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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

AVturns2: Let the celebrations begin!

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

Two years back, on this very day (well…actually night), I posted first article on Analytics Vidhya. Little did I know, what was in store! The excitement about creating one of the best analytics & data science community has only grown exponentially. Analytics Vidhya moved from being a part time leisure blog to (more than) full time engagement for me.

2

In the last 2 years, we have created one of the world’s largest analytics community. This journey would not have been possible without the support of the following three groups (in no particular order):

  • Our followers and readers
  • Analytics Vidhya team
  • Our family & friends

I wish to say my heart felt thanks to all three groups and many more involved in our journey. This journey thrives on the mutual eco-system of all 3 groups. Let me express my gratitude to each of them quickly:

 

Our followers and readers:

You are the source of energy driving this engine. It is that comment you write, that question you ask or that mail you drop, which keeps us going. Our readership has grown beyond the stage where we can name people individually. But every time we meet a reader, we can see the glow in their eyes. I am sure they can see it in our eyes as well.

So keep writing to us, talking to us and most importantly keep contributing to the knowledge creation through blogs and discussion portal. In order to hear more from you, here is a page we have created where you can leave your testimonials about Analytics Vidhya. The best testimonial wins an Amazon voucher and a few good ones win Analytics Vidhya merchandise.

 

Analytics Vidhya Team:

What can I say about this awesome team we are building! Be it those calls we make to each other at 2 o’ clock in the night or those ‘chai’ (tea) sessions we have – we just love working together. I want to thank the entire Analytics Vidhya team for making it the place it is. It is because of this them that we continue to dream and dream big.

 

And our families:

I think our families are the ones, who feel the heat more than we do. It is this support system which enables us to put in these extra long hours to make sure our readers get those awesome articles in their mailboxes. Thanks for being there for us and understanding our long and erratic work routines to keep this engine running.

 

Plan for the anniversary:

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We have some exciting things lined up for the day and week to celebrate the anniversary. As usual, it would involve some data crunching and shouting! But most importantly, it involves a bunch of new things we plan to do:

  • Super Saturdays: Going forward, we will keep a few hours every Saturday (12:00 p.m. to 4:00 p.m.) to talk to our readers for their career related queries. We did this last week and enjoyed it thoroughly. So, if you have any queries related to your career, feel free to reach out to us on [email protected] or on our telephone line.
  • Whatsapp groups: For enabling further networking and access to more people, we are creating 4 kinds of whatsapp groups: AV Transitioners, AV Beginners, AV Rising Stars and AV Gurus. These whatsapp groups would be invite only groups. You can request your membership to these groups here. We will screen the people and approve the requests where there is a good fit. Here is a brief description about each of these groups:
    • AV transitioners: This group is meant for people who are completely new to analytics. You know basic excel or have been in a MIS role. This group will help you make a transition into analytics.
    • AV Beginners: Meant for people who are in early stages of their career in Analytics. You know the basics of at least one advanced tool – R / SAS and have been reading / researching about analytics for some time. But you still need to work under a mentor before you can lead projects independently.
    • AV Rising Stars: These are the flagbearers for the era to come. You ideally have more than 3 years of experience in analytics and can accomplish analytics projects end to end independently.
    • AV Gurus: Only for the best of the best. This is an exclusive lounge meant for only the best in data science. Apply if you have at least 5 years of experience and you know multiple tools to work upon.
  • Meetups: Being an online community can leave you wanting to meet more people. In order to satisfy this craving, we will be doing more and more meetups across the country. We are starting with one in Delhi this week and will follow it up in various cities as we get more volunteers. If you want to host one in your city, please reach out to us and we will support it in every possible manner.
  • Shape up your data contest: We are launching a small contest to celebrate with a few prizes. We will release a small dataset and you will need to visualize the data and tell us the right shape for data. The contest involves basic data exploration. See, if you can win these awesome prizes. Read more details here. The participants of this contest, get a direct entry to our Whatsapp groups.

Let the Celebrations begin! Waiting to hear more from you on this special day.

anniversaryconteststhanks
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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