. He has won several awards including selection by the World Economic Forum as a Young Global Leader.

Dennis R. Mortensen— 514,464 followers

Dennis R. Mortensen is a professional expert who has been successful in leveraging data with business insights. He is the founder and CEO of chúng tôi in 2014 along with co-founders Matt Casey and Marcos J. Belenguer to solve critical problems through upgraded AI algorithms efficiently and effectively. He is also a popular business leader, author as well as a university instructor in Digital Data and Analytics at the University of British Columbia. Dennis is the pioneer in the analytics, optimisation and big data space with a good number of followers in his social media platforms.

Andrew NG— 472,669 followers

Andrew NG is the founder of chúng tôi co-founder of Coursera, general partner of AI Fund and Adjunct Professor at Stanford University. He is an expert in ML, robot learning and online education where he has over 100 research papers available for interested audience. He is focused on entrepreneurial ventures to accelerate the possible AI practices in the world-scale economy. Andrew has over 3.2 million students in his ‘Machine Learning’ course and over 400,000 students in the ‘AI For Everyone’ course.

Cassie Kozyrkov— 342,442 followers

Cassie Kozyrkov is currently a Chief Decision Scientist at Google whose main aim is to democratise Decision Intelligence and safe reliable AI. She assists Google in making effective and efficient use of AI-based machines and technologies. She has successfully worked with over 200 groups and inspired over 400 projects with the training of over 5,000 analysts and decision-makers in the AI and ML field, all with her knowledge and insights to transform workflow. She has a diverse knowledge of data science, decision science, ML, AI, experimental game theory, behavioural economics and many more.

Welcome 2023 With New, Better And More Helpful Analytics Vidhya

Over the last 12 months, we went from a small, little known blog on analytics to one of the most engaging and helpful community in Data Science across the globe.

If 2014 was big (for us), the plans for 2023 are grand!

Before I pull out any rabbits from my hat, I want to thank our followers and audience. They have been the driving force behind our existence, our growth and our inspiration. Nothing beats the satisfaction we get in seeing / meeting / talking to a satisfied reader!

Whenever we are stuck with these questions, we always question ourselves – What is the best decision for our audience? And it has served us well. It goes without saying, that this is not changing in future.

So, the first thing we want to do in 2023 is to listen more, participate more in discussions, in knowledge creation and in turn help out the larger community. Analytics Vidhya Discuss:

Analytics Vidhya Discuss is a question and answer platform for data science professionals (or those who want to enter data science world). You can ask any question related to data science here. Here are a few examples:

An error you are facing while running your analysis in SAS / R / Python / Weka

Confusion on the best technique for a specific problem

Question on how to improve your clustering (or)

How to start your journey in data science

We are super excited and I am sure you would also be, once you look at our new Question and Answer platform – chúng tôi This platform is one of the best platforms we have seen for asking questions and getting answers. It is super intuitive, easy to scroll and find relevant topics. We think that this platform should go on to become the place to discuss analytics and data science.

So, what are you waiting for? Go and test out Analytics Vidhya Discuss

Coming soon – Website re-design:

We started our journey as a blog and the focus at the start was to test out whether we can help people with their analytics learning. We never thought we would add so much content in so little time. We also hacked a blog platform and used it for training listings and job alerts. While all of this has helped our readers and us, it has also resulted in a cluttered website.

So, we are re-designing the site. The site will get a new look shortly. We love the way new look is coming out and are working hard in tuning, fine-tuning and re-tuning it. It feels like we are dressing up a teen who is out to impress his / her friends in a party. Where do we put the social sharing buttons? How is the site looking on mobile devices? Different browsers? How do we ask users to login?

Do lookout for the new look and let us know your feedback.

End Notes:

We are starting the year in style – by launching Analytics Vidhya Discuss and re-designing Analytics Vidhya. The idea is to make it better and more helpful. We dedicate both these changes to our audience for their support and patronage and hope that Analytics Vidhya Discuss turns out to be our biggest gift to data science community across the globe.

Do check out these additions and let us know your feedback. As usual, we will be waiting to hear more from you.

If you like what you just read & want to continue your analytics learning, subscribe to our emails, follow us on twitter or like our facebook page.

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