Emmanuel Faber

Start at 0$ Free of Cost 💸 𝗠𝗮𝘀𝘁𝗲𝗿𝗶𝗻𝗴 𝗦𝗤𝗟: 𝗙𝗿𝗼𝗺 𝗕𝗮𝘀𝗶𝗰𝘀 𝘁𝗼 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 🚀 Are you ready to dive deep into the world of SQL? Whether you're just starting your journey or looking to level up your skills, this comprehensive guide will take you from SQL novice to expert in no time! 💪 𝟭) 𝗦𝗤𝗟 𝗕𝗮𝘀𝗶𝗰𝘀: 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗬𝗼𝘂𝗿 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 Understanding the fundamentals of SQL, or Structured Query Language, is crucial. 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵: SELECT Statement: The bread and butter of SQL. Learn how to retrieve data from databases with precision. FILTERING Data: WHERE clause allows you to filter data based on specific conditions. JOIN Operations: Master INNER, LEFT, RIGHT, and FULL JOINs to combine data from multiple tables. AGGREGATE Functions: SUM, AVG, COUNT, MIN, MAX—these functions are essential for data analysis. 𝟮) 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗦𝗤𝗟 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀: 𝗨𝗻𝗹𝗲𝗮𝘀𝗵 𝗬𝗼𝘂𝗿 𝗣𝗼𝘄𝗲𝗿 Once you've grasped the basics, it's time to unlock the full potential of SQL. 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗲𝗿𝗲 𝘁𝗵𝗶𝗻𝗴𝘀 𝗴𝗲𝘁 𝗲𝘅𝗰𝗶𝘁𝗶𝗻𝗴: * Subqueries: Nest queries within queries for advanced filtering and data manipulation. * Common Table Expressions (CTEs): Improve readability and performance with temporary result sets. * Window Functions: Analyze and manipulate data in partitions with functions like ROW_NUMBER and RANK. * Transactions and ACID Properties: Ensure data integrity by understanding Atomicity, Consistency, Isolation, and Durability. 𝟯) 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗧𝘂𝗻𝗶𝗻𝗴: 𝗧𝘂𝗿𝗯𝗼𝗰𝗵𝗮𝗿𝗴𝗲 𝗬𝗼𝘂𝗿 𝗤𝘂𝗲𝗿𝗶𝗲𝘀 Writing efficient SQL code is an art form. 𝗙𝗼𝗹𝗹𝗼𝘄 𝘁𝗵𝗲𝘀𝗲 𝘁𝗶𝗽𝘀 𝘁𝗼 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗲 𝘆𝗼𝘂𝗿 𝗾𝘂𝗲𝗿𝗶𝗲𝘀 𝗮𝗻𝗱 𝗲𝗻𝗵𝗮𝗻𝗰𝗲 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲: * Indexing Strategies: Use indexes to speed up data retrieval and minimize table scans. * Query Execution Plans: Understand how the database executes your queries and identify areas for improvement. * Normalization and Denormalization: Strike the right balance between data integrity and performance. * Avoiding Anti-Patterns: Say no to SELECT *, nested loops, and other performance killers. 𝟰) 𝗕𝗲𝘆𝗼𝗻𝗱 𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀: 𝗘𝘅𝗽𝗹𝗼𝗿𝗶𝗻𝗴 𝗡𝗲𝘄 𝗛𝗼𝗿𝗶𝘇𝗼𝗻𝘀 SQL isn't limited to relational databases. Explore these advanced topics to broaden your SQL expertise: NoSQL Databases: Understand the differences between SQL and NoSQL databases like MongoDB and Cassandra. 🎯Happy Learning! 📣Kindly share this post with your friends 📚If you found this useful ➡️like 🗨 ➡️Repost #SQLServer #SQL #Data #Tech #Coding #Programming #ContinuousLearning #SkillDevelopment #CareerGrowth #DataManagement #Database #PythonProgramming

4

Tip No. 4 for #CodingWithLLMs: Define Your Analysis Clearly Before diving into data analysis, it's important to have a solid plan in place. Here's how you can effectively strategize for data analysis with an LLM: 1. Understand The Context: Grasp the context of your analysis, the complication causing the need for your analysis, and the question you aim to answer. 2. Formulate a Hypothesis: Start with a clear, testable hypothesis based on your understanding of the dataset. What is the most probably answer to your question? 3. Develop Key Questions: These questions must directly test your hypothesis. Ensure they are specific and measurable. For example, when analyzing retail sales data: - The situation could be "Sales are higher on weekends." - The complication might be “Sales this past weekend were lower than usual.” - This logically leads to the question “Why were sales lower?” - Let’s assume your hypothesis is “Our biggest competitor’s weekend promotion reduced our sales.” - As a result, some key questions might include "What is the average sales volume weekends vs. the past weekend?" and "How do our biggest competitor’s promotional activities typically impact sales?" and other measurable questions. Armed with these ideas, you can guide the LLM to answer questions crucial to your analysis. Give the LLM this information before asking it to write code to analyze your data. Remember, LLMs do not comprehend the bigger picture. Their responses are solely based on patterns from the data they've been trained with, not strategic thinking. Unless you provide it, an LLM lacks context, hypothesis, and key questions. Stay tuned for tomorrow's final tip in this series, where we will discuss effective iteration techniques with your LLM to refine and improve your analyses. Art+Science Analytics Institute University of Notre Dame University of Notre Dame - Mendoza College of Business University of Illinois Urbana-Champaign University of Chicago ELVTR Grow with Google - Data Analytics #Analytics #DataStorytelling #DataScience #AI #MachineLearning #BusinessIntelligence #TechThursday

20

1 commentaire

🚀 𝘿𝙖𝙩𝙖 𝙎𝙘𝙞𝙚𝙣𝙘𝙚 𝙄𝙣𝙩𝙚𝙧𝙫𝙞𝙚𝙬 𝙌𝙪𝙚𝙨𝙩𝙞𝙤𝙣𝙨 📊 : What to Expect If you're aiming to crack a Data Science interview, it's essential to be well-prepared for a variety of technical, statistical, and problem-solving questions. Here are some common questions you might encounter during your interview: 𝟏. 𝐒𝐐𝐋 & 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬: Write a query to find the second-highest salary in a table of employees. How would you optimize a query that takes too long to execute? Explain the difference between INNER JOIN and OUTER JOIN. When would you use each? 𝟐. 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬: Explain the difference between supervised and unsupervised learning. How do you handle overfitting in a model? What is the difference between precision and recall? When would you prioritize one over the other? Explain how a Random Forest works. Why is it better than a single decision tree? 𝟑. 𝐏𝐫𝐨𝐛𝐚𝐛𝐢𝐥𝐢𝐭𝐲 & 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬: What is the probability of drawing 2 aces from a deck of cards? Explain Bayes' Theorem. Can you give an example where it’s applied? What is the Central Limit Theorem and why is it important? How would you set up an A/B test? What statistical measures would you use to evaluate the results? 𝟒. 𝐃𝐚𝐭𝐚 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐬 & 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬: Write a function to check if a string is a palindrome. How would you find the longest consecutive sequence in an unsorted array? Explain the difference between an array and a linked list. Implement a binary search algorithm. 𝟓. 𝐂𝐚𝐬𝐞 𝐒𝐭𝐮𝐝𝐢𝐞𝐬 & 𝐏𝐫𝐨𝐛𝐥𝐞𝐦 𝐒𝐨𝐥𝐯𝐢𝐧𝐠: How would you design a recommendation system for a new Microsoft product? Microsoft wants to reduce churn for its Xbox users. How would you approach this problem using data? Walk us through how you would build a predictive model for sales forecasting. 𝟔. 𝐁𝐞𝐡𝐚𝐯𝐢𝐨𝐫𝐚𝐥 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬: Describe a challenging project you worked on. How did you handle the difficulties? Tell us about a time you had to work under pressure with a tight deadline. How do you handle feedback and criticism on your work? 💡 Final Tip: Companies looks for candidates who are not only technically strong but also able to think critically and solve complex real-world problems. Practice these types of questions to build your confidence and problem-solving abilities. Book 1:1 Mentorship with Me - https://lnkd.in/gfeAj759 I will pin the Answers to these questions in comments Tomorrow #DataScience #Microsoft #InterviewQuestions #SQL #MachineLearning #TechInterviews #Freshers #CareerTips

5

1 commentaire

🚀 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐑𝐨𝐚𝐝𝐦𝐚𝐩: 𝐀 𝐒𝐭𝐞𝐩-𝐛𝐲-𝐒𝐭𝐞𝐩 𝐆𝐮𝐢𝐝𝐞 𝐟𝐨𝐫 𝟐𝟎𝟐𝟒 Want to start your career in 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭? Here’s a 𝐜𝐨𝐦𝐩𝐥𝐞𝐭𝐞 𝐫𝐨𝐚𝐝𝐦𝐚𝐩 𝐭𝐨 𝐛𝐞𝐜𝐨𝐦𝐢𝐧𝐠 𝐚 𝐬𝐮𝐜𝐜𝐞𝐬𝐬𝐟𝐮𝐥 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭! 𝟏. 𝐅𝐮𝐧𝐝𝐚𝐦𝐞𝐧𝐭𝐚𝐥𝐬 𝐨𝐟 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 📊 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 & 𝐏𝐫𝐨𝐛𝐚𝐛𝐢𝐥𝐢𝐭𝐲: Learn the basics of descriptive and inferential statistics. Key concepts include: Mean, Median, Mode, Standard Deviation, Variance Hypothesis Testing, Z-scores, T-scores Probability Distributions (Normal, Binomial, Poisson) 🧮 𝐌𝐚𝐭𝐡𝐞𝐦𝐚𝐭𝐢𝐜𝐬 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚: Linear algebra, calculus, and basic math principles to help in understanding algorithms. 𝟐. 𝐄𝐱𝐜𝐞𝐥 & 𝐒𝐩𝐫𝐞𝐚𝐝𝐬𝐡𝐞𝐞𝐭𝐬 📋 𝐃𝐚𝐭𝐚 𝐂𝐥𝐞𝐚𝐧𝐢𝐧𝐠: Learn to clean and manipulate data using tools like Excel or Google Sheets. 📊 𝐁𝐚𝐬𝐢𝐜 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: Perform simple analysis with functions like VLOOKUP, Pivot Tables, and charts. 📉 𝐃𝐚𝐭𝐚 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Create charts and dashboards to present data. 𝟑. 𝐒𝐐𝐋 - 𝐐𝐮𝐞𝐫𝐲𝐢𝐧𝐠 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬. 💻 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐁𝐚𝐬𝐢𝐜𝐬: Understand relational databases and how data is structured. 📋 𝐒𝐐𝐋 𝐂𝐨𝐦𝐦𝐚𝐧𝐝𝐬: Learn the basics of SQL (Structured Query Language) to: Select, Update, Delete, Join, and Aggregate data Create complex queries for analysis 🗃️ 𝐓𝐨𝐨𝐥𝐬: Practice on MySQL, PostgreSQL, SQL Server, or other databases. 𝟒. 𝐃𝐚𝐭𝐚 𝐌𝐚𝐧𝐢𝐩𝐮𝐥𝐚𝐭𝐢𝐨𝐧 𝐰𝐢𝐭𝐡 𝐏𝐲𝐭𝐡𝐨𝐧 𝐨𝐫 𝐑 🐍 𝐏𝐲𝐭𝐡𝐨𝐧: Master the basics of Python and libraries like Pandas, Numpy, and Matplotlib for: Data cleaning and manipulation Exploratory Data Analysis (EDA) Simple visualizations 𝟓. 𝐃𝐚𝐭𝐚 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐓𝐨𝐨𝐥𝐬 📈 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈/𝐓𝐚𝐛𝐥𝐞𝐚𝐮: Learn how to use BI tools to create interactive dashboards and reports. 📊 𝐕𝐢𝐬𝐮𝐚𝐥 𝐃𝐞𝐬𝐢𝐠𝐧 𝐁𝐞𝐬𝐭 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐬: Understand the importance of clear and concise visualizations. 𝟔. 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 & 𝐓𝐨𝐨𝐥𝐬 📊 𝐄𝐱𝐜𝐞𝐥 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝: Master advanced Excel techniques for data analysis. 📈 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈 / 𝐓𝐚𝐛𝐥𝐞𝐚𝐮: Learn to create comprehensive dashboards. 🔍 𝐆𝐨𝐨𝐠𝐥𝐞 𝐃𝐚𝐭𝐚 𝐒𝐭𝐮𝐝𝐢𝐨: Explore another powerful tool for visual reporting. 𝟕. 𝐕𝐞𝐫𝐬𝐢𝐨𝐧 𝐂𝐨𝐧𝐭𝐫𝐨𝐥 𝐰𝐢𝐭𝐡 𝐆𝐢𝐭 🛠️ 𝐆𝐢𝐭/𝐆𝐢𝐭𝐇𝐮𝐛 𝐁𝐚𝐬𝐢𝐜𝐬: Learn how to use Git for version control and collaborate on projects with GitHub repositories. 𝟖. 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬 & 𝐏𝐨𝐫𝐭𝐟𝐨𝐥𝐢𝐨  📁 𝐁𝐮𝐢𝐥𝐝 𝐚 𝐏𝐨𝐫𝐭𝐟𝐨𝐥𝐢𝐨: Showcase your skills by working on real-life datasets and publishing your projects on platforms like GitHub, Kaggle, or a personal portfolio website. 🔗 𝐂𝐨𝐧𝐧𝐞𝐜𝐭 𝐰𝐢𝐭𝐡 𝐦𝐞 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 𝐫𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬 𝐚𝐧𝐝 𝐠𝐮𝐢𝐝𝐚𝐧𝐜𝐞 𝐨𝐧 𝐛𝐞𝐜𝐨𝐦𝐢𝐧𝐠 𝐚 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭! #DataAnalytics #BusinessIntelligence #SQL #Python #MachineLearning #DataVisualization #PowerBI #CareerInTech

27

2 commentaires

𝐓𝐡𝐞 Free 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐑𝐨𝐚𝐝𝐦𝐚𝐩 Share with everyone so that everyone get benifts <> 7000+ Course Free Access : https://lnkd.in/e7A7hd-t Google Data Analytics:- 🔹https://lnkd.in/edtXhkzb <LimiTed> < FREE RESOURCES - https://lnkd.in/eiHx4pXk [[𝐌𝐚𝐭𝐡𝐞𝐦𝐚𝐭𝐢𝐜𝐬:]] - Learn the basics of linear algebra, calculus, and Understand advanced concepts. Mathematics: https://lnkd.in/ejH42Rs7 [[𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠:]] - Learn Python and R, the most popular programming languages. - Master essential libraries like NumPy, Pandas, Matplotlib. - Learn how to use databases like SQL and MongoDB. 🔹Python: https://lnkd.in/eyFAXpnZ 🔹R language: https://lnkd.in/d35WCs37 🔹SQL: https://lnkd.in/e26sy9N3 🔹MongoDB: https://lnkd.in/eC8Dgj66 [[𝐏𝐫𝐨𝐛𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐚𝐧𝐝 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬:]] - Understand the fundamentals of probability and statistics. - Learn how to apply these concepts to real-world data problems. 🔹Probability: https://lnkd.in/ePMjCpVc 🔹Statistics: https://lnkd.in/ePMjCpVc [[𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠:]] - Learn the basics of machine learning, including model construction, data exploration, and validation. - Explore intermediate concepts like handling missing values, categorical variables. - Dive into ensemble learning techniques like Random Forests. ◽https://lnkd.in/ea4bYqTx [[𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠:]] - Learn about artificial neural networks, convolutional neural networks, and recurrent neural networks. - Implement deep learning models using TensorFlow, or PyTorch. - Understand crucial concepts like stochastic gradient descent, dropout. 🔹https://lnkd.in/ekrAuy6x [[𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠:]] - Learn the art of feature engineering, from creating baseline models to encoding categorical variables, generating new features, and selecting the most impactful features for your models. Feature Engineering : 🔹https://lnkd.in/edc9sN2i [[𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭:]] - Learn how to deploy your data science models to production using cloud platforms like Microsoft Azure, or Google Cloud Platform. [Happy Learning📚] [Follow for more such content] follow @Kanchan Kumari for more Like comment 💥 ⭐ #datascience #datascienceroadmap #datascience

113

27 commentaires

Trustpilot Review Data Scraping Email: [email protected] https://buff.ly/3LNLVDc Unlock valuable insights with Trustpilot Review Data Scraping Services by https://buff.ly/3mDFB8b. Access detailed review data, including ratings, comments, reviewer profiles, and timestamps. Enhance your customer feedback analysis, improve your products and services, and drive business growth with accurate and comprehensive review data. Discover more today! Skype: nprojectshub #trustpilotreviewdatascraping #scrapingtrustpilotbusinessreviews #datascrapingservices #datamining #dataanalytics #webscraping #datascraping #webscrapingexpert #webcrawler #webscraper #datamining #dataentry #emaillistscraping #emaildatabase

1

🛑 Stop paying for online courses ever again. SQL Roadmap with FREE courses Learning SQL is essential for anyone dealing with relational databases or working in the field of data management. So You can take benifts from below ones 👇 🔺 7000+ Course Free Access : https://lnkd.in/dc7dUxkj <>. Google Data Analytics: 🔹https://lnkd.in/e3uPshjh Repost so that all can take benifits 1. Learn SQL Basics for Data Science Specialization In this course, you will learn to: -Master SQL Commands -Data Preparation -End-to-End Data Pipeline -Project Development and Presentation: 🔺Link: https://lnkd.in/eFfhe57d 2. Google Data Analytics Professional Certificate -This is your path to a career in data analytics. In this program, you’ll learn in-demand skills that will have you job-ready in less than 6 months. No degree or experience required. 🔹Link: https://lnkd.in/euJUQjiP 3.SQL for Data Science What you'll learn -Identify a subset of data needed from a column or set of columns and write a SQL query to limit to those results. -Use SQL commands to filter, sort, and summarize data. -Create an analysis table from multiple queries using the UNION operator. -Manipulate strings, dates, & numeric data using functions to integrate data from different sources into fields with the correct format for analysis. 🔺Link: https://lnkd.in/eAX2aehf 4.Databases and SQL for Data Science with Python What you'll learn -Analyze data within a database using SQL and Python. -Create a relational database and work with multiple tables using DDL commands. -Construct basic to intermediate level SQL queries using DML commands. Compose more powerful queries with advanced SQL techniques like views, transactions, stored procedures, and joins. 🔹Link: https://lnkd.in/emhYQ9A4 5. Introduction to Structured Query Language (SQL) What you'll learn -Learn about the basic syntax of the SQL language, as well as database design with multiple tables, foreign keys, and the JOIN operation. -Learn to model many-to-many relationships like those needed to represent users, roles, and courses.What you'll learn -Learn about the basic syntax of the SQL language, as well as database design with multiple tables, foreign keys, and the JOIN operation. -Learn to model many-to-many relationships like those needed to represent users, roles, and courses. 🔺Link: https://lnkd.in/ea7YQBpk Pdf credit- Respective Owner follow Suman Kumar for more Like 👍 Comment ✍️ repost 🔄 #sql #sqldatabase #freecertification #freecourses #dataanalytics #datasciences

209

44 commentaires

🛑 Stop paying for online courses ever again. SQL Roadmap with FREE courses Learning SQL is essential for anyone dealing with relational databases or working in the field of data management. So You can take benifts from below ones 👇 🔺 7000+ Course Free Access : https://lnkd.in/dc7dUxkj <>. Google Data Analytics: 🔹https://lnkd.in/e3uPshjh Repost so that all can take benifits 1. Learn SQL Basics for Data Science Specialization In this course, you will learn to: -Master SQL Commands -Data Preparation -End-to-End Data Pipeline -Project Development and Presentation: 🔺Link: https://lnkd.in/eFfhe57d 2. Google Data Analytics Professional Certificate -This is your path to a career in data analytics. In this program, you’ll learn in-demand skills that will have you job-ready in less than 6 months. No degree or experience required. 🔹Link: https://lnkd.in/euJUQjiP 3.SQL for Data Science What you'll learn -Identify a subset of data needed from a column or set of columns and write a SQL query to limit to those results. -Use SQL commands to filter, sort, and summarize data. -Create an analysis table from multiple queries using the UNION operator. -Manipulate strings, dates, & numeric data using functions to integrate data from different sources into fields with the correct format for analysis. 🔺Link: https://lnkd.in/eAX2aehf 4.Databases and SQL for Data Science with Python What you'll learn -Analyze data within a database using SQL and Python. -Create a relational database and work with multiple tables using DDL commands. -Construct basic to intermediate level SQL queries using DML commands. Compose more powerful queries with advanced SQL techniques like views, transactions, stored procedures, and joins. 🔹Link: https://lnkd.in/emhYQ9A4 5. Introduction to Structured Query Language (SQL) What you'll learn -Learn about the basic syntax of the SQL language, as well as database design with multiple tables, foreign keys, and the JOIN operation. -Learn to model many-to-many relationships like those needed to represent users, roles, and courses.What you'll learn -Learn about the basic syntax of the SQL language, as well as database design with multiple tables, foreign keys, and the JOIN operation. -Learn to model many-to-many relationships like those needed to represent users, roles, and courses. 🔺Link: https://lnkd.in/ea7YQBpk Pdf credit- Respective Owner Follow Suman Kumar for more Like 👍 Comment ✍️ Repost 🔄 #sql #sqldatabase #freecertification #freecourses #dataanalytics #datasciences#roadmap LinkedIn for Creators LinkedIn Learning

155

31 commentaires

Massive Amazon Hack & Tip List - Day 135/365📝 Are you polishing brass on the Titanic? By now I hope you're on board with me that A/B testing is important. But how to test is almost... importanter. Let's take a look at the two methods: 1) Refinement 2) Exploration Here’s how it works: Bob has always loved chocolate chip cookies. He loves them so much that he bakes them all the time. He’s constantly tweaking the levels of his ingredients – a little more flour here, some salt there, but the basic idea is the same. Bob’s trying to refine the recipe to be absolutely perfect. One day, Bob takes a bite of a cranberry and is inspired. He tries an entirely new form of cookie. Bob decides to add cranberries and white chocolate to a cookie instead. Bob is exploring an entirely new way to make a cookie. You see the difference between the two scenarios? That’s the difference between “Refinement” and “Exploration.” One method attempts to take what we already have and make small changes and tweaks to make it better. The other explores, experiments, and tries something totally new. Both are completely valid ways of A/B testing your main images. Refinement sometimes misses entirely new, unique ideas as the technique attempts to iterate on one main idea only. Whereas exploration can often yield extremely interesting and unique results, but often won’t yield the most optimized main image (if there even can be such a thing). Both approaches to A/B testing are complimentary. As a general rule: Begin with an exploratory technique. Try wildly different ideas for your images. Then, refine and refine until you have something wonderful. Come back tomorrow when I show you examples (with pictures) of the two testing types. ♻️ Instantly Repost with a friend who needs to hear this :) See you back here tomorrow at 10:15 EST for the next one (I post at the same time every day) -- #amazonfba #amazonfbaseller #amazonseller

8

10 commentaires

10 interview practical questions focused on Python decorators and regular expressions (regex) 🎯 📍 Write a decorator that logs the execution time of a function. 📍 Implement a decorator that restricts access to a function based on user roles. 📍 Create a decorator that allows a function to be executed a specified number of times. 📍 Write a memoization decorator that caches function results for given inputs. 📍 Develop a retry decorator that retries a function upon failure up to a specified number of attempts. 📍 Write a regex to validate an email address format. 📍 Using regex, extract all phone numbers from a given text in the format XXX-XXX-XXXX. 📍 Write a regex pattern to find and replace all dates in the format DD/MM/YYYY with YYYY-MM-DD in a text. 📍 Create a regex pattern to extract all URLs from a block of text. 📍 Write a regex to remove all non-alphanumeric characters from a string except spaces. Grow Data Skills Shashank Mishra 🇮🇳 Shubhankit Sirvaiya Aman Kumar Sahil Choudhary

27

1 commentaire

𝐓𝐡𝐞 Free 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐑𝐨𝐚𝐝𝐦𝐚𝐩 Share with everyone so that everyone get benifts <> 7000+ Course Free Access : https://lnkd.in/e7A7hd-t Google Data Analytics:- 🔹https://lnkd.in/edtXhkzb [[𝐌𝐚𝐭𝐡𝐞𝐦𝐚𝐭𝐢𝐜𝐬:]] - Learn the basics of linear algebra, calculus, and Understand advanced concepts. Mathematics: https://lnkd.in/ejH42Rs7 [[𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠:]] - Learn Python and R, the most popular programming languages. - Master essential libraries like NumPy, Pandas, Matplotlib. - Learn how to use databases like SQL and MongoDB. 🔹Python: https://lnkd.in/eyFAXpnZ 🔹R language: https://lnkd.in/d35WCs37 🔹SQL: https://lnkd.in/e26sy9N3 🔹MongoDB: https://lnkd.in/eC8Dgj66 [[𝐏𝐫𝐨𝐛𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐚𝐧𝐝 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬:]] - Understand the fundamentals of probability and statistics. - Learn how to apply these concepts to real-world data problems. 🔹Probability: https://lnkd.in/ePMjCpVc 🔹Statistics: https://lnkd.in/ePMjCpVc [[𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠:]] - Learn the basics of machine learning, including model construction, data exploration, and validation. - Explore intermediate concepts like handling missing values, categorical variables. - Dive into ensemble learning techniques like Random Forests. ◽https://lnkd.in/ea4bYqTx [[𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠:]] - Learn about artificial neural networks, convolutional neural networks, and recurrent neural networks. - Implement deep learning models using TensorFlow, or PyTorch. - Understand crucial concepts like stochastic gradient descent, dropout. 🔹https://lnkd.in/ekrAuy6x [[𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠:]] - Learn the art of feature engineering, from creating baseline models to encoding categorical variables, generating new features, and selecting the most impactful features for your models. Feature Engineering : 🔹https://lnkd.in/edc9sN2i [[𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭:]] - Learn how to deploy your data science models to production using cloud platforms like Microsoft Azure, or Google Cloud Platform. - Build web applications with Flask or Django. Microsoft Azure: ◽https://lnkd.in/eTi--pHH [Happy Learning 📚] FREE [t.me/NotesGallery1 / t.me/CodingBugs / wa.oia.bio/jobs] Follow Kanchan Kumari for more Like comment 👍👍 #datascience #datascienceroadmap #datascience

84

26 commentaires

What concepts are commonly asked in 𝗠𝗟 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀? These are fair game in interviews at 𝘀𝘁𝗮𝗿𝘁𝘂𝗽𝘀, 𝗰𝗼𝗻𝘀𝘂𝗹𝘁𝗶𝗻𝗴 𝗳𝗶𝗿𝗺𝘀 & 𝗙𝗔𝗔𝗡𝗚𝘀. Here's a comprehensive list 👇👇👇 𝗠𝗟 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 ↳ Variance-Bias Trade-off ↳ Regression Algorithms (e.g. Linear, Polynomial) ↳ Classification Algorithms (e.g. GBM) ↳ Clustering Algorithms ↳ Deep Learning (e.g. DNN, LSTM, Transformers)  ↳ Reinforcement Learning ↳ Model Evaluation Metrics (Precision, Recall, AUC-ROC) ↳ Handling Missing Data ↳ Feature Scaling (Normalization, Standardization) ↳ Feature Selection Techniques ↳ Dimensionality Reduction (PCA, t-SNE) ↳ Encoding Categorical Data ↳ Hyperparameter Tuning ↳ Cross-Validation Techniques ↳ Regularization Methods (L1, L2) 𝗖𝗼𝗱𝗶𝗻𝗴 ↳ Python for ML ↳ Writing Efficient Code ↳ Data Structures and Algorithms ↳ Implementing ML Algorithms from Scratch ↳ Working with ML Libraries (scikit-learn, PyTorch) 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 ↳ Data Ingestion and Preprocessing ↳ ETL/ELT Processes ↳ Handling Big Data (Hadoop, Spark) ↳ Kafka for streaming  ↳ Caching ↳ SQL vs noSQL ↳ Load Balancing  ↳ Edge Deploymebt 𝗠𝗟 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 ↳ Design Recommender System ↳ Fraud Detection System ↳ Real-Time Bidding ↳ Chatbot Architecture ↳ Sentiment Analysis Pipeline ↳ Image Classification System ↳ Voice Recognition System 𝗠𝗟 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 ↳ Model Serving (Batch, Real-Time) ↳ Monitoring and Maintaining Models in Production ↳ Model Retraining Strategies ↳ Workflow Orchestration (Airflow, Kubeflow) ↳ Experiment Tracking (MLflow) ↳ Model Registry and Versioning 👉 I am kicking off the next cohort for the 𝗠𝗟𝗘 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗕𝗼𝗼𝘁𝗰𝗮𝗺𝗽 🚀: https://lnkd.in/e6HbN6dy. It starts in 3 weeks. Join soon! Ace upcoming interviews with these👇 📕 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗣𝗿𝗲𝗽 𝗖𝗼𝘂𝗿𝘀𝗲𝘀: https://lnkd.in/gzgB-dHT 📘 𝗝𝗼𝗶𝗻 𝗗𝗦 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗕𝗼𝗼𝘁𝗰𝗮𝗺𝗽: https://lnkd.in/eiA5Ntdp 📗 𝗔𝗕 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 𝗖𝗼𝘂𝗿𝘀𝗲: https://lnkd.in/g82dMJ77 👉 Smash 👍 and follow Daniel Lee to land dream data & AI jobs 🚀

335

9 commentaires

𝗗𝗮𝘆 𝟭𝟭𝟯: 𝗦𝗤𝗟 𝗦𝗲𝗿𝗶𝗲𝘀 – 𝗘𝘅𝗽𝗹𝗼𝗿𝗶𝗻𝗴 𝘁𝗵𝗲 𝗠𝗮𝗴𝗶𝗰 𝗼𝗳 𝗪𝗶𝗻𝗱𝗼𝘄 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀! 🚀 Today’s Focus: We dove into the realm of window functions in SQL, which are essential for performing calculations across sets of rows without collapsing them into a single output, as aggregation functions do. 𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀: What Are Window Functions? These are SQL functions used to perform calculations like running totals, moving averages, rank assignments, and more over a set of rows related to the current row. 𝗦𝘆𝗻𝘁𝗮𝘅 𝗢𝘃𝗲𝗿𝘃𝗶𝗲𝘄: Commonly used keywords: OVER, PARTITION BY, and ORDER BY. 𝗣𝗼𝗽𝘂𝗹𝗮𝗿 𝗪𝗶𝗻𝗱𝗼𝘄 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀: 𝗥𝗔𝗡𝗞(), 𝗗𝗘𝗡𝗦𝗘_𝗥𝗔𝗡𝗞(), 𝗥𝗢𝗪_𝗡𝗨𝗠𝗕𝗘𝗥(): Used for ranking rows. 𝗡𝗧𝗜𝗟𝗘(): Distributes rows into a specified number of groups. 𝗟𝗘𝗔𝗗(), 𝗟𝗔𝗚(): Access data from subsequent or preceding rows. 𝗦𝗨𝗠(), 𝗔𝗩𝗚(), 𝗠𝗔𝗫(), 𝗠𝗜𝗡(): Can be used as window functions for cumulative calculations. Why Window Functions? They provide powerful ways to analyze and manipulate data in ways that aren’t possible with simple aggregate functions. They are especially useful in reporting and data analytics. 💬 Question for You: How have you used window functions in your projects, or is there a scenario you'd like to discuss where these could be beneficial? Let’s keep the SQL learning rolling! #SQLSeries #WindowFunctions #DataAnalysis #ContinuousLearning

13

1 commentaire

Microsoft Offering Power BI Courses for 2024 with Certificates 🔥🔥 🔵 Join my telegram for regular job updates, free coding resources and premium notes: https://t.me/Giant_Code 1. Getting Started with Power BI Desktop 🔗 https://lnkd.in/gkjSgDaQ 2. Build Dashboards in Power BI 🔗 https://lnkd.in/gScyJYnC 3. Use Power Bi for Financial Data Analysis 🔗 https://lnkd.in/gUn4d-tC 4 Data-Driven Decisions with Power BI 🔗 https://lnkd.in/gvxriv_G 5. HR Analytics- Build an HR dashboard using Power BI 🔗 https://lnkd.in/gkYCdigJ 6. Create a Sales Dashboard using Power BI 🔗 https://lnkd.in/gFSAxrnN 7. Build an Income Statement Dashboard in Power BI 🔗 https://lnkd.in/gZ2xGPvu 8. Prepare, Clean, Transform, and Load Data using Power BI 🔗 https://lnkd.in/g_zByYNS 9. Power Bi Report Development Crash Course 🔗https://lnkd.in/gY8n-8j7 ✳️Business Case Analysis 1. Introduction to Spreadsheets an Model 🔗https://lnkd.in/gNyq6UMV 2. Business Analysis & Process Management 🔗https://lnkd.in/gvYxB5EW 3. Analysis of Business Problems 🔗https://lnkd.in/g8CtAsW5 4. How to Use the Business Model Canvas for Strategic Analysis 🔗https://lnkd.in/g26BFMsf 5. Managerial Economics and Business Analysis Specialization 🔗https://lnkd.in/gY6U8tJz 6. Value and Business Decision Making 🔗https://lnkd.in/gqjjyA3D 7. Advanced Business Strategy 🔗https://lnkd.in/gR2FNB_G 8. Excel Fundamentals for Data Analysis 🔗https://lnkd.in/gPCreGe2 9. Business Intelligence and Data Warehousing 🔗https://lnkd.in/g9khkkVH 10. Metaverse 🔗https://lnkd.in/gNMEiMsn 11. Meta Social Media Marketing 🔗https://lnkd.in/ga2BGtHR 12. Meta Marketing Analytics 🔗https://lnkd.in/gm7HYvuc 13. Meta Database Engineer 🔗https://lnkd.in/gdpg3vVv 14. Meta Front-End Developer 🔗https://lnkd.in/g6u5aZy6 15. Meta Back-End Developer 🔗https://lnkd.in/gkY5UiaW 16. Meta Android Developer 🔗https://lnkd.in/g5968xD6 17. Meta iOS Developer 🔗https://lnkd.in/gxsJXqV8 18. Mobile Development and JavaScript 🔗https://lnkd.in/g3HHu-Hm 19. Meta React Native 🔗https://lnkd.in/gs9TcgVy 20. Google Advanced Data Analytics Capstone 🔗https://lnkd.in/gxbnB79b 21. Google Data Analytics: 🔗 https://lnkd.in/gxbnB79b 22. Foundations of Project Management 🔗https://lnkd.in/e-_NNbm3 23. Project Execution: Running the Project 🔗https://lnkd.in/e4f9ekcJ 24. Advanced Google Analytics 🔗https://lnkd.in/gxbnB79b 25. Data Analysis with R Programming 🔗 https://lnkd.in/etFW5E9p 26. Project Initiation: Starting a Successful Project 🔗https://lnkd.in/exNY7zKP Happy learning!!! 👉𝗝𝗼𝗶𝗻 𝘁𝗵𝗶𝘀 𝘁𝗲𝗹𝗲𝗴𝗿𝗮𝗺 𝗴𝗿𝗼𝘂𝗽 𝗳𝗼𝗿 𝗝𝗼𝗯𝘀/𝗡𝗼𝘁𝗲𝘀 https://t.me/Giant_Code 👉Follow I for more such content. Pdf credit- Respective Owner hashtag#PowerBI hashtag#coursera hashtag#onlinelearning

107

21 commentaires

🚀 Data Preprocessing Sheet ( The Cheat One ) ! 📊 🔍 Handling Missing Values Identify: df.isnull().sum() Drop: df.dropna() Fill: df.fillna(value) Interpolate: df.interpolate() 📈 Data Transformation Standardize: (df - df.mean()) / df.std() Normalize: (df - df.min()) / (df.max() - df.min()) Log Transform: np.log(df) 🔢 Feature Encoding One-Hot: pd.get_dummies(df) Label: sklearn.preprocessing.LabelEncoder() 📊 Feature Scaling Robust Scaler: sklearn.preprocessing.RobustScaler() MaxAbsScaler: sklearn.preprocessing.MaxAbsScaler() 🧹 Data Cleaning Trim: df['column'].str.strip() Replace: df.replace(old_value, new_value) 🔍 Feature Selection Variance Threshold: sklearn.feature_selection.VarianceThreshold() SelectKBest: sklearn.feature_selection.SelectKBest() 📅 Time Series Data Convert: pd.to_datetime(df['column']) Resample: df.resample('D').mean() 💡 Advanced DataFrame Operations Apply: df.apply(lambda x: ...) GroupBy: df.groupby('column').aggregate(function) 🚀 Unveiling the world of data science through Shivai, my own startup venture. I'm Tripathi Aditya Prakash, and I'm here to make data science, AI, and machine learning approachable and exciting for everyone. 🌟 🎧 Podcasts Alert! Dive into the world of data science with my podcasts: Shivai Data Revelations: Join me as we explore the latest trends and breakthroughs in data science. Whether you're just starting out or are a seasoned expert, this podcast offers a wealth of knowledge and insights. Listen on Spotify: https://lnkd.in/dD_bPZeJ That Data Guy: A special podcast for those navigating the corporate data world. Packed with practical tips and real-world experiences, it's a must-listen for data professionals at the start of their journey. Tune in for advice that hits the mark. 📊💼 . Listen on Spotify: https://lnkd.in/gyU7Cq-w 📚 Expand your data knowledge on my Medium page: https://lnkd.in/dirGC7ca 🎥 Explore the visual side of analytics and machine learning on my YouTube channel: https://lnkd.in/dvvWVxgF , https://lnkd.in/gNUtzYFb 📸 Discover the art of data on my Instagram: https://lnkd.in/dHwMhezE , https://lnkd.in/gG76kwP8 🐦 Get quick bites of data science on my Twitter: https://lnkd.in/dh8Sn7rT , https://lnkd.in/gktSguBh Substack: Data Dilemmas – Join the community for unique insights at the nexus of data and personal development : https://lnkd.in/g3ZPz_CX With Shivai, let's navigate the vast ocean of data science and decode the future, one byte at a time. ✨ ✨ Keep this cheat sheet handy for your next data project! 🚀 #DataScience #MachineLearning #DataPreprocessing #DataCleaning #FeatureEngineering 🔄 Appreciate my content? Stay connected and engage with my posts. Follow me, Tripathi Aditya Prakash, and Shivai for a continuous stream of enlightening data science content!

17

𝐃𝐒 𝐮𝐬𝐢𝐧𝐠 𝐂 𝐯𝐬 𝐃𝐒𝐀 𝐮𝐬𝐢𝐧𝐠 𝐂++😖 "𝐃𝐚𝐭𝐚 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐬 𝐮𝐬𝐢𝐧𝐠 𝐂" and "𝐃𝐚𝐭𝐚 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐬 𝐚𝐧𝐝 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 𝐮𝐬𝐢𝐧𝐠 𝐂++" are distinct courses or topics. Here's why: "Data Structures (DS) refers to the efficient organization of data for easy access and manipulation. Implementing in C involves writing code from scratch and handling data structures manually. In contrast, Data Structures and Algorithms (DSA) often use languages like C++ or Java, which provide built-in functionalities and libraries. Thus, we refer to it as 'DSA using C++' or 'DSA using Java/Python' to highlight the use of these languages’ advanced features and pre-built algorithms. 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞-𝐒𝐩𝐞𝐜𝐢𝐟𝐢𝐜 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧: 𝐃𝐚𝐭𝐚 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐬 𝐮𝐬𝐢𝐧𝐠 𝐂: Focuses on implementing fundamental data structures (like arrays, linked lists, stacks, queues, trees, and graphs) using the C programming language. 𝐃𝐚𝐭𝐚 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐬 𝐚𝐧𝐝 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 𝐮𝐬𝐢𝐧𝐠 𝐂++: Covers not only data structures but also algorithmic techniques and their implementations using C++. This typically includes advanced data structures and algorithms, taking advantage of C++'s features like classes and templates. 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬: 𝐂: Provides a procedural approach, which is more manual and lower-level. Good for understanding fundamental concepts of data structures. 𝐂++: Supports object-oriented programming and the Standard Template Library (STL), which offers pre-built data structures and algorithms. This can simplify the implementation of complex structures and algorithms. 𝐂𝐨𝐮𝐫𝐬𝐞 𝐎𝐛𝐣𝐞𝐜𝐭𝐢𝐯𝐞𝐬: 𝐂: Often used to build a strong foundation in data structures by manually handling memory and implementing structures from scratch. 𝐂++ Ideal for learning data structures and algorithms with modern tools and paradigms, leveraging C++'s rich standard library for more advanced topics. So, specifying "𝐃𝐚𝐭𝐚 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐬 𝐮𝐬𝐢𝐧𝐠 𝐂" and "𝐃𝐚𝐭𝐚 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐬 𝐚𝐧𝐝 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 𝐮𝐬𝐢𝐧𝐠 𝐂++" accurately reflects the focus and tools used in each course. Share your Thoughts in comments 👇

17

2 commentaires

Simple RoadMap To Become A Machine Learning Engineer ⤵️ This roadmap guides you from beginner to proficient ML engineer. It emphasizes hands-on learning, continuous growth, and specialization. Top courses to get... Courses: 1. Machine Learning (Coursera - Stanford University) 🔗 https://lnkd.in/gNXTg8aP 2. Deep Learning Specialization 🔗 https://lnkd.in/gSUchfqm 3. Machine Learning Engineering for Production (MLOps) Specialization 🔗 https://lnkd.in/dP3285Ry 4. Natural Language Processing Specialization 🔗 https://lnkd.in/g4D6RmVX _________________ Here is the simple ROADMAP... 1️⃣ Build Foundations (Months 1-3) ⤵️ - Essential Math: Linear algebra: Vectors, matrices, eigenvalues (Khan Academy, MIT OCW 18.06) - Calculus: Derivatives, gradients (Khan Academy, MIT OCW 18.01/18.02) Statistics: Distributions, hypothesis testing (StatQuest, Coursera) - Python Mastery: Basics, OOP, libraries (NumPy, Pandas, Scikit-learn) - Courses: FreeCodeCamp, Coursera (University of Michigan), Udemy Core ML Concepts: Bias-variance tradeoff, overfitting, regularization - Evaluation metrics: Accuracy, precision, recall, F1-score Course: Machine Learning (Coursera - Andrew Ng) 2️⃣ Hands-on Projects (Months 4-6) ⤵️ - Apply Your Skills: Kaggle competitions, UCI ML Repository, real-world problems - Feature engineering, model comparison, hyperparameter tuning (GridSearchCV) - Build a Portfolio: Showcase projects on GitHub, create a personal website/blog 3️⃣ Phase 3: Specialization (Months 7-12+) ⤵️ - Choose Your Focus: Deep Learning: Neural networks, computer vision, NLP (Coursera - Andrew Ng, fast.ai) - NLP: Text processing, sentiment analysis (Hugging Face, Coursera) Computer Vision: Image recognition, object detection (Stanford CS231n, OpenCV) - MLOps: Deploying and maintaining ML models (Coursera) - Master Advanced Tools: Cloud platforms: AWS SageMaker, Google AI Platform, Azure ML - Experiment tracking: Weights & Biases, MLflow - Model deployment: Docker, Kubernetes, Flask/Django APIs - Continuous Learning: Research papers (arXiv), conferences (NeurIPS, ICML), open-source contributions

150

19 commentaires