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All the FREE Stanford University Machine Learning Lectures 👇 Learn about Probability, NLP, LLMs, Transformers, and more ... 1. Probability for Computer Scientists - https://lnkd.in/e6sCyZGj 2. Machine Learning Full Course taught by Andrew Ng - https://lnkd.in/eWs74qyR 3. NLP with Deep Learning - https://lnkd.in/eazqcvmk 4. Machine Learning Explainability - https://lnkd.in/evimZ5Za 5. Reinforcement Learning - https://lnkd.in/eEf5PETJ 6. Deep Generative Models - https://lnkd.in/euZ2e3xU 7. Building Large Language Models (LLMs) - https://lnkd.in/eVUkaJuF 8. Machine Learning with Graphs - https://lnkd.in/eF_d3iwq 9. Transformers United - https://lnkd.in/eXdGBqQq 👉 Get free resources, curated articles, and expert tips on Data and AI: https://lnkd.in/e7EunZck #python #data #ai #machinelearning #programming

Fascinating Visualization on How ChatGPT Builds Sentences in Real-Time 👇 Data scientist Santiago Ortiz mapped ChatGPT's thinking by running the prompt "𝘐𝘯𝘵𝘦𝘭𝘭𝘪𝘨𝘦𝘯𝘤𝘦 𝘪𝘴" hundreds of times with a high temperature setting of 1.6 for varied responses. Then he used Principal Component Analysis (PCA) to compress 1536-dimensional word embeddings into a 3D visualization, tracking how the AI builds responses word by word. The project displays two linked visualizations:  ↳ A 3D cube showing bifurcating paths  ↳ And a tree diagram revealing word probabilities. Each point represents a text sequence, while hovering highlights specific completion paths, making AI decision-making patterns visible and understandable. Link to the project: https://moebio.com/mind --- 👉 Get free resources, curated articles, and expert tips on Data and AI: https://lnkd.in/e7EunZck #python #data #ai #machinelearning #programming

A Simple Guide to Bias vs Variance in Statistical Models: Why some models fail while others soar ↓ The eternal struggle in machine learning isn't just about accuracy—it's about understanding the delicate balance between bias and variance. This visualization captures four fundamental scenarios every data scientist encounters: 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼 𝗔: High variance, high bias • Scattered predictions consistently missing the target • Model is unstable and systematically wrong • Often indicates underfitting with a poor choice of model architecture 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼 𝗕: Low variance, high bias • Clustered predictions that miss the mark • Precisely wrong—consistent but inaccurate • Typical of oversimplified models that miss underlying patterns 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼 𝗖: High variance, low bias • Scattered predictions centered around truth • Correct on average but inconsistent individually • Common in complex models with insufficient regularization 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼 𝗗: Low variance, low bias • The ideal state—predictions cluster tightly around truth • Both accurate and consistent • Achieved through proper model selection, tuning, and sufficient data 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀: ↳ Use cross-validation to detect high variance (performance varies across folds) ↳ Address high bias by increasing model complexity or feature engineering ↳ Combat high variance with regularization, ensemble methods, or more training data ↳ Remember: as you reduce one, you often increase the other—hence the "tradeoff" This balance determines whether your model generalizes well to new data or simply memorizes noise. Finding this sweet spot is the art behind the science of machine learning. 👉 Get free resources, curated articles, and expert tips on Data and AI: https://lnkd.in/e7EunZck #python #data #ai #machinelearning

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New technical whitepaper from Google & Kaggle breaks down LLM fundamentals. Clear explanations from transformer basics to advanced inference techniques 👇 The "𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀 & 𝗧𝗲𝘅𝘁 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻" whitepaper by Google covers: • Basics of transformer architecture and how modern LLMs evolved • Breakdown of key models from GPT-1 to Gemini • Practical guide to fine-tuning techniques like SFT and RLHF • Smart ways to make LLMs run faster during inference • Real applications across coding, translation, summarization and more • Examples using Google Cloud Vertex AI and AI Studio Perfect for both beginners and experienced practitioners who want to implement LLMs in their work. --- 👉 Get free resources, curated articles, and expert tips on Data and AI: https://lnkd.in/e7EunZck #python #datascience #ai #machinelearning #programming

This is the best way to understand SQL 👇 Learn how database systems handle your queries, step-by-step. This will help you write more effective code and get the data you need faster. 1. FROM and JOINs: The first thing the engine does is combine the tables specified in the FROM clause. 2. ON: Join conditions in the ON clause are applied to the tables being joined. This determines which rows are included from each table in the join. 3. WHERE: Next filters are applied based on the conditions in the WHERE clause. Rows that don't meet the criteria are discarded. 4. GROUP BY: If a GROUP BY clause is used, the remaining rows are grouped based on the specified columns. 5. HAVING: The HAVING clause now filters grouped rows, similar to WHERE but for grouped records. 6. SELECT: With the filtered, joined and grouped table complete, the fields for the final output are selected. 7. ORDER BY: If an order is specified with ORDER BY, the rows are sorted accordingly. 8. LIMIT: Finally, the LIMIT clause restricts the number of rows returned. Understanding the execution order of all the SQL clauses is key for writing optimized queries 🦾. 👉 Get free resources, curated articles, and expert tips on Data and AI: https://lnkd.in/e7EunZck #sql #datascience #ai #machinelearning #programming

All the SQL Window Functions you need to know 👇 A comprehensive cheatsheet covering all functions you'll ever need. SQL window functions are essential tools for data scientists and ML engineers working with relational databases. Let's break down five key categories: 𝗥𝗮𝗻𝗸𝗶𝗻𝗴 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 • ROW_NUMBER(), RANK(), DENSE_RANK() assign positions to rows • NTILE(n) splits data into equal groups • PERCENT_RANK() and CUME_DIST() calculate relative positions 𝗡𝗮𝘃𝗶𝗴𝗮𝘁𝗶𝗼𝗻 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 • LAG() and LEAD() access previous or next row values • FIRST_VALUE(), LAST_VALUE(), and NTH_VALUE() retrieve specific rows 𝗔𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗲 𝗪𝗶𝗻𝗱𝗼𝘄 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 • SUM(), AVG(), COUNT(), MIN(), MAX() calculate across windows • _DISTINCT variants work with unique values only 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝗮𝗹 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 • MEDIAN(), MODE(), PERCENTILE functions for distribution analysis • STDDEV(), VARIANCE(), SKEWNESS() for statistical measures 𝗧𝗶𝗺𝗲-𝗕𝗮𝘀𝗲𝗱 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 • DATE_TRUNC(), TIME_BUCKET() group time data • Functions for handling intervals and time differences Save this guide for reference! I'd love to hear your thoughts 👉 What other cheatsheets would be useful to you? ___ 👉 Get free resources, curated articles, and expert tips on Data and AI: https://lnkd.in/e7EunZck #sql #data #ai #machinelearning

11 Data Structures Every Developer Must Know 👇 Data structures are the backbone of efficient programming. Here's a simple guide to help you understand them: • 𝗔𝗿𝗿𝗮𝘆:   - Like a row of boxes holding similar items  - You can grab any item quickly if you know its position  - Great for storing a fixed number of elements • 𝗤𝘂𝗲𝘂𝗲:  - Works like a line at a store  - New items join at the back, items leave from the front  - Useful for managing tasks in order • 𝗧𝗿𝗲𝗲:  - Branches out from a main "root" into smaller parts  - Helps organize info in a family tree-like structure  - Good for representing hierarchies • 𝗠𝗮𝘁𝗿𝗶𝘅:  - A grid of numbers or data  - Organized in rows and columns  - Handy for tables or image processing • 𝗚𝗿𝗮𝗽𝗵:  - Shows how different things connect  - Uses dots (nodes) and lines (edges) to map relationships  - Perfect for social networks or maps • 𝗟𝗶𝗻𝗸𝗲𝗱 𝗟𝗶𝘀𝘁:  - A chain of items, each pointing to the next  - Easy to add or remove items anywhere in the list  - Flexible for changing data • 𝗠𝗮𝘅 𝗛𝗲𝗮𝗽:  - A tree where the biggest value is always on top  - Keeps things partially sorted  - Efficient for finding the largest item quickly • 𝗦𝘁𝗮𝗰𝗸:  - Last in, first out - like a stack of plates  - Used in undo functions or for tracking program execution  - Helps manage temporary data in a structured way • 𝗧𝗿𝗶𝗲:  - A tree made for storing words or strings  - Shares common prefixes to save space  - Fast for spell-checking or autocomplete • 𝗛𝗮𝘀𝗵𝗠𝗮𝗽:  - Stores data using unique keys for quick retrieval  - Like a super-fast dictionary lookup  - Used in caches or for storing user data in apps • 𝗛𝗮𝘀𝗵𝗦𝗲𝘁:  - Keeps a collection of unique items  - Checks if something is in the set very quickly  - Useful for removing duplicates or tracking membership Data structures help organize and store data in a way that makes it easy to access and modify. Understanding these structures can make your coding more efficient and effective. 👉 Get free resources, curated articles, and expert tips on Data and AI: https://lnkd.in/e7EunZck #python #data #ai #artificialintelligence #programming #coding #tech

5 Loss Functions Every ML Practitioner Should Know 👇 Loss functions are key abilities that help machine learning models learn from data. Here's a simple guide created by my friend Prakhar Deroliya breaking down the most important ones: 𝗠𝗲𝗮𝗻 𝗦𝗾𝘂𝗮𝗿𝗲𝗱 𝗘𝗿𝗿𝗼𝗿 (𝗠𝗦𝗘) • Used in regression problems • Calculates average squared difference between predictions and actual values • Always non-negative and differentiable 𝗕𝗶𝗻𝗮𝗿𝘆 𝗖𝗿𝗼𝘀𝘀 𝗘𝗻𝘁𝗿𝗼𝗽𝘆 (𝗕𝗖𝗘) • Perfect for binary classification tasks • Based on probability theory • Works with outputs between 0 and 1 𝗙𝗼𝗰𝗮𝗹 𝗟𝗼𝘀𝘀 • Tackles class imbalance problems • Gives more weight to hard-to-classify examples • Uses parameters to adjust focus on difficult cases 𝗗𝗶𝗰𝗲 𝗟𝗼𝘀𝘀 • Specialized for image segmentation • Measures overlap between predicted and actual masks • Handles imbalanced pixel distributions naturally 𝗧𝗿𝗶𝗽𝗹𝗲𝘁 𝗟𝗼𝘀𝘀 • Used in metric learning and face verification • Works with anchor, positive, and negative samples • Creates distance between similar and different items Each loss function serves specific needs in machine learning, making them essential abilities for model training. --- 👉 Get free resources, curated articles, and expert tips on Data and AI: https://lnkd.in/e7EunZck #python #data #ai #machinelearning

90% of Data Cleaning and Preprocessing scenarios can be handled with these SQL commands 👇 Here’s a quick breakdown of must-know SQL commands: • String Operations: Use 𝚃𝚁𝙸𝙼 to remove extra spaces, 𝚄𝙿𝙿𝙴𝚁/𝙻𝙾𝚆𝙴𝚁 for case conversion, and 𝚁𝙴𝙿𝙻𝙰𝙲𝙴 to fix text issues. • NULL Handling: Replace missing values with 𝙲𝙾𝙰𝙻𝙴𝚂𝙲𝙴, or filter them out using 𝚆𝙷𝙴𝚁𝙴 𝚌𝚘𝚕 𝙸𝚂 𝙽𝙾𝚃 𝙽𝚄𝙻𝙻. • Numeric Fixes: Round numbers with 𝚁𝙾𝚄𝙽𝙳, or get absolute values using 𝙰𝙱𝚂(). • Date/Time: Extract parts of dates with 𝙴𝚇𝚃𝚁𝙰𝙲𝚃, or calculate differences using 𝙳𝙰𝚃𝙴𝙳𝙸𝙵𝙵. • Aggregation: Use 𝙶𝚁𝙾𝚄𝙿 𝙱𝚈 with 𝙲𝙾𝚄𝙽𝚃, 𝚂𝚄𝙼, or 𝙰𝚅𝙶 to summarize data. • Logic & Updates: Apply conditional logic with 𝙲𝙰𝚂𝙴 𝚆𝙷𝙴𝙽, or modify data using 𝚄𝙿𝙳𝙰𝚃𝙴. Data Cleaning and Preprocessing is a crucial step in any data project. These commands simplify 90% of these tasks. Bookmark this for your next project! 👉 Get free resources, curated articles, and expert tips on Data and AI: https://lnkd.in/e7EunZck #sql #datascience #ai #machinelearning #programming

You don’t need to know every Git command, but mastering the essentials will get you out of almost any situation. Here are 22 Git Commands Every Engineer Must Know 👇 Git helps teams track and manage code changes effectively. Here’s a quick guide to must-know Git commands: 𝗕𝗮𝘀𝗶𝗰 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀: • Start fresh with 'git init' or copy existing projects using 'git clone' • Track file status and history with 'git status' and 'git log' 𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗕𝗿𝗮𝗻𝗰𝗵𝗲𝘀: • Create and switch branches to work on features separately • Merge code safely when features are ready 𝗦𝘁𝗮𝗴𝗶𝗻𝗴 & 𝗖𝗼𝗺𝗺𝗶𝘁𝘁𝗶𝗻𝗴: • Save work with 'git add' and 'git commit' • Store temporary changes using 'git stash' • Undo mistakes with 'git reset' 𝗧𝗲𝗮𝗺 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻: • Share code through 'git push' and 'git pull' • Track remote connections with 'git remote' • Mark important points with 'git tag' 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗖𝗼𝗺𝗺𝗮𝗻𝗱𝘀: • Find bugs faster with 'git bisect' • See file changes with 'git diff' • Track code ownership using 'git blame' These commands form the backbone of efficient code management and team collaboration. --- 👉 Get free resources, curated articles, and expert tips on Data and AI: https://lnkd.in/eS-jpYem #git #ai #developer #machinelearning #programming

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The foundation of data science rests on understanding probability distributions. This cheatsheet provides a compact reference to the 10 most important distributions: • Uniform Distribution: Equal probability across all outcomes, used in random sampling and Monte Carlo simulations • Binomial Distribution: Models success counts in fixed trials, key for conversion rate prediction • Multinomial Distribution: Extends binomial to multiple classes, useful in text classification • Normal Distribution: The classic bell curve found in natural phenomena, central to hypothesis testing • Chi-Square Distribution: Used for variance-based testing and feature selection • t-Distribution: Similar to normal but with heavier tails, ideal for small samples • Multivariate Normal: Extends normal distribution to multiple dimensions • Gamma Distribution: Models waiting times, generalizes exponential distribution • Beta Distribution: Perfect for modeling probabilities between 0 and 1 • Dirichlet Distribution: Multivariate generalization of Beta, used for categorical distributions Each distribution includes its formula, key parameters, and practical applications in data science workflows. --- 👉 Get free resources, curated articles, and expert tips on Data and AI: https://lnkd.in/e7EunZck #datascience #ai #machinelearning #programming

Python's data types explained in 2 minutes (no fluff) ↓ 𝗦𝘁𝗿𝗶𝗻𝗴𝘀 - Immutable: Can't be changed once created. - Ordered: Can be indexed using square brackets (e.g., my_string[0]). - Can store any character. - Created using single quotes `'` or double quotes `"` (e.g. 'Hello' or "Hello"). 𝗟𝗶𝘀𝘁𝘀 - Mutable: Can be changed after creation. - Ordered: Can be indexed and sliced.  - Can store any data type. - Created using square brackets `[]` (e.g. ['Hello', 'World']). 𝗧𝘂𝗽𝗹𝗲𝘀 - Immutable: Cannot be changed after creation.  - Ordered: Can be indexed and sliced. - Can store any data type except other mutables like lists or sets. Can contain other tuples. - Created using parentheses `()` (e.g. ('Hello', 'World')). 𝗦𝗲𝘁𝘀 - Mutable: Can be changed after creation. - Unordered: Cannot be indexed but can iterate. - Does not allow duplicate members.  - Created using curly braces `{}` (e.g. {'Hello', 'World'}). 𝗗𝗶𝗰𝘁𝗶𝗼𝗻𝗮𝗿𝗶𝗲𝘀 - Mutable: Can be changed after creation. - As of Python 3.7, retains insertion order by default. - Allows duplicate values but not keys. - Created using curly braces `{}`  - Keys can be int, str, or tuple. Values can be any type. (e.g. {'Hello': 'World'}) 👉 Get free resources, curated articles, and expert tips on Data and AI: https://lnkd.in/e7EunZck #python #datascience #ai #machinelearning #programming

How Convolutional Neural Networks Work👇 A Convolutional Neural Network (CNN) works by using layers to process images: • Convolutional layers scan the input image using filters to detect features like edges, textures, and patterns.  • Next, Pooling layers reduce the spatial dimensions while preserving important information. • Then, fully connected layers take these extracted features and make final classifications by weighting connections between all neurons. ___ 👉 Get free resources, curated articles, and expert tips on Data and AI: https://lnkd.in/e7EunZck #python #data #ai #machinelearning

4 Ways Algorithms Learn From Data — Pick the Right One for Your Project 👇 𝗦𝘂𝗽𝗲𝗿𝘃𝗶𝘀𝗲𝗱 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 • Works with labeled data • Target variables: continuous (regression) or categorical (classification) • Applications: housing price prediction, medical imaging 𝗨𝗻𝘀𝘂𝗽𝗲𝗿𝘃𝗶𝘀𝗲𝗱 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 • Works with unlabeled data • No target variables available • Techniques: clustering, association • Applications: customer segmentation, market basket analysis 𝗦𝗲𝗺𝗶-𝗦𝘂𝗽𝗲𝗿𝘃𝗶𝘀𝗲𝗱 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 • Combines labeled and unlabeled data • Handles categorical variables and clustering • Applications: text classification 𝗥𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 • Based on reward systems • Can work with or without specific target variables • Applications: GPS lane detection, marketing optimization, autonomous vehicles 👉 Get free resources, curated articles, and expert tips on Data and AI: https://lnkd.in/e7EunZck #python #data #ai #artificialintelligence #programming #coding #tech

You don’t need to know every Linux command, but mastering the basics will get you out of almost any situation. I've created this cheatsheet with essential Linux commands for daily use 👇 Here's what it covers: 𝗙𝗶𝗹𝗲 𝗡𝗮𝘃𝗶𝗴𝗮𝘁𝗶𝗼𝗻: • 'ls' and 'cd' help you move around folders • 'pwd' shows your current location • 'mkdir' and 'rmdir' create and remove directories 𝗙𝗶𝗹𝗲 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀: • 'cp' copies files from one place to another • 'mv' moves or renames your files • 'rm' removes files when you need to clean up • 'touch' creates empty files quickly 𝗧𝗲𝘅𝘁 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴: • 'cat' lets you view and combine files • 'head' and 'tail' show file beginnings and endings • 'grep' finds specific text in files 𝗦𝘆𝘀𝘁𝗲𝗺 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: • 'chmod' and 'chown' control file access • 'ps' and 'top' monitor running programs • 'sudo' gives you admin rights when needed --- 👉 Get free resources, curated articles, and expert tips on Data and AI: https://lnkd.in/e7EunZck #python #datascience #ai #machinelearning #programming

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