Abhinav kumar

𝐒𝐐𝐋 𝐑𝐞𝐟𝐫𝐞𝐬𝐡𝐞𝐫 𝐚𝐧𝐝 𝐃𝐞𝐞𝐩 𝐃𝐢𝐯𝐞 (𝐏𝐚𝐫𝐭-𝟖)🍃 𝐒𝐐𝐋 𝐉𝐨𝐢𝐧𝐬 : A key to Relational Data Analysis Joins allows to combine data from multiple tables in a relational database, unlocking deeper insights from the datasets. 𝐓𝐡𝐞 𝐝𝐨𝐜𝐮𝐦𝐞𝐧𝐭 𝐝𝐢𝐯𝐞𝐬 𝐢𝐧𝐭𝐨 𝐭𝐡𝐞 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭 𝐭𝐲𝐩𝐞𝐬 𝐨𝐟 𝐣𝐨𝐢𝐧𝐬 𝐚𝐥𝐨𝐧𝐠 𝐰𝐢𝐭𝐡 𝐞𝐱𝐚𝐦𝐩𝐥𝐞𝐬. 𝐓𝐡𝐞𝐬𝐞 𝐚𝐫𝐞 𝐚𝐬 𝐟𝐨𝐥𝐥𝐨𝐰𝐬: 1. Inner Join 2. Left Join 3. Right Join 4. Full Outer Join 5. Cross Join 6. Self Join 𝐒𝐨𝐦𝐞 𝐨𝐟 𝐭𝐡𝐞 𝐢𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐩𝐨𝐢𝐧𝐭𝐬 𝐭𝐡𝐚𝐭 𝐬𝐡𝐨𝐮𝐥𝐝 𝐛𝐞 𝐫𝐞𝐦𝐞𝐦𝐛𝐞𝐫𝐞𝐝 𝐰𝐡𝐢𝐥𝐞 𝐰𝐨𝐫𝐤𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐉𝐨𝐢𝐧𝐬: ·  Understanding the core functionalities of every join type. ·  Defining clear join conditions to specify how rows from tables should be matched based on compatible columns. ·  Watching out for ambiguity in complex join queries. ·  Complex joins can be slow. Optimize if needed. ·  Left and full outer joins introduce NULLs. Be prepared to address them. ·  Choosing the right join type, inner joins for matches, left/right for including all records from one table, full outer for all data regardless of matches. ·  Mostly left and inner joins are being used. ·  Considering subqueries or CTEs for complex joins to improve readability or performance. Attaching a SQL Joins Cheatsheet for reference too. Grateful to Zain Ul Hassan for the invaluable mentorship he has been providing since the starting. Also appreciating Rishabh Mishra for providing concise and informative content for learning.

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Delving into Details:  𝐅𝐚𝐬𝐭𝐀𝐏𝐈 𝐯𝐬. 𝐅𝐥𝐚𝐬𝐤 for 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠  Applications. Which framework aligns best with your ML projects? 🤔 Let's explore the variances! 💡 Also, follow us on: Facebook: http://bit.ly/41wCgrz Instagram: https://bit.ly/3NL6RMz Twitter: https://bit.ly/3tkjyqK Threads: https://bit.ly/3NNUgYS #FastAPI #Flask #MachineLearning #Python #FrameworkComparison #Regbits

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#Excercise5 #MachineLearning #ClassificationProblem #LogisticRegression Hello Data Masters ⭐ ⭐ ⭐ #Problemstatement: We have a classification problem of multiple variable , in which we have to predict the name of the flower by following properties? Sepal Length Sepal Width Petal Length Petal Width Flower Name Given: (1)Sertosa (2)Versiclor (3)Verginica #Solution: #Backend: We import data of iris flowers from the sklearn.datasets library and then split the data into training and testing part by use of sklearn.model_selection function called train_test_split .And finally create a model from the sklearn.linear_model function called logistic regression , and predict the flower name from that model . #Frontend: Frontend is purely design by use of tkinter library of python there is a button for generating the name of the flower and a button for the clear purpose of all the entries and text areas. Tools Utilized: train_test_split from sklearn.model_selection LogisticRegression from sklearn.linear_model iris_dataset from sklearn.datasets tkinter library Thank you Codebasics and Dhaval Patel sir for providing me the platform to learn . Codebasics Microsoft Google Huawei IBM Coursera Dhaval Patel Hemanand Vadivel Injae Park Shashank Singh 🇮🇳 Saim Khan Afiniti Sixlogics Rolustech WebX Programmers Force Nexus info ZAPTA Technologies (Pvt.) Limited

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What is Thresholding? Thresholding is the process of converting continuous prediction scores (probabilities) into discrete class labels. Typically used in binary classification to decide the class based on a probability score. Default Threshold: The default threshold is usually set at 0.5. If the predicted probability is greater than or equal to 0.5, the instance is classified as positive; otherwise, it is classified as negative. Adjusting the Threshold: Adjusting the threshold can help balance precision and recall, depending on the application's needs. A higher threshold can increase precision but may decrease recall. A lower threshold can increase recall but may decrease precision. Finding the Optimal Threshold: Use techniques like ROC (Receiver Operating Characteristic) curves and AUC (Area Under the Curve) to evaluate different thresholds. Precision-Recall curves can also help determine the threshold that balances precision and recall. Applications: Medical diagnosis: Lower thresholds might be preferred to ensure fewer false negatives. Spam detection: Higher thresholds might be preferred to ensure fewer false positives. Credits to Analytics Vidhya for explaining thresholding in machine learning. Apply these insights to fine-tune your classification models and make more informed decisions! 🚀 Follow Muhammad Mursaleen for more such content. 🔍 #MachineLearning #Thresholding #Classification #DataScience #ModelEvaluation #Python #Programming #Tech #Learning #CareerDevelopment #LinkedInLearning #TechCommunity #AI #Analytics #DataDriven #CareerGrowth #LinkedIn #MursaleenUpdates

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For imbalanced classification problem two sampling techniques are quite useful: 1) Sampling Ratio detection using genetic algorithm. 2) Distribution optimally balanced stratified cross validation/ Stratified sampling I have practically used both while solving imbalanced classification problem. #machinelearning #imbalancedclassification.

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Decode the enchanting tales of machine learning from industries 🧠📚🏭 🌟Netflix's Calibrated Recommendations youtu.be/DOWXNrBpO4w  🌟Netflix's Unified Recommendation ML Model: youtu.be/OKmv9sUrvk8 🌟Evolution of Recommendation Systems | Instagram Pinterest Twitter youtu.be/lgoyJn7MsH8 🌟Multi-Armed Bandit Startegies: youtu.be/2A5f3GrX0dA  🌟Overcoming Biases for a Better Recommender System | How Tech-Titans Combat Recommender System Biases youtu.be/oGb_mIdO0tA 🌟Building Scalable Query-Item Two-Tower Model based Retrieval System youtu.be/o-pZk5R0TZg  🌟Meituan's Game-Changing Two-Tower Retrieval Recsys Model | KDD'21 youtu.be/UhpbTSbi3lI  🌟 Innovative Linkedin's Deep-leaning based CTR Modeling: The Deep, Wide, and Shallow Towers Explained youtu.be/7l0HLYVFEuU 🌟 Lessons from Instagram’s Cutting-Edge Recommendation System youtu.be/Myna6rnmCG8 🌟 How Twitter recommends Tweets: Inside Twitter's Recommendation Algorithm 🐦 youtu.be/IhGq9jgcxFM 🌟 How eBay Built a Next-Generation Language Model to Revolutionise Online Shopping 🛒 youtu.be/h51nbWr7feo 🌟 Leveraging Pre-trained Bert to generate Entity Embeddings across domain 📚 youtu.be/v-0J7o-nDBE 🌟 Uplift Modeling to Detect Causal Effect | Uplift Modeling & Causal Inference to Improve Instagram ✨youtu.be/rKzG0Ct_ReA 🌟 Advertiser Recommendation Systems at Pinterest | Machine Learning & Algorithm-driven Ads Platform 📌youtu.be/KEiBJ0fal3Q 🌟 Dynamic Pricing Strategy to effectively fill slots | E-commerce | Travel | Hospitality Domains 💰youtu.be/a_CXpnsvPa0 🌟 Train your own Product Embeddings | Embeddings across Domains | Training on 1.02 Billion rows on GPU 💡youtu.be/DN4S96oHRhE 🌟 Model-Based Account Recommendations @ Twitter | Recommendation System | Retrieval & Ranking | RECSYS 📊 youtu.be/Xqo8fwgjxW4 🌟 Approximate Nearest Neighbour and a popular Library Annoy for k-Nearest Neighbour Search & Product Quantizer 🔍youtu.be/DSQOrBTqmYA & youtu.be/50PNumB7s3U 🌟 PID Controller to add a layer of diversity to the recommender systems 🎛️ youtu.be/laTxgnzjfR0 #datatrek #datascience #machinelearning #statistics #deeplearning #ai

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Numeric data types handle various numbers like whole numbers, decimals, and those with large or small exponents, crucial for precise math and statistics in data analysis. 🧮 Date and time data types manage temporal info, including dates, times, and timestamps, vital for time-based data analysis. 📅⏰ Character data types maintain language integrity through character strings and variable-length strings, critical for text-based data analysis. 📝 Miscellaneous data types store Boolean values, spatial data, and JSON format, for logical operations, geographical info, and data interchange, respectively. 💡🗺️ Unicode character data types store data from any language, facilitating globalized data storage and analysis. 🌐🔤 #Sql #DataScience

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FastX Embedding FastText embeddings, or similar word embeddings generated using efficient methods, have numerous use cases across various natural language processing (NLP) tasks. Here are some common applications: 1.    Text Classification: FastText embeddings can be used as input features for text classification tasks, such as sentiment analysis, topic classification, spam detection, and intent recognition. By representing text as dense vector representations, classifiers can effectively learn patterns and make accurate predictions. 2.    Named Entity Recognition (NER): NER involves identifying and classifying named entities (e.g., persons, organizations, locations) in text. FastText embeddings can help capture semantic similarities between words, aiding in recognizing entities even if they haven't been seen in the training data. 3.    Machine Translation: In machine translation systems, FastText embeddings can be used to represent words or subword units in the source and target languages. These embeddings help capture semantic similarities between words in different languages, improving translation quality. 4.    Question Answering: FastText embeddings can assist in question answering systems by encoding questions and passages into dense vector representations. By measuring the similarity between question and passage embeddings, the system can identify relevant passages for answering questions. 5.    Text Similarity and Clustering: FastText embeddings enable measuring semantic similarity between pieces of text, such as documents, sentences, or paragraphs. They can also be used for text clustering tasks, grouping similar documents together based on their embeddings. 6.    Text Summarization: FastText embeddings can be incorporated into text summarization models to represent input text and generate concise summaries. By leveraging semantic similarities captured in the embeddings, summarization systems can produce informative and coherent summaries. 7.    Named Entity Disambiguation: In applications where named entities may have multiple meanings (e.g., "Washington" referring to a person or a location), FastText embeddings can help disambiguate by capturing contextual information and semantic relationships between words. 8.    Spell Checking and Correction: FastText embeddings can aid in spell checking and correction by suggesting corrections for misspelled words based on the semantic similarity between the misspelled word and candidate corrections. These are just a few examples of the many use cases for FastText embeddings and similar word embedding techniques in NLP. Their versatility and effectiveness in capturing semantic information make them invaluable for a wide range of applications.

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𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗮 𝗙𝗹𝗼𝗼𝗱 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻 𝗗𝗮𝘁𝗮𝘀𝗲𝘁 -Example for Regression Model With CATBoost Using Optuna Hyperparamter tuning. 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗟𝗶𝗻𝗸: https://lnkd.in/d_wd34Wu 𝗚𝗲𝘁 𝗬𝗼𝘂𝗿 𝗠𝗼𝗱𝗲𝗹 𝗡𝗼𝘄! https://lnkd.in/dNbVS-iF

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Discover the gems of industry where data science has brilliantly solved complex business challenges. 🌟Netflix's Calibrated Recommendations youtu.be/DOWXNrBpO4w  🌟Netflix's Unified Recommendation ML Model: youtu.be/OKmv9sUrvk8 🌟Evolution of Recommendation Systems | Instagram Pinterest Twitter youtu.be/lgoyJn7MsH8 🌟Multi-Armed Bandit Startegies: youtu.be/2A5f3GrX0dA  🌟Overcoming Biases for a Better Recommender System | How Tech-Titans Combat Recommender System Biases youtu.be/oGb_mIdO0tA 🌟Building Scalable Query-Item Two-Tower Model based Retrieval System youtu.be/o-pZk5R0TZg  🌟Meituan's Game-Changing Two-Tower Retrieval Recsys Model | KDD'21 youtu.be/UhpbTSbi3lI  🌟 Innovative Linkedin's Deep-leaning based CTR Modeling: The Deep, Wide, and Shallow Towers Explained youtu.be/7l0HLYVFEuU 🌟 Lessons from Instagram’s Cutting-Edge Recommendation System youtu.be/Myna6rnmCG8 🌟 How Twitter recommends Tweets: Inside Twitter's Recommendation Algorithm 🐦 youtu.be/IhGq9jgcxFM 🌟 How eBay Built a Next-Generation Language Model to Revolutionise Online Shopping 🛒 youtu.be/h51nbWr7feo 🌟 Leveraging Pre-trained Bert to generate Entity Embeddings across domain 📚 youtu.be/v-0J7o-nDBE 🌟 Uplift Modeling to Detect Causal Effect | Uplift Modeling & Causal Inference to Improve Instagram ✨youtu.be/rKzG0Ct_ReA 🌟 Advertiser Recommendation Systems at Pinterest | Machine Learning & Algorithm-driven Ads Platform 📌youtu.be/KEiBJ0fal3Q 🌟 Dynamic Pricing Strategy to effectively fill slots | E-commerce | Travel | Hospitality Domains 💰youtu.be/a_CXpnsvPa0 🌟 Train your own Product Embeddings | Embeddings across Domains | Training on 1.02 Billion rows on GPU 💡youtu.be/DN4S96oHRhE 🌟 Model-Based Account Recommendations @ Twitter | Recommendation System | Retrieval & Ranking | RECSYS 📊 youtu.be/Xqo8fwgjxW4 🌟 Approximate Nearest Neighbour and a popular Library Annoy for k-Nearest Neighbour Search & Product Quantizer 🔍youtu.be/DSQOrBTqmYA & youtu.be/50PNumB7s3U 🌟 PID Controller to add a layer of diversity to the recommender systems 🎛️ youtu.be/laTxgnzjfR0 #datatrek #datascience #machinelearning #statistics #deeplearning #ai

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This video tutorial elucidates the process of estimating a Quantile Regression Model in EViews. https://lnkd.in/d66Yz2Nr LIKE, SHARE & SUBSCRIBE @followers #quantileregression #Eviews #econometrics #statistics #dataanalysis #econometricsmodeling #quantileestimation #conditionalquantiles #robustregression #heteroscedasticity #nonnormaldistributions #medianregression #quantileestimation

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Common Issues with Retrieval-Augmented Generation (RAG) Retrieval-augmented generation (RAG) models face several challenges that can impact their effectiveness. Here are some common issues associated with RAG: 1. Retrieval Challenges Relevance of Retrieved Documents: The retriever may fetch documents that are only tangentially related to the query, leading to less accurate or useful responses. Scalability: Handling large knowledge bases or document corpora efficiently requires significant computational resources and sophisticated indexing techniques. Latency: Real-time retrieval from large datasets can introduce delays, affecting the speed of the RAG system, especially in interactive applications. Context Drift: Retrieved documents might not precisely match the context or the specifics of the query, leading to irrelevant information being passed to the generator. 2. Generation Challenges Coherence and Consistency: The generator may produce responses that are inconsistent or incoherent, particularly if the retrieved documents contain conflicting or incomplete information. Over-Reliance on Retrieval: The generation can become too dependent on the retrieved content, leading to verbatim copying or failure to produce novel, contextually appropriate responses. Incorporation of Misinformation: If retrieved documents have incorrect or outdated information, the generator might produce inaccurate responses. 3. Integration Issues Alignment between Retriever and Generator: Ensuring the retriever’s output is optimally formatted and relevant for the generator is complex, requiring careful fine-tuning and alignment. Model Complexity: The combined architecture of retrievers and generators increases the overall system's complexity, making it harder to manage and optimize. Data Handling: Managing data flow between the retriever and generator, particularly with large datasets, can lead to inefficiencies and bugs. 4. Evaluation and Tuning Evaluation Metrics: Traditional NLP metrics often fall short for RAG systems, requiring new evaluation methods assessing retrieval and generation quality. Fine-Tuning: Effective fine-tuning of RAG models requires balancing the retriever’s ability to fetch relevant documents with the generator’s coherence, which is both resource-intensive and technically challenging. 5. Domain Adaptation Generalization to New Domains: Adapting RAG models to new or specialized domains may require extensive retraining and reconfiguration of both retrievers and generators. Data Sparsity: In domains with limited data, the retriever might struggle to find relevant documents, and the generator may produce inaccurate responses. 6. Security and Bias Bias in Retrieved Content: The retriever might fetch biased or controversial content, which can cause bias in generator responses. Security Risks: Exposure to harmful or malicious content in the retrieved documents can lead to inappropriate or harmful outputs from the generator.

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𝗛𝗼𝘄 𝘁𝗼 𝗛𝗮𝗻𝗱𝗹𝗲 𝗡𝘂𝗹𝗹 𝗩𝗮𝗹𝘂𝗲𝘀?  👨‍💻 One of the most crucial part of the data pre-processing stage is to handle null values. Therefore, it is very important to understand the science behind fixing it effectively as we will have to adopt different imputation or replacement techniques to fix null values in different scenarios. 𝗠𝗮𝗷𝗼𝗿 𝗿𝗲𝗮𝘀𝗼𝗻𝘀 𝗯𝗲𝗵𝗶𝗻𝗱 𝗶𝘁𝘀 𝗶𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝗰𝗲 𝗮𝗿𝗲: 1) To get better accuracy of Machine learning models 2) To reduce biasness or skewness in datapoints to avoid biased conclusions 3) To avoid unnecessary computational errors in pre-processing To fix null values, first thing you need to keep in mind that is u cant always use mean, median and mode replacement technique. There are many approaches to fix null values, and all require suitable scenario to give best outcome. Therefore, what matters is that u understand which approach is best for the scenario u are getting in ur project. Along with yours, domain expert's input will play key role in deciding final strategy. So to make urself capable enough to deal with null values u need to make sure u have an idea about the: 1) Categories of Missing data e.g MCAR, MNAR, and MAR. 2) Which technique is suitable for each category 𝗧𝘆𝗽𝗲/𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀: For MCAR, we prefer Random Sample Imputation or Mean/Median/Mode replacement For MNAR, we prefer Capturing NaN values with new feature For MAR, we let domain expert to decide or we can use ML model to predict missing value by using observed values of that feature. And don't just stick to Mean/Mode replacement u can explore other techniques like Frequent category imputation, End of distribution imputation, and Arbitrary imputation. This can be explained further by checking which strategy is suitable for numeric and categorical features respectively. But tbh once u have the strong understanding about the types of missing data and about these strategies u will be able to decide about best way to handle missing values in different scenarios.  Things are available online but not in an order we are supposed to learn them, that's why decided to share it with everyone so that you guys don't have to spend extra time in learning things separately and then connecting dots with each other! #DataScience #DataPreprocessing #DataAnalytics #Datasciencelifecycle #Datacleaning

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