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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

AV Blogathon is Live – Inspiring a new breed of Data Scientists

Kunal Jain
Kunal Jain Last Updated : 05 Jan, 2016
3 min read

Do you also have the passion to write and inspire ?

Writing is an art which let’s you convey a piece of information without any criticism. Sometime, it’s difficult to speak and deliver an idea, but you can write it anytime. Have you felt this before?

 

A Short Story

Jon and Harry felt the same. They both shared a common passion to write. They both wanted to share their knowledge. In other words, both of them wanted to write and inspire people. But, both chose different ways. Jon decided to create his website to share his knowledge. It costed him a lot. More than that, his website demanded dedicated effort and hardwork for scaling it. He failed to keep up.

On the other hard, Harry played smart. He searched for some popular blogs in his domain. He, then connected with one of them. And, published his article on their website. With in few months, he become one of the most sought after thought leader in the industry. He got the recognition which he had never ever dream of!

 

Moral of the Story: If you are in analytics, take this opportunity and become the next Harry!

 

Announcing AV Blogathon

I am delighted to announce launch of AV Blogathon. We are inviting bloggers to position themselves as data hackers & thought leaders in the industry. Don’t get mislead by the term ‘blogger’. For us, you are a ‘blogger’, if you know the art of expressing yourself in writing.

All you have to do is pen down your article and make a submission. We will not only publish the best of articles on Analytics Vidhya, but also provide feedback to every writer about their article.

We have grown this community through blogs and if there is some one who would understand the value of a well written article – it is us!

Here is a once in a lifetime opportunity when you can make a mark on our audience and stand a chance to win exciting prizes at the same time!

AV Blogathon

 

Registration Process

You can drop in your registration here. You will need to create a profile. If your article gets selected, it would be published with the same id.

 

Win Prizes!

  • Every article which meets Analytics Vidhya standards will get published on Analytics Vidhya. Once it gets published, we’ll ensure that your effort are rewarded.
  • The best articles, as judged by Analytics Vidhya would earn the following prizes:
    • 1st prize – Amazon vouchers worth INR 5,000 (~$100)
    • 2nd prize – Amazon vouchers worth INR 3,000 (~$60)
    • 3rd prize – Amazon vouchers worth INR 2,000 (~ $40)
  • For articles, which don’t meet our standard, we will provide feedback on how to improve the article and your writing.

Don’t let this opportunity pass by you. Its better to act and repent than not to act and regret. Get ready now and start making plans to spread your knowledge.

 

Rules & Regulations

  • You are free to make as many submissions as you want. But, don’t make multiple submissions of the same article.
  • The submissions would open on 15th January 2016, 00:01 IST and will close after 14th February 2016, 23:59 IST (GMT + 5:30 hrs)
  • You are not allowed to re-publish any of your article else where until and unless discussed with Analytics Vidhya.
  • Analytics Vidhya owns the right to disqualify / reject any submission with out any explanation.
  • The decision for best entry, as declared by Analytics Vidhya would be final.

 

Guidelines for Writing Articles

  • All participating bloggers must adhere to the highest standards of creating content that is accurate, informative, well-structured and entertaining at the same time.
  • The sole purpose behind writing and publishing an article should be to add value to the audience. The audience should get something unique and feel value addition after reading the article. This unique feature could be something you have learnt over the years, a trick you might have discovered / invented, tips you have learnt through your experience
  • Remember the old saying “A picture speaks a thousand word”. Same applies to blogging. Visuals, charts and diagrams make your article more readable.
  • Content cannot contain obscene, indecent, or profane material or links to such material.
  • Plagiarism will not be tolerated. If short excerpts of copyrighted material are used, the original source and author must be cited.
  • The purpose of an article cannot be to expressively sell merchandise or promote a business that sells merchandise.
  • The tone of voice on Analytics Vidhya is polite, down to earth and informative. Try and keep the same in your article.
  • Try and keep the article interactive. Invite comments at places, ask questions and take opinions wherever possible.

Sounds like fun? 

Join the competition and get ready to take away a bundle of knowledge and expertise!

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

Anon
Anon

Great to see AV keep up the pace and develop in different directions.

123

Kunal Jain
Kunal Jain

Hi Anon, Glad you liked it. Hoping to see an entry from you. Regards, Kunal

123
abhijit roy
abhijit roy

Great thinking to keep the people engaged and to excel in their skills! AV- Rocks!!

123

Write for us

Write, captivate, and earn accolades and rewards for your work
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  • Build Your Brand & Audience
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