Data Visualization is a technique for presenting information using visual elements, making it accessible and easy to comprehend. It plays a crucial role in all various stages of machine learning and data science. To be effective, data visualizations should be aesthetically simple, creative and informative. In this blog, we explore various processes and examples of data visualization.
In machine learning and data science, API (Application Programming Interface) is a powerful tool that enables seamless communication and data sharing between applications and servers. APIs are mainly used for data gathering and model deployment In data science and ML. This blog provides a step-by-step explanation of how APIs work.
The Apriori Algorithm is a powerful tool in association rule mining that helps to uncover the relationships and associations among items. This technique is widely used by supermarkets and online shopping platforms to optimize product placement and offer discounts on bundled purchases. In this article, we have explained its step-by-step functioning and detailed implementation in Python.
Jupyter Notebook is a popular open-source IDE for data science and machine learning practitioners. It supports three programming languages: Julia, Python, and R. In this guide, we cover the installation process for Jupyter Notebook, as well as provide an overview of the basic steps for starting the server, creating notebooks, executing cells, and working with kernels.
In data science, databases play a crucial role in storing, managing, and scaling large amounts of data. This data is then analyzed to gain meaningful insights. In this blog, we will delve into the concept of databases and understand how data science relies on them, as well as their advantages.
Data Science is a sought-after career option attracting many fresh graduates. The path to becoming a successful data scientist involves following a structured sequence of steps. We have outlined the journey in 7 concise steps and presented a data science job description to make the discussion realistic.
Artificial Neural Networks (ANNs), also known as Neural Networks, are a type of supervised Machine Learning algorithms that can be used to solve both classification and regression problems. In this article, we have discussed the analogy between ANNs and the human brain, the terms used to define a Neural Network, the components present in a schematic diagram of an ANN, the advantages and disadvantages of ANNs compared to other Machine Learning algorithms, and some practical use-cases of ANNs in real-world applications.
In order to fully grasp the concept of a Neural Network, it is crucial to understand the various components that make up a Neural Network. In this article, we delve into the key components of an Artificial Neural Network, including Neurons, Input Layers, Output Layers, Hidden Layers, Connections, Parameters, Activation Functions, Optimization Algorithms, and Cost Functions. These components work together to solve both classification and regression problems in Machine Learning.
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.
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.
Linear Regression is a supervised machine learning algorithm used to solve regression problems. In this blog, we have discussed: 1) What is linear regression? 2) Various types 3) The loss function for linear regression 4) Ordinary Least Squares (OLS) method 5) Polynomial regression 6) Python implementation of linear regression.
In recent years, the quantity of spam emails has decreased significantly due to spam detection and filtering software. In this, We have covered these concepts: 1) Methods to segregate incoming emails into the spam or non-spam categories? 2) Steps to implement a spam classifier using the k-NN algorithm. 3) Real-life use case of Gmail, Outlook, and Yahoo.
Machine Learning and Data Science have become vital in developing life-saving applications, such as drug discovery. Using these ML technologies, researchers can reduce the time needed to create new medicines for known and unknown diseases. In this blog post, we provide a step-by-step guide to building an application that addresses the drug discovery problem using Machine Learning in Python. To solve this regression problem, we utilized the XGBoost regressor model, which is a popular CART algorithm in the ML community.
Every machine learning project needs to go through these steps: Problem finalization, data gathering, data pre-processing, model selection, data splitting, performance evaluation, and model deployment. In this blog, we have discussed: 1) Steps of implementing machine learning projects 2) How to make ML models production-ready?, etc.
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.
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.
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
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.
Many learners want to master machine learning and don’t know where to start. It seems like a formidable task, especially if one lacks a thorough background. This article will discuss some of the factors that can be obstacles in learning machine learning. Working around these obstacles can help us to develop a long-term interest in this subject.
Time series forecasting uses statistical models to predict future values using previously recorded observations. It is classified into two parts: 1) Univariate time series forecasting (Involves a single variable) 2) Multivariate time series forecasting (Involves multiple variables). Note: Time Series is a set of observations taken at a specific periodic time.
Time Series Preprocessing techniques have a significant influence on data modeling accuracy. In this blog, we have discussed: 1) Definition of time-series data and its importance. 2) Preprocessing steps for time series data 3) Structuring time-series data, finding the missing values, denoising the features, and finding the outliers present in the dataset.
When we talk about the machine learning model, one question comes to mind: What are the errors associated with that prediction? Bias and Variance are those error-causing elements, and ideas about these errors will help to diagnose the model. Bias, Variance and Bias-Variance tradeoffs are frequently asked questions in machine learning interviews.
Unlike humans, machines don’t understand words and their semantic context. So, we convert processed text into a format that the machine can understand using vector encoding. In this blog, we will learn: 1) Word embedding 2) Techniques to embed words (One-hot encoding, Word2Vec, TF-IDF, etc) 3) Implementation of all these embeddings.
We need to clean the text data before feeding it to machine learning algorithms. Fortunately, Python has excellent support for NLP libraries (NLTK, spaCyto) to ease text analysis. In this blog, we will learn: 1) Real-time working on the sentiment analysis dataset 2) Techniques for cleaning text data. 3) Exploratory analysis of text data.
Artificial intelligence and machine learning are the most famous buzzwords in the technical industries. We generally use them as synonyms, but these tech stacks are different, although machine learning is just a part of artificial intelligence. In this blog, we will discuss the basic comparison between artificial intelligence and machine learning.
Machine Learning has become a tool used in almost every task that requires estimation. Companies like Cars24 and Cardekho.com uses Regression analysis to estimate the used car prices. So we need to build a model to estimate the price of used cars. The model should take car-related parameters and output a selling price.
We sometimes overemphasize the capabilities of machine learning. Here are some popular misconceptions: 1) ML, DL, and AI are the same 2) Machine learning can predict the future 3) Machine learning can solve any problem 4) One machine learning algorithm will be sufficient 5) More features in the data, better will be the machine learning model, etc.
In Machine Learning, a machine learns by using algorithms and statistical models to identify patterns in data. Here the process of learning begins with feeding a large amount of training data to the algorithm. The algorithm then uses this data to make predictions or take actions based on the patterns it has identified. In other words, the algorithm constantly adjusts its parameters to minimize the difference between its predictions and actual outcomes.
This is a glossary of machine learning terms commonly used in the industry. Some popular machine learning terminologies: neural networks, supervised learning, unsupervised learning, reinforcement learning, regularization, classification, regression, clustering, optimizers, outliers, bias, variance, underfitting, overfitting, normalization, etc.
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