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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

5 years of building Analytics Vidhya – our journey and learnings

Kunal Jain
Kunal Jain Last Updated : 20 Apr, 2018
6 min read

Here is a bit of wisdom from Elon Musk:

When something is important enough, you do it even if the odds are not in your favour

– Elon Musk

I will actually extend it a bit:

When you feel passionately about something, so much that the work would impact the generations to come – you do it without calculating the odds.

By no means I am comparing my wisdom to the great man here, but when I look back – the only reason to start Analytics Vidhya was the passion and the need to create a knowledge based community portal for data science professionals.

If I had calculated the odds, in all likelihood I wouldn’t have started Analytics Vidhya.

Little did I know that it will turn out to be the most exciting and impactful decision in the years to come.

 

The journey to become one of the largest data science community

It all started 5 years back. I booked the domain to launch a (personal) blog on analytics and data science learning. The idea was to share what I had learnt during my industry experience with a larger audience.

After a few articles on site, things started taking its own shape and destiny – blog found its own followers and life, to the extent that I could see a much larger impact coming through Analytics Vidhya rather than continuing in my job. So I started doing Analytics Vidhya full time.

Since then, it has been a dream come true. It has been a journey where I have cherished every moment – every up and down, every problem, every triumph. I have had immense support from friends, family, mentors, the Analytics Vidhya team and their family members and reaching here would not have been possible without their efforts and support (more on this later).

Check out the journey in brief to relive some of the moments you might have seen:

What is coming next?

If you see the video above, you would see that the journey has just begun. The effort to build and strengthen the ecosystem only doubles down from here. This means more interactions and activities for our users, more resources to enhance your knowledge, more competitions, meetups, webinars and career opportunities for our community members.

Here are a few things you can expect to see in coming days:

  • Awesome technical resources – We will continue to take complex topics, break them in simple, easy to understand articles and publish them to our community.
  • Focus on hackathons and ML competitions – There is a very exciting pipeline of competitions coming up this year – make sure you are ready for them before they strike!
  • Enabling the next generation (students) to learn data science – We aim to provide our learning resources to students and take data science ecosystem to colleges this year. If you are a student, who want to be part of this journey – check out our campus ambassador program.
  • Trainings, open courses and video based content – Our training portal would be filled with courses for our community members in coming days. You would be able to track your progress through learning paths and have access to resources handy.

If you have any suggestions on what you would want AV to do this year – let us know. We will be more than happy to hear from you and your need.

 

Learnings from the journey

This is something I get asked quite often. I usually don’t talk about this because I want Analytics Vidhya to stay as the place and platform to learn data science. This being a slightly different post, I will take the liberty to share some of my learnings very briefly. If you want more on this – let me know and I will write a detailed post on a more appropriate portal.

So, here are some learnings I had in building Analytics Vidhya. I personally think these would be very useful and relevant for people building communities or bootstrapping startups:

  1. Think Long term – The only way something like Analytics Vidhya can be built is when you think long term. You have to be clear on what you are building and how you want to build it. For example, we always had option to become a media company, or an analytics services company – but we stay away from these opportunities because they don’t fit in how we want to build Analytics Vidhya in long term.
  2. Act short term (immediately) – The only way to build a startup is by moving fast. Seek clarity on the next steps of larger projects and move fast. The biggest threat to any startup is not moving fast enough. So while you think long term – your actions need to be focused and immediate.
  3. Focus on a thing and the universe expands – If you come across a need or a problem you feel strongly about, focus your efforts and energy on that thing and you will see the universe expand. The need to learn data science / analytics looked small on the surface, but it was a problem I had faced. See the universe which has been created around this need in the last 5 years. The other impact of this is that you don’t diversify your risks – so if you are doing a start up, don’t think of a plan B. When I left my last job – I did not keep any formal document about my employment with me. It might sound insane – but I didn’t want any way to go back in a job when I was building AV.
  4. Team and culture matters more than anything – Your team is what you have and how much you can achieve. These are the people who enable you to fight your battles. Make sure that you provide them as much visibility and comfort as you can. I was blessed to have a really good team around with people who feel very passionately about what we do. But, building culture needs a lot of constant and consistent effort. But the results are awesome – No one in our team accepts a bad / half baked article, no badly written code. Failure is fine, but not fighting hard is not. All of this has been possible as a result of consistent effort.
  5. Have a support system – There are times when you will be down – things will not go your way every time. There will be times when you need some one to look at things independently and advise you. Make sure you have a support system to go to. My family and my mentors end up being the system I need. Irrespective of how things are at work, I know I can always come back to my family and de-stress. I can always look up to my mentors and ask for their advice.
  6. Health and routine – This actually stems from first learning of thinking long term. If you are thinking long term – make sure health is a top priority. One of my mentors gave me an advice very early in AV journey and it has stuck with me since then. He said “The only risk in life is Health. As long as that is covered, everything can be managed.”
  7. Importance of relationships – Before starting Analytics Vidhya – I had never done any sales. I still don’t do sales. I believe in building relationships and doing the right thing for long term. And it has worked wonders for us. Our clients have stayed with us over these years and the business with them has grown year on year. We trust each other – they understand who we are and how we can help. They also understand what we don’t do and it helps us take long term bets together in this journey.
  8. Keep Reading and Learning – Daily learning and improvements is the sole reason for building something like Analytics Vidhya. Take out time on a daily basis to learn something new or read a few pages of a book. You can learn so much by reading experiences of other people and adapting it – transfer learning at work!

 

A big thanks to the entire AV family and community

This journey would not be possible without the efforts and the support of the larger ecosystem I rely on. This is where I draw my strength for. Let me thank each of them:

  • Analytics Vidhya community – First and foremost I would thank all the community members. They have been our primary source of inspiration. Whenever in doubt, we think what would the community members need! Thanks for your support and the response / support you give to any thing we do.
  • The Analytics Vidhya team and their family – As I said, the team enables me to dream big and fight battles. They are the ones who hold the fort, they are the ones who enable this dream. And our family members support us through this. Long nights, weekends, forgotten anniversaries – we have seen it all. The support from the team & our families is nothing short of amazing and I am blessed to have them.
  • Analytics Vidhya Volunteers – Here is a group of people working selflessly to enable this ecosystem. And they are fabulous. Every time I meet them, I cringe to spend more time with them. The passion, the ownership and the zeal to get things done for AV in amazing. This is where I see the true ecosystem taking shape.
  • Our mentors – I can’t thank our mentors enough for their support and advice. Since the day we started till today, there is so much I have learnt by discussions with the mentors. Every time I go to them for a discussion, I walk out wiser from the room.
  • Clients – Last but not the least, the trust clients have shown in our ability to bring the community together and execute things is immense. Be it DataHack Summit or hiring hackathons, every time we help a client – they also take a leap of faith in our ability to deliver the results. Our relationships have grown immensely through these years and I wish we can continue to help our clients for years to come.

 

Your time to speak

This has been a very different post – one which has no data science knowledge explicitly, but one which shares immense learnings we had through the years building Analytics Vidhya. If you have any suggestions / feedback / experience how we have helped you or how we can help you better – I am all ears to listen it. Looking forward to your comments.

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

Pranov Shobhan Mishra
Pranov Shobhan Mishra

Amazing post. I loved the breakdown of the journey and learning provided. The elaboration of long term vision is resonating so much with what I have been thinking over the last 2 years in terms of why I have taken a plunge into Analytics/machine learning. My plunge has been putting in all the effort I can to learn the subject. There is a still a long way to go for me. I love AV articles and the platform as they play big role in helping me learn and boosting my confidence. Keep going from strength to strength. All the best

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Kunal Jain
Kunal Jain

Thanks Pranov! Looking forward to seeing you around

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siva
siva

How is Analytics Vidhya Using Analytics to grow their operations ?

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Kunal Jain
Kunal Jain

Hi Siva - we are using it in places where machines can do the heavy lifting. Starting from identifying spam, recommendations of articles, fraud detection in hackathons and matching on the job platform - everything is based on Analytics

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Sastry Dasigi
Sastry Dasigi

Congratulations, Kunal! Keep up the good work. Wish you and AV a great future!

123

Kunal Jain
Kunal Jain

Thanks Sastry!

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