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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

Top 10 Guest Authors on Analytics Vidhya in 2022

M
Meetu Arora Last Updated : 12 Dec, 2022
7 min read

Data science is one of India’s rapidly growing and in-demand industries, with far-reaching applications in almost every domain. Not just the leading technology giants in India but medium and small-scale companies are also betting on data science to revolutionize how business operations are performed. Data science is the field where large datasets are collected, analyzed, and interpreted in a way that assists in making critical business decisions in a better manner. Data scientists are the experts in the data science community, having considerable knowledge and experience in utilizing scientific techniques to gather and interpret monstrous data to be used for a specific purpose or project. Data science is a perfect field for anyone with a passion for data and numbers and a keen interest in mathematics and technology. While obtaining a degree in data science is the first step to penetrating the industry, keeping yourself abreast with the recent trends and information in the same domain is also crucial. If you’re an aspiring data scientist, here’s a list of the top 10 guest authors on Analytics Vidhya to follow in 2022 and gain valuable insight into what’s new and important in the industry:

Top 10 Guest Authors on Analytics Vidhya

  1. Aman Preet Singh Gulati

Aman Preet Singh Gulati (aman_preet_singh_gulati) is one of our top and most-followed guest authors on Analytics Vidhya. He has been contributing to the data science community for 2 years now. He started his writing journey with Analytics Vidya back in October 2021 and has 58 published articles to his credit.

Aman Preet Gulati

The topics and niches he specializes in cover machine learning, linear regression, computer vision, business analytics, data visualization, and Python. He has contributed some commendable insights and work when it comes to harnessing machine learning and data visualization in the healthcare sector.

Some of his most popular published wiring pieces are:

 

  1. Drishti Sharma

Drishti Sharma

Though quite a recent contributor to the ever-growing data science community of Analytics Vidhya, Drishti Sharma (Drishti_sharma) has garnered much appreciation and following. She started her writing and knowledge-sharing journey with Analytics Vidhya in January 2022 and has contributed about 50 articles already. Her expertise lies in a variety of subjects that include Machine Learning, Deep Learning, Python, Artificial Intelligence, NLP, Cloud Computing, and Reinforcement Learning.

Some of her popular writing pieces are:

 

  1. Prateek Majumdar

Prateek Majumdar

Prateek (prateekmaj21), one of the guest authors on Analytics Vidhya, is a final-year engineering student from the Institute of Engineering and Management, Kolkata. He likes to code, study analytics, and Data Science, and watch Science Fiction movies. His favourite Sci-Fi franchise is Star Wars. He is also an active Kaggler and part of many student communities in college.

He started his data-sharing journey with Analytics Vidhya data science community in April 2021 and has 34 successful published works to his credit already. His expertise holds solid in IoT, business analytics, artificial intelligence, Blockchain, machine learning, data engineering, and cloud computing. Let’s look at some of his most applauded and followed work on Analytics Vidhya:

 

  1. Abhishek Jaiswal

Abhishek Jaiswal (datanuts) has been an ardent contributor when it comes to the top authors in Analytics Vidhya platform. He started his writing repertoire on AV in December 2021 and has been going strong ever since. He has 30 published articles on multiple niches under his belt and is one of the most followed guest authors on Analytics Vidhya.

Abhishek Jaiswal

His area of expertise lies in computer vision, data engineering, Python, machine learning, big data, deep learning, cloud computing, AWS, and several other beginners to advanced topics. Some of his revered published work is:

 

  1. Chetan Dekate

Chetan Dekate

Chetan Dekate (Chetan159) is one of our top contributing guest authors on Analytics Vidhya and most loved data science expert sharing his knowledge with the data science community. He started his writing journey with Analytics Vidhya in April 2022 and has 27 published articles already covering some of the trickiest technology subjects. His expertise lies in picking up complex technology topics and write in detail as to make it easily understandable for the non-technical people too.

His expert areas cover a wide gamut of domains like data engineering, cloud computing, cybersecurity, data science, Python, data visualization, and business intelligence. Here are some of his published and most followed work on Analytics Vidhya:

 

  1. Prashant Sharma

Prashant Sharma (prashantsharma16) is a huge technology enthusiast and is currently pursuing his B.Tech. Degree from Vellore Institute of Technology. He has demonstrated a keen interest in programming and its real applications including software development, machine learning and data science. He started his knowledge sharing journey with Analytics Vidhya in August 2021 and has been an active contributor ever since.

Prashant Sharma

He has 24 published articles on multiple and a wide variety of topics that range from NLP, data visualization, Tableau, Big data, data engineering, deep learning, machine learning, an many more. His blog archives are a store house of knowledge and valuable information on critical data science topics. Here are some of his published and much appreciated work on Analytics Vidhya:

 

  1. Gitesh Dhore

Gitesh Dhore

Gitesh Dhore (gitesh20) became a part of the Analytics Vidhya data science community in July 2022 and has contributed about 23 published articles. He is one of our top-rated guest authors on Analytics Vidhya, participating in most Blogathons and harnessing every chance to share his valuable knowledge on a variety of subjects in technology. His expertise range in a multitude of topics including data engineering, machine learning, MLOps, AwS, Cloud Computing, Big data, business analytics, and plethora of other topics relevant for beginner to advanced professionals. Let’s look at some of his best work on AV platform:

 

  1. Devashree Madhugiri

Devashree Madhugiri

With 22 successfully published articles and an admirable amount of technical expertise, Devashree Madhugiri is one of our top-rated authors on Analytics Vidhya platform. She enthusiastically participates in Blogathon every month and shares her acquired knowledge with the rest of the community. She started writing with AV in July 2021 and has become a star author in a commendable amount of time. Her expertise lies in data visualization, computer vision, deep learning, Python, dataa engineering, machine learning, big data, and regression. Her notable work is:

 

  1. Aryan Garg

Aryan Garg

Aryan Garg (aryan5059) is one the most-followed guest authors on Analytics Vidhya and has been an active member our data science community since December 2021. He has published 21 articles till date and his expertise vests in computer vision, deep learning, machine learning, database, programming, Python, artificial intelligence, IoT, Web 3.0, and cybersecurity. Here are some of his best-rated published articles:

 

  1. Amrutha K

Amrutha K

Amrutha K, one of the top-league authors at Analytics Vidhya, has a deep interest and expertise in cryptocurrency, programming, and machine learning. She joined AV data science community in January 2022 and has got 17 articles published already. A regular feature at every Blogathon, she connects well with the readers in her easy-to-understand language and a strong grasp of the subject. Some of her best published work is:

We hope the above list of top 10 guest authors on Analytics Vidhya inspired you to penetrate the data science space and make a rewarding career out of it. Keep following the above authors and more on our blog section for all the latest updates and comprehensive information on everything happening in data science.

M
Meetu Arora

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