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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

Brace Yourself – DATAFEST 2017 is coming & Call for AV Volunteers!

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

The start

Big things often have small beginnings

What is common between Richard Branson, Pierre Omidyar, Mark Zuckerberg and Colonol Sanders?

They all started small, continuously worked on their idea and made it big. They had an interesting idea or a pet project which they enjoyed. None of them would have realized the true potential of their ideas until they started to build a traction.

In a lot of ways, these stories are similar to our journey at Analytics Vidhya. Obviously, I am comparing our scale to these companies – but there are many parallels in the journey. When I started AV as a blog, I was not very clear about the shape and form it will take. I had no idea what scale it could reach. I only knew I was enjoying what I was doing.

Slowly and steadily things fell in place and we have built one of the largest analytics community across the globe.

The community today

Today, we help millions of people in their pursuit of finding practical knowledge about analytics and data science. We are blessed with a vibrant and active community ready to help other members. I see many people like me on the portal helping other community members. I am sure we have enabled a few thousand careers in analytics and data science.

I see people running their own meetups, whatsapp groups and other ways to take the knowledge forward. The world today looks so different from where I started – when I could not find a place or portal to meet other people in the analytics industry.

Today, I wanted to announce 2 things which would take us to a different level – DATAFEST 2017 and AV Volunteer Program. Read on!

 

DATAFEST 2017

This April, our blog completes 4 years of existence. Like last year, we plan to celebrate the occasion with an a grand online festival – DATAFEST 2017. DATAFEST 2017 would not only be bigger and better – it would be a reflection of our growth and presence today. We plan to come out and celebrate this month like there is no tomorrow (you never know what smart machines will do tomorrow!).

 

This DATAFEST, we will bring you multiple hackathons, lots of jobs and career opportunities. We will also provide you numerous ways to interact with industry experts. We will make sure your learning reaches new heights and you mark this celebration with ardor and intensity.

 

Brace yourself – we are on our way!

I am not spilling too many beans today! Today, I can only tell you this DATAFEST is going to be bigger, better and the grandest of all online events we ever did. We will leave no stone unturned in our efforts to celebrate this occassion.

You will hear more details from us shortly. In the meanwhile, make sure you register yourself for event updates by signing up here . We will make sure you are the first ones to hear our plans.

 

Call for Volunteers

Ok – let me announce something special. I could not keep everything to myself. Here is your opportunity to be in middle of the action. We have opened enrollments for our volunteer program. It is a unique program for all the data science evangelists out there.

As part of this program, you will be our torch bearers. You will be the ones who will take our efforts and initiatives to the next level. You will help us define how to make Analytics Vidhya bigger, better and more useful. Last date for registration is 28 Feb.

 

What do you get as an AV Volunteer?

  • You will become a part of this closely knit group.
  • You will have access to all AV resources and community.
  • You will have access to all our knowledge treasures.
  • You will be the first ones to be considered for recruitment in AV, if there is a vacancy we are looking for.
  • Of course, the fame and the recognition.

 

What do you need to be an AV Volunteer?

Only a few things

  • Incredible amount of passion and motivation.
  • The desire to make analytics and data science accessible to everyone across the globe.
  • A track record of learning and contributing to the community.
  • Past association with AV and our community

 

What is expected from you?

  • Be our torch bearers
  • Help out in ways you can – likely a few hours every week

 

How do you register?

  • Leave your details on this page and we will reach out to you
  • We will understand your motivation and ways in which you will like to help Analytics Vidhya grow to new heights.

 

Getting back to work

So, that is it for now. I will get back to my to do list. You make sure that you are registered for DATAFEST 2017 updates and register for a volunteer (if you are interested) . Once you have done that, wait and see the magic unfold in next 40 days. See you around.

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

Arihant
Arihant

Thanks Kunal for the awesome initiatives . Looking forward to work as volunteer in AV

123

JAGADEESH MM
JAGADEESH MM

Delighted with the opportunity and looking forward to contribute to AV.

123

Mohd Aves Malik
Mohd Aves Malik

Bravo! to you Kunal for taking such a great initiative it will be a great opportunity for me to be a volunteer in AV initiative.

123

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