Amruta Patole’s Post

🌟 **Exploring the World of Data Encoding!** 🌟 Hello LinkedIn family, Today, I had the opportunity to dive deep into the fascinating world of data encoding techniques, a crucial aspect of data preprocessing in Machine Learning. Understanding and implementing these techniques correctly can significantly impact the performance of predictive models. Here’s a brief overview of what I learned: 1. One Hot Encoding: Transforms categorical variables into a form that could be provided to ML algorithms to do a better job in prediction. 2. Label Encoding: Each unique category value is assigned a numerical value from 0 to N-1, where N is the number of categories for the feature. 3. Ordinal Encoding: Unlike one hot encoding, the categories are ordered in such a way that there is a relationship between them. 4. Target Guided Encoding: Categories are replaced with numbers derived from the mean of the target variable, providing a more nuanced encoding based on the dataset’s specific characteristics. These techniques each have their unique applications and nuances that can help improve model performance across various scenarios. Thanks to mentor Ajay Kumar Gupta sudhanshu kumar Vishwa Mohan Priya Bhatia Krish Naik Ekta Negi PW Skills iNeuron.ai #DataScience #MachineLearning #DataEncoding #DataPreprocessing #ArtificialIntelligence #ProfessionalGrowth

𝗧𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗶𝘀 𝗡𝗼𝘄: 𝗗𝗶𝘃𝗲 𝗶𝗻𝘁𝗼 𝗔𝗜 & 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 The world runs on data, and the ability to analyze it effectively is the key to success. This AI and Data Analytics training equips you with the skills to unlock the hidden potential within your data, transforming you from a passive observer to an active decision-maker. Whether you're a business professional, aspiring data scientist, or simply curious about the future of technology, this training will provide you with the knowledge and skills to thrive in the data-driven world. 𝗝𝗼𝗶𝗻 𝘂𝘀 𝗮𝗻𝗱 𝘂𝗻𝗹𝗼𝗰𝗸 𝘁𝗵𝗲 𝗽𝗼𝘄𝗲𝗿 𝗼𝗳 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮! For registration and more info please visit the following link: https://bit.ly/3RFUKT5 𝗙𝗼𝗿 𝗺𝗼𝗿𝗲 𝗶𝗻𝗳𝗼 𝗰𝗮𝗹𝗹 𝘂𝘀 𝗼𝗻: 𝟮𝟭𝟯 𝟮𝟲𝟮𝟲 𝗼𝗿 𝗪𝗵𝗮𝘁𝘀𝗔𝗽𝗽: 𝟱𝟴𝟰𝟴 𝟲𝟮𝟴𝟰 𝗧𝗵𝗲 𝗰𝗼𝘂𝗿𝘀𝗲 𝗶𝘀 𝗠𝗤𝗔 𝗮𝗽𝗽𝗿𝗼𝘃𝗲𝗱 𝗮𝗻𝗱 𝗛𝗥𝗗𝗖 𝗿𝗲𝗳𝘂𝗻𝗱𝗮𝗯𝗹𝗲 (𝗱𝗲𝗽𝗲𝗻𝗱𝗶𝗻𝗴 𝗼𝗻 𝘆𝗼𝘂𝗿 𝗿𝗲𝗳𝘂𝗻𝗱 𝘀𝘁𝗮𝘁𝘂𝘀 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗹𝗮𝘁𝘁𝗲𝗿) #ArtificialIntelligence #GenerativeAI #DataAnalytics #DataVisualizations #Dashboard #Data #Ethics #DataGovernance

I am thrilled to announce the release of the first edition of "Confessions of a Data & AI Pre-Sales Engineer." Drawing from over a decade of experience in the AI and Big Data industry, I have crafted this book to share invaluable insights and lessons I wish I had known at the start of my journey. Whether you're a seasoned professional or just beginning, this book is packed with practical advice, real-world examples, and behind-the-scenes stories that will inspire and empower you in your career. Don't miss out on this essential guide to navigating the dynamic world of data and AI. #NewBookRelease #DataScience #AI #BigData #PreSalesEngineer #TechInsights #CareerAdvice #AIIndustry #DataEngineer #TechJourney #ProfessionalDevelopment #BehindTheScenes #Inspiration #Empowerment #TechCommunity

🌟 Day 5: Data Preparation and Cleaning 🌟 Data is the backbone of any data science project, but raw data often comes with its challenges. Today, we dive deep into the essential processes of data preparation and cleaning. Here are the key takeaways: 🔍 Importance of Data Cleaning 🕯 Removing Duplicates and Errors: Ensuring that our dataset is free from duplicates and erroneous data is crucial for accurate analysis. Clean data leads to more reliable and valid results. 🕯Handling Missing Values: Missing data can skew our analysis. We explored various techniques to handle missing values, from imputation to removing incomplete records. 🔄 Data Transformation Techniques 🕯Normalization & Standardization: These techniques are vital for bringing all our data into a common scale, which is especially important for algorithms sensitive to the scale of input features. 🕯Encoding Categorical Variables: Converting categorical data into numerical form is essential for many machine learning models. We covered methods like one-hot encoding and label encoding to prepare our categorical variables for analysis. By mastering these steps, we're setting a solid foundation for building robust and accurate models. Data cleaning might not be the most glamorous part of data science, but it's absolutely necessary for success. Stay tuned for more insights as we continue our journey through the exciting world of data science and AI! 🚀 Share your thoughts or ask any questions in the comments below! Let's keep learning and growing together. 🌱 #Koblousani #DataScience #AI #DataCleaning #DataPreparation #MachineLearning #DataTransformation #FreeTraining #LearningJourney #TechCommunity #Universityofgloucestershire #20daylinkedinchallengewithhaoma

𝐇𝐨𝐰 𝐝𝐨𝐞𝐬 𝐡𝐚𝐧𝐝𝐥𝐢𝐧𝐠 𝐢𝐦𝐛𝐚𝐥𝐚𝐧𝐜𝐞𝐝 𝐝𝐚𝐭𝐚 𝐢𝐦𝐩𝐚𝐜𝐭 𝐭𝐡𝐞 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐨𝐟 𝐌𝐋 𝐦𝐨𝐝𝐞𝐥𝐬? 📈 Dealing with imbalanced data is essential for developing machine learning models that provide accurate insights and effectively serve diverse scenarios. ⏩ Here are some effective techniques: 1. Resampling Techniques: Balance your data by adding more instances of the minority class or reducing the majority class. 2. Data Augmentation: Boost the minority class by creating new data points from existing ones. 3. SMOTE (Synthetic Minority Over-sampling Technique): Generate synthetic examples to even out your dataset. 4. Ensemble Techniques: Combine several models to improve predictions, especially with imbalanced data. 5. One Class Classification: Focus on learning patterns from just the minority class, useful when examples are rare. 6. Cost-Sensitive Learning: Adjust misclassification costs to make the model prioritize the minority class. 7. Evaluation Metrics: Use precision, recall, and F1-score for a clearer performance picture than accuracy alone. Effectively addressing imbalanced data with these techniques is not just about improving model accuracy, it's about ensuring fairness and equity in decision-making processes. By prioritizing inclusivity and fairness, we create data-driven solutions that benefit society as a whole. [ Explore more in the post ] <><><><><> If you found this helpful don't forget to save this for later and comment your thoughts. 💬 Repost to help others ♻ Follow Piku Maity for more invaluable insights on Data & AI Happy Learning!! #dataprocessing #datacleaning #datascience #machinelearning

💡 𝐁𝐘𝐓𝐄 24 𝐎𝐅 𝐌𝐀𝐂𝐇𝐈𝐍𝐄 𝐋𝐄𝐀𝐑𝐍𝐈𝐍𝐆 𝐄𝐒𝐒𝐄𝐍𝐓𝐈𝐀𝐋𝐒 𝐔𝐍𝐏𝐀𝐂𝐊𝐄𝐃 📦 Have you ever wondered how machines handle categorical data like "Red," "Yellow," or "Green"? 🤔 One-hot encoding simplifies this challenge! 🎯 📄Check out this crisp, beginner-friendly PDF to grasp the concept and see it in action. 🏷️Tagging some brilliant minds in the Data Science & AI community! Hargurjeet Singh Ganger ; Daksh Bhatnagar, Data Analyst ; Korrapati Jaswanth ; Shivam Shrivastava ; Mohamed Kayser ; Akshay Kumar ; Sarthak sharma ; Khushi Dubey 𝗥𝗲𝗽𝗼𝘀𝘁 with comments to educate and grow your own network! Follow Tarun K T for more on breaking down ML concepts! #MachineLearning #DataScience #AI #ML #MLEssentials #MLForBeginners #TechEducation #OneHotEncoding

Well-covered knowledge page for Data & Data Science What's the relationship between data and data science? 𝐃𝐚𝐭𝐚 can be anything from numbers and text to images and audio recordings. It's often messy, incomplete, and unorganized. 𝐃𝐚𝐭𝐚 𝐬𝐜𝐢𝐞𝐧𝐜𝐞 uses various techniques like statistics, machine learning, and programming to clean, organize, analyze, and interpret data. It helps uncover patterns, trends, and relationships within the data that wouldn't be readily apparent from simply looking at it raw. 𝐓𝐡𝐞𝐬𝐞 𝐢𝐧𝐬𝐢𝐠𝐡𝐭𝐬 can then be used to inform decision-making, solve problems, predict future outcomes, and create new products or services. Image credit to 365 Data Science on Undemy ✔️Follow us for more data science and AI strategy insights Premier Strategy Consulting We focus on delivering value-added and scalable AI solutions to a variety of businesses. Follow to unlock your business potential. 💫 #datascience #business #technology #machinelearning

A few weeks ago, I came across an advertisement for the Elite Global AI Cohort 2.0 on LinkedIn. I decided to choose the Data Analytics and Business Analytics course, specifically focusing on AI in Data Analysis. The course covered a wide range of topics, like data summary, data visualization, hypothesis generation, data cleansing, and statistical concepts such as descriptive statistics, probability, and inferential statistics. We explored into various data visualization techniques like bar charts, pie charts, scatter plots, and line graphs. We also explored data modelling and prediction, which included model selection, training, evaluation, and deployment. Additionally, the course emphasized the importance of ethics and privacy in data analysis, covering aspects like fairness, transparency, privacy, and accountability. We learned how to effectively communicate insights through data storytelling, understanding our audience, and making actionable recommendations. We used tools like Data Squirrel and Gamma AI, which enhanced our learning experience. The training was truly remarkable. I'm sincerely grateful to the program's organizing team #EliteGlobalAI and everyone involved in making the experience a success. Thank you for providing us with such valuable learning in data analysis, I completed the course joyfully and am thankful to Elite Global AI. #DataAnalysis #DataAnalyst #EliteGlobalAI #EliteGlobalAICohort2_0 #Artificial_InteligenceCohort2_0

AI Challenges: Building a Strong Data Science Engineering Team for AI Success. Build a Strong Data Science Engineering Team 👥 Medium-sized businesses should focus on building a strong data science engineering team to support AI initiatives. A skilled team is essential for developing, deploying, and maintaining AI solutions. Stay tuned as we explore the transformative power of AI and its impact on the future. Follow Swadeep Singh for expert insights and strategies on navigating these challenges. #Aidwise #DataScience #AIEngineering

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