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Reading list

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
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 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

Highlights of 2013

Kunal Jain
Kunal Jain 26 Aug, 2021
3 min read

2013 has been an outstanding year for me personally. Among other things, there have been 2 key highlights for this year:

  • Becoming a father to the cutest baby (Jenika) in this world; and
  • Starting up and running Analytics Vidhya

While there is no comparison between the 2, there have been times when I felt that I am raising 2 babies simultaneously. I still remember sitting along side baby incubator (while Jenika was being treated for Jaundice) and scrambling my notes @ 3:00 a.m. in morning to make sure that a post goes out next day.

So while I was creating a collage of Jenika’s photos from 2013, I thought it might be a good idea to quickly reflect on 2013 for Analytics Vidhya and bring out some of the highlights.

rear-view

Here is what we achieved in 2013:

  • In the first 8 months of existence, Analytics Vidhya was visited by more than 20,000 unique visitors who made more than 33,000 visits to the blog.
  • We have published a total of 51 posts till date and are still counting
  • Our monthly visits has been consistently growing (except for the month when I took ~15 day break and Jenika was born).

I can go on and on….but you get the idea! We are one of the most read blogs in India in Analytics domain.

To summarize, the journey has been extremely fulfilling for me personally and every one involved in creation of Analytics Vidhya. We hope that we have created a difference in life of several analysts and the larger analytics community.

We thank our audience for their engagement, feedback and comments and hope that we have contributed to their Analytics learning in some way or the other. For those of you, who might have missed some of the awesome articles and discussions we have had, following are the key articles we posted on various topics:

[stextbox id = “section”] How to start and build a career in Analytics? [/stextbox]

[stextbox id = “section”]Certifications and trainings for continued learning: [/stextbox]

[stextbox id = “section”]Analytics best practices:[/stextbox]

[stextbox id = “section”]Career related advice:[/stextbox]

[stextbox id = “section”]Other resources for an analyst:[/stextbox]

[stextbox id = “section”]Subject matter knowledge: Regression, Clustering and Segmentation:[/stextbox]

[stextbox id = “section”]Articles on SAS[/stextbox]

[stextbox id = “section”]Articles on Qlikview[/stextbox]

[stextbox id = “section”]Articles on Big data:[/stextbox]

[stextbox id = “section”]Articles on web analytics:[/stextbox]

The sheer number of articles in this list is just a representation of the effort and quality time we have spent creating Analytics Vidhya. We hope that we have created enormous value for our audience and would continue to do so in 2014.

If you have any suggestions, feedback, instances how Analytics Vidhya helped you or what you would want more out of Analytics Vidhya please let us know. As always, we are eager to listen from you.

Wishing a Happy and Properous New Year to all our audience, team members and patrons.

New_Year_wallpapers_Happy_new_2014_year

If you like what you just read & want to continue your analytics learning, subscribe to our emails or like our Facebook page.

Image credit – ebuilder.net and Godwallpaper

Analytics trainingAnalytics VidhyaBusiness Analyticsclusteringlinear regressionQlikviewregressionSASSegmentation
Kunal Jain
Kunal Jain 26 Aug, 2021

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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