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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

Analytics Vidhya’s Top 10 Machine Learning Blogs in 2022

Sanjana Rajpal
Sanjana Rajpal Last Updated : 20 Feb, 2024
5 min read

Introduction

Though machine learning isn’t a relatively new concept, organizations are increasingly switching to big data and ML models to unleash hidden insights from data, scale their operations better, and predict and confront any underlying business challenges. All this positively impacts the ML industry while opening up new career avenues, job roles, a plethora of opportunities to harness this field, and much more.

Machine Learning

In line with our quest for knowledge sharing with the world, here are the top 10 blogs on machine learning published on Analytics Vidhya in 2022. The blogs have been chosen based on how impeccably they are written, the maximum number of views, and engagement. We hope this list will help you get your pressing questions about ML answered and give you an opportunity to connect with those who have the knowledge and experience to use them to a greater benefit.

So, Let’s dive into the concepts of machine learning.

1. Different Types of Regression Models

Ranking right at the first spot amongst the top 10 blogs on machine learning published on Analytics Vidhya in 2022 is a spotless work by author Prashant Sharma. The blog revolves around different types of regression models and is a technically-sound piece of information. 

machine learning

2. Diabetes Prediction Using Machine Learning

Written by one of our top guest authors Aman Preet Gulati, this blog on ‘Diabetes Prediction Using Machine Learning’ is at the second spot in the list of top 10 blogs on machine learning published on AV in 2022. The blog talks about an ML model, Pima Indians Diabetes Database, and its features that help ascertain if a person has diabetes.

The article predicts whether the patient has diabetes or not on the basis of the features, we will provide to our machine learning model, and for that, we will be using the famous Pima Indians Diabetes Database. It reads the dataset, which is in the CSV format

3. Complete Guide on Rest API with Python and Flask

At the third spot amongst the list of top 10 blogs on machine learning published on AV in 2022 is a creation by author Raghav Agrawal on the topic “Rest API with Python and Flask.” The article is crafted for advanced machine learning professionals and covers all the doubts about Rest API. The blog is a must-read for those who understand Python and Flask.

Rest API is now growing as the most common method for connecting components in a microservice architecture. APIs will enable you to get or send data to a website and perform some action to get your task done over a web service. Ticket booking apps use a desired single portal API to keep updated data at a familiar interface. The article creates the Hello world API, which says that if you hit a get request on it, you will get a JSON response because it’s universal that API always gives a JSON-type response.

4. Heart Disease Prediction Using Machine Learning

Securing the fourth spot in the list of top 10 blogs on machine learning published on AV in 2022 is a blog about ‘Heart Disease Prediction Using Machine Learning’ coined by author Aman Preet Gulati. The blog is technically-dense and covers everything to know about heart disease prediction. Created by one of our popular guest authors, Aman Preet Gulati, this blog is a must-read for all inclined toward harnessing ML’s power in healthcare to reap powerful outcomes.

In this article, we will be closely working with the heart disease prediction and will look into the heart disease dataset. From that dataset, we will derive various insights that help us know the weightage of each feature and how they are interrelated to each other, but our sole aim this time is to detect the probability of a person being affected by a saviour’s heart problem or not.

5. K-Fold Cross Validation Technique and its Essentials

Yet another well-written and deeply explained blog amongst the list of top 10 blogs on machine learning published on AV in 2022 is about ‘K-Fold Cross Validation Technique and its Essentials’ authored by Shanthababu Pandian. The blog sheds light on K-Fold Cross Validation techniques, model performance, its significance, the accuracy of the model and performance, the life cycle of K-Fold cross-validation, its key rules, model selection, parameter tuning, and K-Fold in visual form.

6. Loan Approval Prediction Machine Learning

Author Vedansh Shrivastava penned down a technically-rich and informative article on “Loan Approval Prediction Machine Learning” wherein he explained a classification problem of whether the loan will be provided or not. If you have doubts about classification problems, this blog is a must-read. The author explained classification as a predictive modeling problem where a class label is predicted for a given example of input data. 

7. Implementing Logistic Regression from Scratch using Python

“Loan Approval Prediction Machine Learning” by author Sunil Kumar is yet another one of the best blogs to read on machine learning. The article is apt for beginners just starting out in the field of ML. You’d find the concept of implementing logistic regression explained to the ‘T’ also touching upon subjects as importing libraries for logistic regression, standardization, initializing parameters, sigmoid function, cost function, gradient descent, prediction, F-1 score, and more.

logistic regression | machine learning

If you have already been introduced to the concept of logistic regression and are looking for an informative article to help you implement the same using Python, bookmark this right away.

8. Image Classification Using Machine Learning

If you’re looking for a deeply explained informative article on how to perform image classification using machine learning, this is the right spot for you. Penned by one of our best authors, the article is a comprehensive put-together work that helps explain to readers why Deep Learning is preferred for image classification. The concepts explained and demonstrated in this article will help serve research purposes if one desires to compare their CNN image classifier model with some machine learning algorithms. 

9. Flight Fare Prediction Using Machine Learning

Suppose you’re looking for a wholesome and well-explained write-up on analyzing flight fare prediction using Machine Learning dataset using essential exploratory data analysis techniques. In that case, we suggest you bookmark it right away. Penned by author Aman Preet Gulati, the article explains how to draw some predictions about the price of the flight based on some features such as what type of airline it is, what is the arrival time, what is the departure time, what is the duration of the flight, source, destination and more.

10. Linear Regression with Python Implementation

If you have a basic understanding of linear regression and ML, this article further adds up to your knowledge by explaining all related concepts in a detailed manner. Written by author Shailash M, it is one of the best blogs on ML on Analytics Vidhya in 2022. The article touches upon an introduction to ML, different types of ML algorithms (supervised, unsupervised, and reinforcement machine learning), regression, implementation of linear regression, and more.

Conclusion 

We hope you have bookmarked the above list of top 10 blogs on machine learning published on Analytics Vidhya in 2022. ML is slowly and gradually paving its way to becoming one of the most viable propositions we have today. If you’re keen to explore the world of machine learning or have already begun this amazing journey, do check out the above blogs to have all-encompassing ML knowledge.

Also, please comment below how did you like our work and what other topic you want us to cover in the next articles. We would love to hear from you!

Sanjana Rajpal
Sanjana Rajpal

A highly motivated Content Writer with knowledge of blogging, copywriting, SEO, and content framework creation. I am familiar with basic knowledge of analyzing blog traffic and deriving actionable insights.

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