Data Insight Mag
@DataInsightMag
New Online Publication. .... Solving complex problems with data! #MachineLearning #DataScience #ArtificialIntelligence #DataAnalytics
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In the world of technology and data, precision is key. This Christmas, may every moment be precisely filled with happiness and warmth. Merry Christmas
Getting Started with Machine Learning: An Overview for Beginners datainsightmag.com/getting-starte…
On this special day, we celebrate the invaluable contributions of data scientists everywhere. Happy Workers' Day to all who labor tirelessly to unlock the potential of data and drive progress in our interconnected world. #Data #workerday
Lastly, Effective communication and collaboration with domain experts, stakeholders, and team members are essential for a successful data science workflow. #Data #MachineLearning #DataModel #DataScientists #DataScience #IoT
Throughout these stages, it's important to note that data science is an iterative process You might find yourself going back and forth between stages as you uncover new insights, encounter challenges, and refine your approach. #Data #DataScience #DATAMATICS #MachineLearning
6. Model Deployment After developing a successful model, the next step is to deploy it for real-world use This involves integrating the model into a production environment, creating APIs for interaction, and ensuring the model can handle incoming data reliably and efficiently.
5 Model Building This stage, you design, train, and evaluate machine learning models using preprocessed data & engineered features You select appropriate algorithms based on the problem you're solving (classification,regression) tune their hyperparameters assess their performance
4 Feature Engineering This involves selecting, transforming &creating relevant features from your data to improve the performance of #ML models This stage requires domain knowledge to extract meaningful information &create features that capture the underlying patterns in the data
3. Data Visualization This is a powerful tool for communicating findings& insights from your data You can present complex information in a more understandable way by using graphs, charts etc Data viz helps decision-makers &stakeholders understand the significance of your analysis
2 EDA EDA is the process of summarizing & visualizing the main characteristics, patterns &relationships within a dataset EDA assists you in understanding the data structure,identifying missing values,outliers & anomalies &gaining insights that will guide subsequent workflow steps
1. Programming Programming is the foundation of any data science project. It involves writing code to manipulate, clean, & preprocess data and to implement algorithms & models. #Python & #R are popular programming languages for data science due to their rich libraries and tools.
6 Stage of Workflow in Data Science 1. Programming 2. Exploratory Data Analysis 3. Data Visualization 4. Feature Engineering 5. Model Building 6. Model Deployment #AI #ArtificialIntelligence #DataScience #MachineLearning #DigitalTransformation #data #dataviz
Hi guys I did a quick analysis om the most googled presidential aspirants in Nigeria. @DavidHundeyin Labour party Go and Verify #Obidiots #Obidients She's 41 #SuperFreakyGirl Wike
Hi guys, I did an analysis of cars in Nigeria. Here's the dashboard created with Power Bl. #DataAnalytics
Machine learning is a subset of AI that deals with algorithms, statistical models, and analytics. Machines have historically been programmed to obey certain instructions given to them, and have not had the opportunity to make decisions. datainsightmag.com/introduction-t…
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