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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

Welcome 2015 with new, better and more helpful Analytics Vidhya

Kunal Jain
Kunal Jain Last Updated : 18 Apr, 2015
3 min read

Over the last 12 months, we went from a small, little known blog on analytics to one of the most engaging and helpful community in Data Science across the globe.

If 2014 was big (for us), the plans for 2015 are grand!

Analytics Vidhya Logo

Before I pull out any rabbits from my hat, I want to thank our followers and audience. They have been the driving force behind our existence, our growth and our inspiration. Nothing beats the satisfaction we get in seeing / meeting / talking to a satisfied reader!

One of the things, which set us on this high growth trajectory is that we have listened to our audience! We have listened to every comment on our blog, every mail in our inbox, every social media interaction and the list goes on! And, it has helped us immensely. Whenever we had to decide between A or B, or take any important decision – it has always been guided by what our audience have said.

Whenever we are stuck with these questions, we always question ourselves – What is the best decision for our audience? And it has served us well. It goes without saying, that this is not changing in future.

 One of the things, which set us on this high growth is that we have listened! We have listened to every comment on our blog, every mail in our inbox, every social media interaction and the list goes on. And it has helped us immensely!

So, the first thing we want to do in 2015 is to listen more, participate more in discussions, in knowledge creation and in turn help out the larger community.

Analytics Vidhya Discuss:

Analytics Vidhya Discuss is a question and answer platform for data science professionals (or those who want to enter data science world). You can ask any question related to data science here. Here are a few examples:

  • An error you are facing while running your analysis in SAS / R / Python / Weka
  • Confusion on the best technique for a specific problem
  • Question on how to improve your clustering (or)
  • How to start your journey in data science

We are super excited and I am sure you would also be, once you look at our new Question and Answer platform – discuss.analyticsvidhya.com. This platform is one of the best platforms we have seen for asking questions and getting answers. It is super intuitive, easy to scroll and find relevant topics. We think that this platform should go on to become the place to discuss analytics and data science.

So, what are you waiting for? Go and test out Analytics Vidhya Discuss

Analytics Vidhya discussions

Coming soon – Website re-design:

We started our journey as a blog and the focus at the start was to test out whether we can help people with their analytics learning. We never thought we would add so much content in so little time. We also hacked a blog platform and used it for training listings and job alerts. While all of this has helped our readers and us, it has also resulted in a cluttered website.

So, we are re-designing the site. The site will get a new look shortly. We love the way new look is coming out and are working hard in tuning, fine-tuning and re-tuning it. It feels like we are dressing up a teen who is out to impress his / her friends in a party. Where do we put the social sharing buttons? How is the site looking on mobile devices? Different browsers? How do we ask users to login?

Do lookout for the new look and let us know your feedback.

 

End Notes:

We are starting the year in style – by launching Analytics Vidhya Discuss and re-designing Analytics Vidhya. The idea is to make it better and more helpful. We dedicate both these changes to our audience for their support and patronage and hope that Analytics Vidhya Discuss turns out to be our biggest gift to data science community across the globe.

Do check out these additions and let us know your feedback. As usual, we will be waiting to hear more from you.

If you like what you just read & want to continue your analytics learning, subscribe to our emails, follow us on twitter or like our facebook page.

20142015communitydata sciencediscussdiscussions
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

Rajarshi Chakraborti
Rajarshi Chakraborti

Way to go @AnalyticsVidhya.com. Hope this year you be landing up many more hits and I a career in data sciences.

123

Kunal Jain
Kunal Jain

Thanks Rajarshi. All the best to you too.

123
Abhijit Ghatak
Abhijit Ghatak

Can you suggest how do I install rattle along with RGtk2 package in Mac OSX 10.9.5 Thanks, - Abhijit

123

Kunal Jain
Kunal Jain

Abhijit, You should post this question on our QA portal - discuss.analyticsvidhya.com Regards, Kunal

123
Anon
Anon

Nice move, AV Team. I'm looking forward to learning at the new forum.

123

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