Selva Prabhakaran

Selva is the Chief Author and Editor of Machine Learning Plus, with 4 Million+ readership. He has authored courses and books with100K+ students, and is the Principal Data Scientist of a global firm.

Top 50 matplotlib Visualizations – The Master Plots (with full python code)

A compilation of the Top 50 matplotlib plots most useful in data analysis and visualization. This list lets you choose what visualization to show for what situation using python’s matplotlib and seaborn library. Introduction The charts are grouped based on the 7 different purposes of your visualization objective. For example, if you want to picturize …

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Naive Bayes Feature

How Naive Bayes Algorithm Works? (with example and full code)

Naive Bayes is a probabilistic machine learning algorithm based on the Bayes Theorem, used in a wide variety of classification tasks. In this post, you will gain a clear and complete understanding of the Naive Bayes algorithm and all necessary concepts so that there is no room for doubts or gap in understanding. Contents 1. …

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parallel processing python

Parallel Processing in Python – A Practical Guide with Examples

Parallel processing is a mode of operation where the task is executed simultaneously in multiple processors in the same computer. It is meant to reduce the overall processing time. In this tutorial, you’ll understand the procedure to parallelize any typical logic using python’s multiprocessing module. Contents 1. Introduction 2. How many maximum parallel processes can …

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Cosine Similarity – Understanding the math and how it works (with python codes)

Cosine similarity is a metric used to measure how similar the documents are irrespective of their size. Mathematically, it measures the cosine of the angle between two vectors projected in a multi-dimensional space. The cosine similarity is advantageous because even if the two similar documents are far apart by the Euclidean distance (due to the …

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Gensim Tutorial

Gensim Tutorial – A Complete Beginners Guide

Gensim is billed as a Natural Language Processing package that does ‘Topic Modeling for Humans’. But it is practically much more than that. It is a leading and a state-of-the-art package for processing texts, working with word vector models (such as Word2Vec, FastText etc) and for building topic models. Gensim Tutorial – A Complete Beginners …

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Lemmatization Approaches with Examples in Python

Lemmatization is the process of converting a word to its base form. The difference between stemming and lemmatization is, lemmatization considers the context and converts the word to its meaningful base form, whereas stemming just removes the last few characters, often leading to incorrect meanings and spelling errors. Comparing Lemmatization Approaches in Python. Photo by …

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Feature Selection – Ten Effective Techniques with Examples

In machine learning, Feature selection is the process of choosing variables that are useful in predicting the response (Y). It is considered a good practice to identify which features are important when building predictive models. In this post, you will see how to implement 10 powerful feature selection approaches in R. Introduction 1. Boruta 2. …

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LDA in Python – How to grid search best topic models?

Python’s Scikit Learn provides a convenient interface for topic modeling using algorithms like Latent Dirichlet allocation(LDA), LSI and Non-Negative Matrix Factorization. In this tutorial, you will learn how to build the best possible LDA topic model and explore how to showcase the outputs as meaningful results. Contents 1. Introduction 2. Load the packages 3. Import …

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Caret Package

Caret Package – A Practical Guide to Machine Learning in R

Caret Package is a comprehensive framework for building machine learning models in R. In this tutorial, I explain nearly all the core features of the caret package and walk you through the step-by-step process of building predictive models. Be it a decision tree or xgboost, caret helps to find the optimal model in the shortest …

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Numpy Tutorial Part1

Python Numpy – Introduction to ndarray [Part 1]

This is part 1 of the numpy tutorial covering all the core aspects of performing data manipulation and analysis with numpy’s ndarrays. Numpy is the most basic and a powerful package for scientific computing and data manipulation in python. Numpy Tutorial Part 1: Introduction to Arrays. Photo by Bryce Canyon. Related Posts Numpy Tutorial – …

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Python Regex

Python Regular Expressions Tutorial and Examples: A Simplified Guide

Regular expressions, also called regex, is a syntax or rather a language to search, extract and manipulate specific string patterns from a larger text. It is widely used in projects that involve text validation, NLP and text mining Regular Expressions in Python: A Simplified Tutorial. Photo by Sarah Crutchfield. Contents Introduction to regular expressions What …

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Top 15 Evaluation Metrics for Classification Models

Choosing the right evaluation metric for classification models is important to the success of a machine learning app. Monitoring only the ‘accuracy score’ gives an incomplete picture of your model’s performance and can impact the effectiveness. So, consider the following 15 evaluation metrics before you finalize on the KPIs of your classifier model. Introduction: Building …

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Logistic Regression with R

Logistic Regression – A Complete Tutorial With Examples in R

Logistic regression is a predictive modelling algorithm that is used when the Y variable is binary categorical. That is, it can take only two values like 1 or 0. The goal is to determine a mathematical equation that can be used to predict the probability of event 1. Once the equation is established, it can …

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Machine Learning A-Z™: Hands-On Python & R In Data Science

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Machine Learning A-Z™: Hands-On Python & R In Data Science

Machine Learning A-Z™: Hands-On Python & R In Data Science

Machine Learning A-Z™: Hands-On Python & R In Data Science

Machine Learning A-Z™: Hands-On Python & R In Data Science

Machine Learning A-Z™: Hands-On Python & R In Data Science