data-science

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What is Data Science? Introduction and Importance

Data Science, the application of scientific methods to extract valuable insights from data, is crucial for businesses to perform various analyses. In this paragraph, we discuss the importance of Data Science, its usage in businesses, the roles and responsibilities of a data scientist, the necessary skills for a career in Data Science, the distinctions between Data Science, Machine Learning, Data Engineering, and Business Analysis, and the challenges faced by data scientists.

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What is Big Data? Introduction, Types, Characteristics

Big Data, the stage of data that exceeds the capabilities of traditional storage, analytical and processing methods, requires specialized solutions like the Hadoop framework. In this article, we explore the characteristics, types, examples, advantages, and challenges of Big Data and how Hadoop supports its use-cases.

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Feature Selection Techniques in ML

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.

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Drug Discovery using Machine Learning

Machine learning has become so advanced that it is being used for drug discovery, which reduces the time needed to produce a new drug. In this blog, we have discussed: 1) Use cases of drug discovery problem 2) Steps involved in drug discovery 3) Implementation steps of XGBoost regressor model 4) Active and inactive compounds 4) Need of fingerprints, etc.

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Music Recommendation System Using Machine Learning

Learn to build a music recommendation system using the k-means algorithm. We will use the audio features from the million song data and cluster them based on their similarities. In this blog, we will be discussing these topics: 1) Methods to build a recommendation system for songs 2) Step-wise implementation 3) Ordering songs for the recommendation, etc.

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Image Compression Using Machine Learning

In this blog, we will build an image data compressor using unsupervised learning technique, Process Component Analysis. We will be discussing these topics: 1) Image types and quantization 2) PCA overview 3) Step-wise implementation of PCA for image compression. 4) Techniques to optimize the tradeoff between compression and the number of components.

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Introduction to Pandas in Python

Pandas is a famous library package of Python used by data scientists and analysts for data understanding, data preprocessing, and much more. It provides us with numerous tools to do these manipulations and analysis efficiently. In this blog, We will cover installation and all the basic Pandas functions frequently used while building machine learning projects.

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Data Types and Variables in Python Programming

Python is the most preferred language for developing machine learning and data science applications. It has a large community support that can help debug the errors and resolve all the roadblocks appearing while developing any solution. In this blog, we have discussed various data types, expressions, variables and string operations in python.

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Conditions and Branching in Python

We sometimes need to execute specific instructions only when some conditions are true. If not, then we will perform a different set of instructions. In this blog, we have discussed: 1) Various comparison operations in Python. 2) What are conditions in python? 3) What is branching? 3) How do we use logical operations to combine the two conditions? etc.

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Introduction to NumPy in Python

Numpy is considered one of the most used python libraries. In this blog, we have discussed: 1) What is NumPy? 2) Python lists vs. NumPy array 3) Shape, reshaping, squeezing, expanding, slicing and indexing of Numpy arrays 4) Concatenating, stacking, broadcasting of NumPy arrays 5) Mathematical operations on Numpy arrays.

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Loop in Python Programming

Loops are the set of instructions that needs to be executed until a defined condition is satisfied. In this blog, we have discussed: 1) What is the range function in python? 2) How does the loop work? 3) for loop in python 4) while loop in python 4) How can we make conditional loops in python? 5) Use of Continue and Break statements in a loop.

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Functions in Python Programming

Functions are a set of instructions grouped in a block and get executed only when it is called inside our program. In python programming, functions follow specific syntaxes to ensure their validity. In this blog, we have discussed: 1) What are functions in python? 2) How to create and call functions? 4) Various function arguments? 5)The anonymous function.

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Introduction to OOPS in Python Programming

In Python, everything is an object which holds different properties and methods. Class is a blueprint that creates these objects. In this blog, we have explained fundamental oops concepts in python: 1) What are classes and objects? 2) How to use classes and objects? 3) Default classes examples in python 4) Abstraction, Inheritance and Polymorphism.

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Introduction to Seaborn in Python

Seaborn is an open-source library built over Matplotlib and makes plots more appealing and understandable. It works excellently with data frames and pandas libraries. In this blog, we have discussed: 1) Advantages of Seaborn over Matplotlib library, 2) Installation process of Seaborn in Python 3) Various Data Plots using the Seaborn library.

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Matplotlib Library in Python

Matplotlib is one of Python's most effective visualization libraries for data visualization. It is an open-source library built over NumPy arrays. In this blog, we have discussed: 1) What is Matplotlib 2) Installation of Matplotlib using PIP 3) What is Pyplot in Matplotlib 4) The subplot in Matplotlib's pyplot module 5) Various plots using Matplotlib.

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Sets and Dictionaries in Pyhton Programming

In python, sets and dictionaries are unordered data structures frequently used in machine learning applications. In this blog, we have explained these concepts: 1) What is set in python? 2) Various operations on sets 3) Conversion of lists into sets 4) What is dictionary python? 5) Various operations on dictionaries? 6) Comparison of sets and dictionaries.

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Sentiment Analysis using Naive Bayes

Sentiment analysis is a technique that comes under natural language processing(NLP) and is used to predict emotions reflected by a word or a group of words. Sentiment analysis is instrumental in brand monitoring, market research, social media monitoring, etc. This blog will discuss naive bayes to predict sentiments using their tweets.

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Tuples and Lists in Python Programming

Tuples and lists are popular python data structures. They are also called compound data types because they can store a mixture of primitive data types like strings, ints, and floats. Tuples are ordered sequences of the same or mixed data types enclosed in smaller parentheses. Lists store an ordered sequence of similar or different data type python objects.

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Python Date and Time

As data scientists, we should know how to handle the date-time data and the standard set of date-time operations we can apply to transform the raw data. Fortunately, we have date-time manipulation libraries specifically for this purpose. In this blog, we will talk about all basic date-time manipulations, explorations, transformations, and applications.

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t-SNE Algorithm in Machine Learning

t-SNE (t-distributed stochastic neighbor embedding) is a non-linear dimensionality reduction algorithm used for exploring high-dimensional data. In this blog, we have discussed: 1) What is t-SNE? 2) t-SNE vs PCA 3) How t-SNE algorithm works? 4) Concept of similarity? 5) Python implementation of t-SNE 6) Mathematical analysis of t-SNE algorithm.

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Applications of Regex in Data Science

In this blog, we will focus on applications of regex by implementing it to some tedious tasks that wouldn’t be possible without regular expressions. Some standard applications of regular expressions in data science: 1) Web scraping and data collection 2) Text preprocessing (NLP) 3) Pattern detection for IDs, e-mails, names 4) Date-time manipulations

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Employee Attrition Prediction using ML

In this blog, we have demonstrated data analysis of the company's attrition rate and built a machine learning model (logistic regression model) to predict it. We have explored some exciting patterns that lead to employee attrition. We will be using Kaggle's IBM HR analytics Employee Attrition and Performance dataset for this analysis.

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Regular Expression in ML (Python)

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

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Anomaly Detection in Machine Learning

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 use anomaly detection algorithms to detect fraud transactions.

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K-Means Clustering Algorithm in ML

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 predefined K distinct and non-overlapping partitions. These partitions are called clusters, and the value of K depends upon the user's choice.

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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.

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Data Processing 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.

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Principal Component Analysis (PCA) in Machine Learning

Principle component analysis (PCA) is an unsupervised learning technique to reduce data dimensionality consisting of interrelated attributes. The PCA algorithm transforms data attributes into a newer set of attributes called principal components (PCs). In this blog, we will discuss the dimensionality reduction method and steps to implement the PCA algorithm.

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