Interview Prep
Career
GenAI
Prompt Engg
ChatGPT
LLM
Langchain
RAG
AI Agents
Machine Learning
Deep Learning
GenAI Tools
LLMOps
Python
NLP
SQL
AIML Projects

Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning LifecycleIntroduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data InsightsDescriptive StatisticsInferential StatisticsHow to Understand Population Distributions?
Reading Data Files into PythonDifferent Variable Datatypes
Probability for Data ScienceBasic Concepts of ProbabilityAxioms of ProbabilityConditional Probability
Central Tendencies for Continuous VariablesSpread of DataKDE plots for Continuous VariableOverview of Distribution for Continuous variablesNormal DistributionSkewed DistributionSkeweness and KurtosisDistribution for Continuous Variable
Central Tendencies for Categorical VariablesUnderstanding Discrete DistributionsPerforming EDA on Categorical Variables
Dealing with Missing ValuesUnderstanding OutliersIdentifying Outliers in DataOutlier Detection in PythonOutliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and PopulationCentral Limit TheoremConfidence Interval and Margin of Error
Bivariate Analysis Introduction
CovariancePearson CorrelationSpearman's Correlation & Kendall's TauCorrelation versus CausationTabular and Graphical methods for Bivariate AnalysisPerforming Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical VariablesIntroduction to Hypothesis TestingP-valueTwo sample Z-testT-testT-test vs Z-testPerforming Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares TestBivariate Analysis on Categorical Categorical Variables
Multivariate AnalysisA Comprehensive Guide to Data ExplorationThe Data Science behind IPL
Supervised Learning vs Unsupervised LearningReinforcement LearningGenerative and Descriminative ModelsParametric and Non Parametric model
Machine Learning PipelinePreparing DatasetBuild a Benchmark Model: RegressionBuild a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should knowConfusion MatrixAccuracyPrecision and RecallAUC-ROCLog LossR2 and Adjusted R2
Dealing with Missing ValuesReplacing Missing ValuesImputing Missing Values in DataWorking with Categorical VariablesWorking with OutliersPreprocessing Data for Model Building
Understanding Cost FunctionUnderstanding Gradient DescentMath Behind Gradient DescentAssumptions of Linear RegressionImplement Linear Regression from ScratchTrain Linear Regression in PythonImplementing Linear Regression in RDiagnosing Residual Plots in Linear Regression ModelsGeneralized Linear ModelsIntroduction to Logistic RegressionOdds RatioImplementing Logistic Regression from ScratchIntroduction to Scikit-learn in PythonTrain Logistic Regression in pythonMulticlass using Logistic RegressionHow to use Multinomial and Ordinal Logistic Regression in R ?Challenges with Linear RegressionIntroduction to RegularisationImplementing RegularisationRidge RegressionLasso Regression
Introduction to K Nearest NeighboursDetermining the Right Value of K in KNNImplement KNN from ScratchImplement KNN in Python
Bias Variance TradeoffIntroduction to Overfitting and UnderfittingVisualizing Overfitting and UnderfittingSelecting the Right ModelWhat is Validation?Hold-Out ValidationUnderstanding K Fold Cross Validation
Introduction to Feature SelectionFeature Selection AlgorithmsMissing Value RatioLow Variance FilterHigh Correlation FilterBackward Feature EliminationForward Feature SelectionImplement Feature Selection in PythonImplement Feature Selection in R
Introduction to Decision TreePurity in Decision TreeTerminologies Related to Decision TreeHow to Select Best Split Point in Decision Tree?Chi-SquaresInformation GainReduction in VarianceOptimizing Performance of Decision TreeTrain Decision Tree using Scikit LearnPruning of Decision Trees
Introduction to Feature EngineeringFeature TransformationFeature ScalingFeature EngineeringFrequency EncodingAutomated Feature Engineering: Feature Tools
Introduction to Naive BayesConditional Probability and Bayes TheoremIntroduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings!Working of Naive BayesMath behind Naive BayesTypes of Naive BayesImplementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification ProblemEvaluation Metrics: Multi Class Classification
Introduction to Ensemble TechniquesBasic Ensemble TechniquesImplementing Basic Ensemble TechniquesFinding Optimal Weights of Ensemble Learner using Neural NetworkWhy Ensemble Models Work well?
Introduction to StackingImplementing StackingVariants of StackingImplementing Variants of StackingIntroduction to BlendingBootstrap SamplingIntroduction to Random SamplingHyper-parameters of Random ForestImplementing Random ForestOut-of-Bag (OOB) Score in the Random ForestIPL Team Win Prediction Project Using Machine LearningIntroduction to BoostingGradient Boosting AlgorithmMath behind GBMImplementing GBM in pythonRegularized Greedy ForestsExtreme Gradient BoostingImplementing XGBM in pythonTuning Hyperparameters of XGBoost in PythonImplement XGBM in R/H2OAdaptive BoostingImplementing Adaptive BoosingLightGBMImplementing LightGBM in PythonCatboostImplementing Catboost in Python
Different Hyperparameter Tuning methodsImplementing Different Hyperparameter Tuning methodsGridsearchCVRandomizedsearchCVBayesian Optimization for Hyperparameter TuningHyperopt
Understanding SVM AlgorithmSVM Kernels In-depth Intuition and Practical ImplementationSVM Kernel TricksKernels and Hyperparameters in SVMImplementing SVM from Scratch in Python and R
Introduction to Principal Component AnalysisSteps to Perform Principal Compound AnalysisComputation of Covariance MatrixFinding Eigenvectors and EigenvaluesImplementing PCA in pythonVisualizing PCAA Brief Introduction to Linear Discriminant AnalysisIntroduction to Factor Analysis
Introduction to ClusteringApplications of ClusteringEvaluation Metrics for ClusteringUnderstanding K-MeansImplementation of K-Means in PythonImplementation of K-Means in RChoosing Right Value for KProfiling Market Segments using K-Means ClusteringHierarchical ClusteringImplementation of Hierarchial ClusteringDBSCANDefining Similarity between clustersBuild 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 DaskWorking with CuML
Introduction to Machine Learning InterpretabilityFramework and Interpretable Modelsmodel Agnostic Methods for InterpretabilityImplementing Interpretable ModelUnderstanding SHAPOut-of-Core MLIntroduction to Interpretable Machine Learning ModelsModel Agnostic Methods for InterpretabilityGame Theory & Shapley Values
Introduction to AutoMLImplementation of MLBoxIntroduction to PyCaretTPOTAuto-SklearnEvalML
Pickle and JoblibIntroduction to Model Deployment
Deploying Machine Learning Model using StreamlitDeploying ML Models in DockerDeploy Using StreamlitDeploy on HerokuDeploy Using NetlifyIntroduction to Amazon SagemakerSetting up Amazon SageMakerUsing SageMaker Endpoint to Generate InferenceDeploy on Microsoft Azure CloudIntroduction to Flask for ModelDeploying ML model using Flask
Model Deployment in AndroidModel Deployment in Iphone

Index page to learn everything about Analytics

kunal
kunal Last Updated : 25 Sep, 2015
2 min read

Analytics Vidhya has been a tremendous journey for us. Today, when we look back at the journey we have covered so far – it makes us feel proud. It gives us a sense of accomplishment – that we have added value to our readers. It gives us a sense of fulfillment – that we, along with our audience go to bed after doing our bit of learning.

During this journey, we have added a lot of articles on a lot of topics. While the tags and categories have helped to provide some structure to this madness – it is becoming difficult for people to discover the best of our articles easily.

Hence, we decided to create this index page. This page will serve as an index to some of our best articles on the site. This is by no means an exhaustive list – but a good start.

You can also access this page from the drop down in About Us header on the top bar of this blog

Analytics Vidhya articles

We will continue to put more and more information on the subject out there and make sure it is of top notch quality. Here are 2 things, we would want you to do to get your brain cells working:

  1. Now that you have seen one way to organize this information about various articles on Analytics Vidhya – can you think of some more ways to represent this information? For example, one interesting view can be to change the size / color of fonts of titles mentioned in this list based on number of vies or user engagement and see what it throws up. What other views can you think of?
  2. If there are other topics on which you would want us to write on or include in this list, please add them in comments below and we will action on them.

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.

kunal
kunal

Login to continue reading and enjoy expert-curated content.

Free Courses

Generative AI 4.7

Introduction to CrewAI: Building a Researcher Assistant Agent

Build smart AI agents with CrewAI to automate tasks and solve problems.
Generative AI 4.7

Understanding the working of Neural Networks

Learn the neural network basics, concepts, layers, and activation functions.
Generative AI 4.5

No Code Predictive Analytics with Orange

No-code AI course for business pros with real-world ML use cases.
Generative AI 4.6

GenAI Landscape: Foundations & Hands On

Learn Generative AI basics: prompting, RAG, fine-tuning & agents.
Generative AI 4.5

Getting Started with Tableau

Free Tableau certification course covering data visualization essentials.

Recommended Articles

Responses From Readers

Clear

Shivam Kushwaha
Shivam Kushwaha

Very great job done. Good going Kunal. I found this website very useful and wish you best. Thank you

123

Dinesh
Dinesh

Hi Kunal, I am following your site from last 3 -4 month. I did not know too much about the analytic, big data and there resource. Your site help me to understand this field and now i have taken a small step towards this field and working on that. Hope full will switch my career soon. I found lots of site in this filed but your site is too good compare to other one. All article provide references that help me to understand things. Keep going with this great work... Thanks, Dinesh k..

123

KG RATHNASABHAPATHI
KG RATHNASABHAPATHI

Mr.Kunal, It's a great home work done by you/your team. Very useful for freshers like me. Encourages to learn. Guides to understand the subject. If it is published in a package of subtitles it's more informative. Expecting more from your browser. Thanks with regards

123

Become an Author

Share insights, grow your voice, and inspire the data community.
  • Reach a Global Audience
  • Share Your Expertise with the World
  • Build Your Brand & Audience
  • Join a Thriving AI Community
  • Level Up Your AI Game
  • Expand Your Influence in Genrative AI
imag