data-science

Introduction to Pandas For Beginners Cover Image
Introduction to Pandas For Beginners

Pandas is a famous library package of Python used by Data scientists and analysts for data understanding, data preprocessing, and much more. we will cover installation and all the basic Pandas functions frequently used while building machine learning projects.

Conditions and Branching In Python Cover Image
Conditions and Branching In Python

In this blog, we have discussed techniques of conditions and branching using which the instructions of our program get executed.

Machine learning in Drug Discovery Cover Image
Machine learning in Drug Discovery

In this article, we want to identify the compounds with the best effect on Acetylcholinesterase protein using XGBoost Machine Learning Algorithm. We will be using the famous ‘Chembl’ database to demonstrate the complete use-case.

Python Data Structures for ML and Data Science: Sets and Dictionaries Cover Image
Python Data Structures for ML and Data Science: Sets and Dictionaries

Here we will cover two unordered data structures frequently used in Artificial Intelligence applications: Sets and Dictionaries.

Sentiment Analysis using Naive Bayes Cover Image
Sentiment Analysis using Naive Bayes

Sentiment Analysis is a technique that comes under natural language processing(NLP) and is used to predict the emotions reflected by a word or a group of words. In this blog, we will discuss one of the simplest probabilistic algorithms, Naive Bayes to predict the sentiments using their tweets.

Python Data Structures for ML and Data Science: Tuples and Lists Cover Image
Python Data Structures for ML and Data Science: Tuples and Lists

In this article, we will discuss two data structures tuples and lists in Python. They are frequently used in Machine Learning and Data Science. These data types are also called compound data types because they can store mixture of primitive data types like Strings, ints, and floats.

Handling Date & Time in Python Cover Image
Handling Date & Time in Python

Temporal attributes are crucial since they help clarify the cyclic trends in data. This article will talk about all basic date-time manipulations, explorations, transformations, and some miscellaneous applications.

t-SNE Algorithm In Machine Learning Cover Image
t-SNE Algorithm In Machine Learning

t-SNE is a non-linear dimensionality reduction algorithm used for exploring high-dimensional data. It stands for t-distributed Stochastic Neighbor Embedding, where t represents "t-distribution”.

Python Basics for Data Science and Machine Learning: Part 1 Cover Image
Python Basics for Data Science and Machine Learning: Part 1

Python is the most preferred language for developing Machine Learning and Data Science applications. It has a large community support that can help debugging the errors and resolving all the roadblocks appearing while developing any solution.

Applications of Regex in Data Science Cover Image
Applications of Regex in Data Science

In this blog, we will focus on the industrial applications of regex by implementing it to some tedious tasks that wouldn’t be possible without regular expressions, like Web-Scrapping & Data Collection, text processing and pattern matching.

Employee Attrition Rate Prediction Using Machine Learning Cover Image
Employee Attrition Rate Prediction Using Machine Learning

Here we have demonstrated a deeper data analysis of company's attrition rate and built a logistic regression model to predict it. This project can help management team to control the project pipeline efficiently.

Introduction to Regular Expression in Python Cover Image
Introduction to Regular Expression in Python

Regular expression is an expression that holds a defined search pattern to extract the pattern-specific strings. Today, RE are available for almost every high-level programming language and as data scientists or NLP engineers, we should know the basics of regular expressions and when to use them.

Feature Selection Techniques in Machine Learning Cover Image
Feature Selection Techniques in Machine Learning

The best machine learninmodel would have the lowest number of features involved in the analysis keeping the performance high. Therefore, determining the relevant features for the model building phase is necessary. In this session, we will see some feature selection methods and discuss the pros and cons of each.

Anomaly Detection in Machine Learning Cover Image
Anomaly Detection in Machine Learning

Anomaly detection is a process of finding samples behaving abnormally compared to the majority of samples present in the dataset. Anomaly detection algorithms have important use-cases in Data Analytics and Data Science fields. For example, fraud analysts rely on anomaly detection algorithms to detect fraud in transactions.

K-Means Clustering Algorithm in Machine Learning Cover Image
K-Means Clustering Algorithm in Machine Learning

The clustering technique is prevalent in many fields, so many algorithms exist to perform it. K-means is one of them! K-means is an unsupervised learning technique used to partition the data into pre-defined K distinct and non-overlapping partitions. These partitions are called clusters, and the value of K depends upon the user's choice.

Exploratory Data Analysis: Univariate, Bivariate, and Multivariate Analysis Cover Image
Exploratory Data Analysis: Univariate, Bivariate, and Multivariate Analysis

Exploratory data analysis can be classified as Univariate, Bivariate, and Multivariate analysis. Univariate refers to the analysis involving a single variable; Bivariate refers to the analysis between two variables, and Multivariate refers to the statistical procedure for analyzing the data involving more than two variables.

Data Pre-processing of Structured Data in Machine Learning Cover Image
Data Pre-processing of Structured Data in Machine Learning

Nowadays, data collection is one of the most common trends, and every company collects data for various uses. When they record any form of data, it comes with multiple impurities. So data preprocessing techniques are used to remove impurities from data and make it useful for training machine learning models.

Principal Component Analysis (PCA) in Machine Learning Cover Image
Principal Component Analysis (PCA) in Machine Learning

Principle Component Analysis (PCA) is an unsupervised learning technique to reduce data dimensionality consisting of many inter-related attributes. The PCA algorithm transforms data attributes into a newer set of attributes called Principal Components (PCs).

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