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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

Index page to learn everything about Analytics

K
Kunal Jain 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.

analytics careeranalytics interviewslearningpythonRresourcesSAS
K
Kunal Jain

Kunal Jain is the Founder and CEO of Analytics Vidhya, one of the world's leading communities of AI professionals.

With over 17 years of experience in the field, Kunal has been instrumental in shaping the global AI landscape. His expertise spans diverse markets, from developed economies like the UK to emerging ones like India, where he has successfully led and delivered complex data-driven solutions. As a recognized thought leader, Kunal has empowered countless individuals to realize their AI ambitions through his visionary approach to AI education and community building.

Before founding Analytics Vidhya, Kunal earned both his undergraduate and postgraduate degrees from IIT Bombay and held key roles at Capital One and Aviva Life Insurance across multiple geographies. His passion lies at the intersection of analytics, AI, and fostering a thriving community of data science professionals.

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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

Neelima
Neelima

Good job Kunal! I appreciate it. An improvement in the index would be to link to the page from the Subject Namei.e. in the index all the topics you have covered are put, it is great if the end user is taken to the url of the topic when she clicks it.

123

Akshay
Akshay

Hi Kunal, Analytics Vidya has helped me tremendously to know about analytics industry in detail. Readers like me who are new to Analytics industry are not aware about statistical models which are developed & how to use statistical concepts to develope a model. I would like to have a topic which will include a real time business case which will have a problem & stepwise explaination of generating model (any model) & solution. Thanks

123

Shashi
Shashi

Hi Kunal Good going, and best wishes. BI and BA are little tricky terminologies for me. How do you differentiate BA tools with BI tools? My assumption is, BA is something more to do with Data discovery, inferences, Story building etc which is more close to Decision making whereas BI is more of ETL and underlying technology that supports data discovery. Appreciate your comments on it. thanks

123

Kunal Jain
Kunal Jain

Shashi, You are broadly right. You can read more details on these terminologies here. Regards, Kunal

123
Rajesh
Rajesh

Hello Kunal, I am completed my study in engineering with electronic and telecommunications . And further more I am certified with Business Analytics professional R can you help me out for openings in market.

123

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