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NeuAI Labs
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A day in the life of a data scientist involves deciphering insights, wrangling complex datasets, and crafting algorithms. From morning EDA sessions to evening model fine-tuning, each day is a dynamic journey of transforming raw data into actionable intelligence, shaping the future with every analyzed byte.Join the NeuAI Labs Data Scientist Course and give a kick-start to your career.For more info:Call Us: 9021152995Visit us: www.neuailabs.com#datascience#datascientist#datascientistlife#datascientistjobs#dataanalytics#dataanalysis#datacollection#datacleaning#generatinginsights#buildingalgorithms#buildingtrainingdata#datasciencecourse#datascienceinternship#datasciencetraining#neuailabs#futureofai
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Samuel Jude Philips
Data science, Machine Learning, Deep learning, Feature engineering, Neural Networks, NLP
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I am so glad to exhibit that I am selected for the Data Science and Machine Learning Internship from one of the most reputed and high-level Research centers in India (Gilbert Research Center). Finally, will soon elevate to a data science intern and not a data science fresher anymore. My awaited curiosity to finally learn and work on real-time data and real-time projects shall be fulfilled. Thank you so much for the opportunity Gilbert Research Center.The corporate job market and especially the tech sector isn't at it's best place right now, with a lot of adjustments and decisions being made with AI and it's reliability reaching peaks. Only the most advanced and well-versed stand a chance to get into or stay in the dream companies.To all the tech aspirants who aren't crazy intellectual, and find learning hard just like me. Never stop. Even if it takes span of years. This field, this domain requires constant upgrade in learning, information, knowledge and skills. Grab every opportunity, most important being the small ones. We all need to climb the first step in order to reach the next step and the next step and...........and then the top and I am so doing that after understanding that there is no shortcut to that 60 LPA job. Keep hustlin!Peace out.
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Bijo Varghese M T
Big Data Data Science Intern at luminar Technolab | Mathematics | Python | Bigdata| SQL | Power BI | Hadoop | Spark | Machine Learning
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IRIS FLOWER CLASSIFICATIONI am excited to present my machine learning project on classification using the Iris dataset as part of my application for the internship at CognoRise InfoTech. This project demonstrates my proficiency in data analysis, feature engineering, and model development.The Iris dataset is a classic dataset in the field of machine learning and is commonly used for classification tasks. It consists of 150 samples from three different species of Iris flowers (Iris setosa, Iris versicolor, and Iris virginica), with 50 samples per class. Each sample includes four feature measurements: sepal length, sepal width, petal length, and petal width.1)Data Exploration2)Data Preprocessing3)Feature Engineering4)Model Development5)Model EvaluationI achieved high classification accuracy, demonstrating the model's effectiveness in predicting the Iris species. Technologies and Tools:PythonScikit-LearnNumPy and Pandas for data manipulationMatplotlib and Seaborn for data visualizationCognoRise InfoTech#DecisionTreeClassifier
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Manish Kumar Shah
AI Enthusiast 🤖 | AI & Tech Content Creator 👨💻 | Sharing Latest AI Tools ⚡| Web Developer 🌐 | 150K+ Instagram & Telegram Community 🚀 | Helping Client's to Grow their Business 📈 | DM for Promotion 📩
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🚀 𝐑𝐨𝐥𝐞𝐬 𝐚𝐧𝐝 𝐑𝐞𝐬𝐩𝐨𝐧𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬 𝐨𝐟 𝐃𝐀, 𝐃𝐒 & 𝐌𝐋𝐄 & 𝐃𝐄.𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁: A Data Analyst interprets complex data sets, often using statistical techniques to provide insights and help inform business decisions. They also visualize data, create reports, and ensure data quality and accuracy.𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁: A Data Scientist not only analyzes and interprets complex data, but also uses machine learning techniques to build predictive models. They use advanced statistical models and algorithms to extract meaningful insights and create data-driven solutions.𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿: Machine Learning Engineers design, implement and maintain machine learning systems in technology products. They work on creating algorithms, prototyping models.𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿: A Data Engineer is responsible for the design, construction, and maintenance of the data architecture, databases, and processing systems. They ensure data availability and manage big data projects, transforming data into a format that can be easily analyzed.📌 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐩𝐫𝐨𝐠𝐫𝐚𝐦 𝐬𝐭𝐚𝐫𝐭𝐬 𝐨𝐧 𝐀𝐮𝐠𝐮𝐬𝐭 19, 𝟐𝟎𝟐𝟑! Don't miss this opportunity to jumpstart your career as a Data Scientist. For more details and to secure your spot, visit our website at http://digyquant.com/ or contact us at +91-6381057440.👉👉Keep yourself informed by following DigyQuant Analytics.#internshala #jobs #business #engineer #growth #fresher #lookingforjob #internship #career #careerdevelopment #coding #programming
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Heerthi Raja H
Intern @Jarvislabs.ai | Computer Vision Engineer | CV/Robotics Enthusiast | Sharing my lessons | Learning and building in public!
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Bad news for juniorsOut of 4,325 Data Scientist job listings, a mere 4% are targeted toward Junior or Intern positions. Similarly, Machine Learning Engineer positions, which totaled 2,732, showed that only 3% were available for those at the junior or intern level. Even more striking is the scenario in ML Ops – with a sample of 1,367 jobs, only a scant 1% were reserved for newcomers.”This was an extract from this interestingpostbyjobs-in-data.comcredit: DS Boost newsletter.
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Nikita .
IIT KGP, M.Tech, AIR 65 GATE 2022 AG | Data Science| ML | DL | NLP| SQL
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Hello Connection,Successfully completed all the tasks assigned to me during the Data Science internship at Cognifyz TechnologiesTask 1 :Data Exploration:a)Conducted in-depth exploration of the dataset.b)Identified trends and relationships between the target variable and various independent variables.Data Cleaning:a)Addressed symbols in the dataset to enhance model accuracy.b)Ensured the dataset was clean and ready for analysis.Descriptive Analysis:a)Analyzed numerical variables.b)Obtained insights on mean, median, and standard deviation, providing a comprehensive understanding of the data distribution.Exploratory Data Distribution:a)Investigated categorical variables.b)Identified the top cuisines and cities with the highest number of restaurants, contributing valuable insights for strategic decision-makingTask 2:Table Booking Preference:a)Determined that only 12.12% of restaurants prefer table booking.Online Delivery Preference:a)Found that only 25.67% of restaurants prefer online delivery.Task 3:Data Splitting:a)Split the dataset into X and y variables.b)Utilized the train_test_split method to create training and testing sets.Model Training:Trained the model using various algorithms:a)Linear Regressionb)Decision Tree Regressorc)Random Forest RegressorModel Evaluation:a)Identified the Random Forest Regressor as the algorithm with the lowest mean squared error, indicating superior performanceI would like to express my sincere gratitude to Cognifyz Technologies for providing me with this opportunity.
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Sanjay Krishna M
Intern at Luminar Technolab
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I am excited to present my machine learning project on classification using the Iris dataset as part of my application for the internship at CognoRise InfoTech. This project demonstrates my proficiency in data analysis, feature engineering, and model development.The Iris dataset is a classic dataset in the field of machine learning and is commonly used for classification tasks. It consists of 150 samples from three different species of Iris flowers (Iris setosa, Iris versicolor, and Iris virginica), with 50 samples per class. Each sample includes four feature measurements: sepal length, sepal width, petal length, and petal width.1)Data Exploration2)Data Preprocessing3)Feature Engineering4)Model Development5)Model EvaluationI achieved high classification accuracy, demonstrating the model's effectiveness in predicting the Iris species. Technologies and Tools:PythonScikit-LearnNumPy and Pandas for data manipulationMatplotlib and Seaborn for data visualizationCognoRise InfoTech#DecisionTreeClassifier
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EVOASTRA VENTURES PVT LTD
2,989 followers
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In light of the recent rise in deepfake scams, it's crucial to stay vigilant and protect your privacy. Deepfake technology can manipulate videos and audio to deceive people into believing false information.To safeguard yourself, follow these steps:1. Be cautious of unsolicited messages or calls asking for personal information.2. Verify the source of any information before sharing it.3. Use strong, unique passwords and enable two-factor authentication.4. Regularly update your security software and operating system.For more details, read our blog: https://lnkd.in/dBAcQA5YStay informed and Stay safe!#datascience #analytics #hiring #internship #dataanalytics #machinelearning #artificialintelligence #python #datascientist #bigdata #data #jobs #career #ai #coding #technology #jobsearch #dataanalyst #techjobs #sql #programming #jobhunt #datavisualization #staysafe
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Adrija Chakraborty
Student at School of Computer Engineering, KIIT DU
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Hello everyone! In my second task as an ML intern at InternPe , I was assigned with the task of developing a car price prediction model. Here's a breakdown of the process:For the data acquisition and preprocessing part, I imported all the necessary libraries (e.g., pandas, scikit-learn), then loaded car data from a CSV file and then performed data cleaning (i.e, removed rows with missing values and converted data types of relevant columns). For the model training and evaluation, I split the data into training and testing sets, implemented a Linear Regression model, used OneHotEncoder to handle categorical variables, created a pipeline to streamline data processing and model fitting and evaluated model performance using R-squared score. For the model optimization and deployment, due to limited data size, identified the model with the highest R-squared score using NumPy's argmax function and employed Pickle to serialize the trained pipeline (model) for future use. This process resulted in a functional car price prediction model ready to accept new data for price estimation.This project proved to be a valuable learning experience, requiring patience and providing significant exposure to several key data science techniques that will be instrumental in my future endeavors.
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