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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

Launching Analytics Vidhya’s Medium Publication and AV Editor’s club!

Pranav Dar
Pranav Dar Last Updated : 07 May, 2019
4 min read

Introduction

Analytics Vidhya started as a blog in 2013 and has grown into a thriving community of data science professionals. We have evolved the platform based on our community needs and feedback. Today Analytics Vidhya offers Meetups, Webinars, DataHack platform, job portal, training portal and our flagship event DataHack Summit.

Across these engagement points offered by Analytics Vidhya – the most popular continues to be our blog. Our blog continues to play an anchor in the way we have built this community. Thousands of people have benefitted from the resource provided on our blog. We are planning to step up our efforts in this direction today.

We are extremely excited to announce the launch of

Analytics Vidhya’s Medium Publication!

 

Why start a Medium Publication?

Now you might ask, why start a Medium publication?

Simply because we believe that we can grow our community and its impact multi-fold by creating a channel where people can contribute in the same way they learnt from the platform.

Medium offers seamless manner for a community run publication and we intend to run it that way. We want community members to come and share their knowledge so that everyone in the community benefits further.

 

What can you expect from our Medium Publication?

Here are a few things you can expect from our Medium publication:

  • Top notch articles from our community members – We will work with our community members to continuously provide them feedback and improve their writing. We will only publish something when it meets our standards.
  • Community Editors and Writers – The Editors and writers would be from community. We will groom them to share what we have learnt over the years. Call for Writers and Editors is open below. Come join us to share your knowledge and make an impact in life of millions of data scientists.
  • Ton of free content for community to learn – We encourage open knowledge sharing and would keep it that way. The aim for medium channel is to create an impact on the community. To that extent, we will keep as many resources free as possible.

Excited? Here is what you need to do next

 

 

Open Call for Writers

As we launch this publication, we are thrilled to open the floor for budding and experienced writers who want to showcase their work to our broad and thriving community. Our aim has always been to spread data science knowledge and make it accessible to as many people as possible, around the globe.

We have played an active role in this regard in the last 5 years, and continue to do so proudly. You bring the creativity and your passion for data science, and we now provide you with a platform to display it to the world.

Some of the key benefits you will receive as a writer:

  • We have a thriving and active community. As a writer, you get visibility in front of a broad audience!
  • Recognition as a subject matter expert
  • Obtain valuable and insightful feedback from experienced editors well versed in machine learning topics

Interested? Go ahead and fill this form for us. You can read more about what benefits we offer, and what we expect from you, in this detailed article.

Wait, there’s more..

 

Join the AV Editor’s Club

Yes, that’s right – there is an opportunity to work with us in the capacity of an Editor! We are looking for Editors and Content creators who can drive our blog and content platform to the next level.

Are you passionate about data science journalism? Do you have an itch to try and experiment with the latest tools, techniques and frameworks as soon as they come out? Can you take complex data ideas and explain them in simple easy to understand content? Do you love to share your knowledge and skill with the rest of the universe? Do you think your writing skills can enable and inspire the next generation of data scientists across the globe?

If the answer to the above questions was yes, then you are welcome to become the Founding member of AV’s Editor Club.

Apply here to submit your application and we will be in touch with you.

Got questions? Head over to this article on Medium which covers a lot of what you might be wondering. If there’s anything else, please go ahead and connect with us in the comments section

 

Are you ready?

So, are you ready to answer the call and bring your A-game? This is a golden opportunity to collaborate and work with Analytics Vidhya’s awesome team and launch yourself on a global platform. See you on AV’s Medium page!

analytics vidhya medium pagewrite for analytics vidhya
Pranav Dar
Pranav Dar

Senior Editor at Analytics Vidhya.Data visualization practitioner who loves reading and delving deeper into the data science and machine learning arts. Always looking for new ways to improve processes using ML and AI.

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