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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

It’s our 3rd Birthday – Come & Celebrate

Kunal Jain
Kunal Jain Last Updated : 15 Mar, 2017
4 min read

A big Thank You to our community. We just turned 3!

On this day, 3 years back – I started Analytics Vidhya. I knew I had to address the glaring knowledge gap in the industry, but I did not know “How to address the knowledge gap?”. So I started Analytics Vidhya as a  blog. Over these 3 years, we first blossomed as a blog and have now transitioned the blog into a community. Going by a popular statistic, less than 10% of start-ups see this fate. We have been lucky to do so.

God has been kind and so has been the awesome support of our community members. I have called up many of you at odd times with seemingly weird requests, but never has a request been turned down – no matter what it is. I feel proud and blessed to know you and be a part of shaping this community.

My Experience My Way

Back in 2014 – I left my well paying job to raise 2 babies together. Jenika (my daughter) was barely 6 months then and Analytics Vidhya was starting to get a few thousand visitors a month. I knew I wanted to create a knowledge engine for the world, but didn’t know how I would do it.

Thankfully, I didn’t dwell much on that question. We just focused on adding quality articles and resources to our portal. With each article and each question this community wrote, we served many more people with high quality knowledge. Obviously, there were those odd days, when I felt things are not moving as they should. But thankfully, they were small in number and I had the support of my friends, family, our awesome team at AV and the larger community.

I truly believe that the best is yet to come – now more than ever. All our new initiatives in last year have seen tremendous response from the community and we have no plan to change that any time soon. Today is our 3rd Birthday! Analytics Vidhya  has completed 3 years of its dedicated support to global analytics community. And, we’ve made sure there is a lot on table for you to feast. Let’s begin the party!

AV Data Fest 2016

What’s the celebration plan?

Yeah! I knew many of you wanted to know this.

To make this occasion special, we have set the table with many exciting options. Also, this occasion marks the beginning of AV Data Fest 2016.

We didn’t want geographical boundaries to be stop you from joining the party. Thus, this is an online fest event where several activities such as competitions, workshops have been organized

Keeping in mind the various levels of expertise people possess, we have something for every category of data practitioners:

 

For Beginners:

We have arranged a workshop which is meant for anyone interested in learning more about data analytics.

Experiments with Data (Analytics Workshop for Beginners)

You can attend this even if you don’t have any prior experience in analytics. Some background in statistics will help, but is not mandatory. All your concepts will get clear from the video tutorials and content we are providing. You will also get to work on a practice problem at the end for which you will be getting a score to evaluate yourself.

Participate Here

 

For Intermediates:

Are you looking to develop additional skills that will make your demand higher in the job market? Then you need to practice and exploit your skills. Here we are! with a series of hackathons for you. Test your competence level and get to understand where you need to work more.

The Creative Analyst (Data Visualization competition)

Do you possess the blend of creativity and analytics?

Participate Here

The Strategist (Strategic Thinking Competition)

Can you take an unstructured business problem and solve it using your structured thinking? Take this challenge and find out.

Participate Here

 

If this does not fulfill your appetite, then you can try something more challenging.

The Seer’s Accuracy (Data Science Competition)

Dig into the many essential concepts of Machine learning, R , Python, Predictive Modeling and Data Mining.

Participate Here

 

For Experts:

So now you possess the tag of being into the sexiest profession and know how to deal with large amount of data. This is the chance for you to get the limelight by showing off your acquired skills to the community of data scientists out there. And what if we say that you also stand a chance to grab the best in industry job profile.

The Seer’s Accuracy (Data Science Competition)

A 3 day contest where you will get to play with your machine learning or data mining abilities.

Participate Here

McKinsey Hiring Hack

The brand itself suggests the value of this hiring hackathon. You may get to work with one of the most famous multinational company.

Participate Here

Considering the response we have got from our community members I feel a deep responsibility to make most of my time here and serve you the best I can.

In the end, I would personally like to know your feedback so that we can improve, your experience (good or bad) or a success story. Please feel free to drop a personal note at [email protected] or leave a comment here. I’ll be waiting to hear from you.

Cheers!

Kunal Jain
Kunal Jain

Kunal Jain is the Founder and CEO of Analytics Vidhya, one of the world's leading communities of Al professionals. With over 17 years of experience in the field, Kunal has been instrumental in shaping the global Al 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 Al ambitions through his visionary approach to Al 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, Al, and fostering a thriving community of data science professionals.

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Responses From Readers

Clear

Roshan Sharma
Roshan Sharma

Thank you for your awesome articles , It really helps a lot for a fresher like us who wants to build a career in analytics Industry. I was just wondering when u guys are coming with an APP??

123

Kunal Jain
Kunal Jain

Hi Roshan, App is one of the things we want to do, but we do not have the bandwidth currently. Will keep you posted if we move ahead on it. Regards, Kunal

123
Kishor
Kishor

A big congratulations ! :) I thank you on behalf of entire community for leaving an impact in the analytics world. Wish you all the best and wish you many such wonderful years ahead

123

Kunal Jain
Kunal Jain

Kishor, Thanks for these wishes. I am sure the community will make a much larger impact by itself. Regards, Kunal

123
Ulhas Shetty
Ulhas Shetty

AV, Happy Birthday! Kunal and AV team, you'll are doing a beautiful job in giving back to this community and we AVions will make sure to keep spreading this knowledge! Kudos Guys! Have a great year ahead.

123

Kunal Jain
Kunal Jain

Thanks Ulhas. That is the spirit which keeps this community rocking! Regards, Kunal

123

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