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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

Blogathon 18 is Live Now! Total Prizes Worth INR 6 Lakh(≅USD 8000)

Sakshi Raheja
Sakshi Raheja Last Updated : 07 Apr, 2022
4 min read

Analytics Vidhya is back with another edition of Blogathon which is live now! Click here to register!

 

“There is no greater agony than bearing an untold story inside you.”

                                                                                                               Maya Angelou

Introduction

Data Science Blogathon began with a mission to build a community where our data scientist enthusiasts are able to share their knowledge with the world. Our community is growing with each month, adding valuable authors and readers each month! We have been successfully able to publish approximately 2000 articles. We know how our Data Scientists are always excited to share their stories, through building different models via computing codes. And guess what? Analytics Vidhya made it more interesting by rewarding you for those exciting stories.

As we celebrate Women’s Day today, we are happy to share that Blogathon 18 has a special rewards category for Women. Hang on and read more!

Why should you Participate in Blogathon?

You might be wondering – what is in it for you to write a data science blog? Well, there are plenty of benefits!

  1. Learn and master the concept – Learning and writing go hand-in-hand. When we make notes for our exams, we tend to remember them better. Isn’t it? Once you begin writing an article on any concept, you will not only learn a new concept but will master the art of doing ‘research’ for that particular topic.
  2. Improve your storytelling skills – I will share a little secret with you here. Two of the most important characteristics of a great data scientist are storytelling skills and a structured thinking mindset. This is not an inherent quality but is developed by working closely on problem statements. Writing a blog improves both of these qualities.
  3. Build up your resume – One of the biggest advantages of writing a data science blog is that it makes you stand out from the crowd. Most of you already know that Analytics Vidhya’s Blogathon series is the second largest in the World. Thus, your articles are being read by a global audience which not only gives you feedback, and readership but also, helps you build up your resume and LinkedIn.
  4. Get attention and networking opportunities within the community – While gaining knowledge through the process of blog writing is important but getting a chance to network with industry leaders and like-minded people is priceless. Analytics Vidhya has 2.5 million monthly users including some of the well-renowned personalities so there’s a good chance of reaching potential people and employers.
  5. Win cash prizes – All of this is great but we all crave a reward for our efforts. Isn’t it? Don’t worry, cash prizes worth thousands are waiting for you. Read ahead to learn about all the rewards.

How can you Participate in Blogathon?

You simply need to register on the link to enter the competition. This link will lead you to all the required details about the Blogathon. Once you register, you will be led to our editor platform where you could begin your writing journey! If you face any issues or need assistance then, please reach out to us directly on this email for faster communication: [email protected].

Did you Know?

Women make up only approximately 15% to 22% of all data science professionals, according to Global Gender Gap Report by WEF. Analytics Vidhya aims to do its bit and bridge this gap through this Blogathon. As we’re celebrating Women in this Blogathon, we would be rewarding Top 10 Women Authors by adding INR 1500 to their final amount.

What are the Prizes on Offer?

Choose the Data Science content you want to create and win for each published article. There are two categories: Articles (1000 words)  and Guides (3000 words) and each of them has a different cash amount for AVCC members and non-members.

Prizes

Mega Cash Rewards

Mega Cash Rewards | Blogathon 18

 

A Special Award Category – Win the new iPad!

You read that right – you even stand an opportunity to win the new iPad! Every author in the blogathon will be eligible to win an iPad through a lucky draw where a single winner will be chosen!

iPad | Blogathon 18

This is an unmissable opportunity to get your hands on the latest Apple iPad that has the world swooning. Imagine the writing and reading you’ll be able to accomplish on this blazing fast tablet.

Important Dates and Deadlines

We don’t want you to miss any opportunity. So, make sure you don’t miss any of these below dates:

Important Dates | Blogathon 18

 

If you wish to take a look at our blog then, here is the link.

Conclusion

That’s all folks! Head on to the registration page to know more details and some terms and conditions that are important for you to adhere to. We are excited for you to participate and contribute to our community!

Analytics VidhyaArtificial Intelligenceblogathondata sciencedeep learningmachine learning
Sakshi Raheja
Sakshi Raheja

I am a passionate writer and avid reader who finds joy in weaving stories through the lens of data analytics and visualization. With a knack for blending creativity with numbers, I transform complex datasets into compelling narratives. Whether it's writing insightful blogs or crafting visual stories from data, I navigate both worlds with ease and enthusiasm. 

A lover of both chai and coffee, I believe the right brew sparks creativity and sharpens focus—fueling my journey in the ever-evolving field of analytics. For me, every dataset holds a story, and I am always on a quest to uncover it.

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The Biggest Data Science Blogathon – Prizes Worth INR 4 Lakh
The Biggest Data Science Blogathon – Prizes Worth INR 4 Lakh

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