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Python Projects & Free Books

Python Projects & Free Books

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Python Interview Projects & Free Courses Admin: @Coderfun

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Python Projects & Free Books (@pythonfreebootcamp) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 40 857 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 3 346-o'rinni va Hindiston mintaqasida 10 078-o'rinni egallagan.

๐Ÿ“Š Auditoriya koโ€˜rsatkichlari va dinamika

ะฝะตะฒั–ะดะพะผะพ sanasidan buyon loyiha tez oโ€˜sib, 40 857 obunachiga ega boโ€˜ldi.

04 Iyun, 2026 dagi oxirgi maโ€™lumotlarga koโ€˜ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 156 ga, soโ€˜nggi 24 soatda esa 58 ga oโ€˜zgardi va umumiy qamrov yuqori darajada qolmoqda.

  • Tasdiqlash holati: Tasdiqlanmagan
  • Jalb etish (ER): Auditoriya oโ€˜rtacha 3.73% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 0.77% ini tashkil etuvchi reaksiyalarni toโ€˜playdi.
  • Post qamrovi: Har bir post oโ€˜rtacha 1 526 marta koโ€˜riladi; birinchi sutkada odatda 314 ta koโ€˜rish yigโ€˜iladi.
  • Reaksiyalar va oโ€˜zaro taโ€™sir: Auditoriya faol: har bir postga oโ€˜rtacha 5 ta reaksiya keladi.
  • Tematik yoโ€˜nalishlar: Kontent learning, analyst, framework, link:-, structure kabi asosiy mavzularga jamlangan.

๐Ÿ“ Tavsif va kontent siyosati

Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida taโ€™riflaydi:
โ€œPython Interview Projects & Free Courses Admin: @Coderfunโ€

Yuqori yangilanish chastotasi (oxirgi maโ€™lumot 05 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli boโ€˜lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim taโ€™sir nuqtasiga aylantirishini koโ€˜rsatadi.

40 857
Obunachilar
+5824 soatlar
+247 kunlar
+15630 kunlar
Postlar arxiv
How to get job as python fresher? 1. Get Your Python Fundamentals Strong You should have a clear understanding of Python syntax, statements, variables & operators, control structures, functions & modules, OOP concepts, exception handling, and various other concepts before going out for a Python interview. 2. Learn Python Frameworks As a beginner, youโ€™re recommended to start with Django as it is considered the standard framework for Python by many developers. An adequate amount of experience with frameworks will not only help you to dive deeper into the Python world but will also help you to stand out among other Python freshers. 3. Build Some Relevant Projects You can start it by building several minor projects such as Number guessing game, Hangman Game, Website Blocker, and many others. Also, you can opt to build few advanced-level projects once youโ€™ll learn several Python web frameworks and other trending technologies. @crackingthecodinginterview 4. Get Exposure to Trending Technologies Using Python. Python is being used with almost every latest tech trend whether it be Artificial Intelligence, Internet of Things (IOT), Cloud Computing, or any other. And getting exposure to these upcoming technologies using Python will not only make you industry-ready but will also give you an edge over others during a career opportunity. 5. Do an Internship & Grow Your Network. You need to connect with those professionals who are already working in the same industry in which you are aspiring to get into such as Data Science, Machine learning, Web Development, etc. Python Interview Q&A: https://topmate.io/coding/898340 Like for more โค๏ธ ENJOY LEARNING ๐Ÿ‘๐Ÿ‘

๐Ÿฒ ๐—™๐—ฟ๐—ฒ๐—ฒ ๐—™๐˜‚๐—น๐—น ๐—ง๐—ฒ๐—ฐ๐—ต ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐—ฌ๐—ผ๐˜‚ ๐—–๐—ฎ๐—ป ๐—ช๐—ฎ๐˜๐—ฐ๐—ต ๐—ฅ๐—ถ๐—ด๐—ต๐˜ ๐—ก๐—ผ๐˜„๐Ÿ˜ Ready to level up your tech game wi
๐Ÿฒ ๐—™๐—ฟ๐—ฒ๐—ฒ ๐—™๐˜‚๐—น๐—น ๐—ง๐—ฒ๐—ฐ๐—ต ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐—ฌ๐—ผ๐˜‚ ๐—–๐—ฎ๐—ป ๐—ช๐—ฎ๐˜๐—ฐ๐—ต ๐—ฅ๐—ถ๐—ด๐—ต๐˜ ๐—ก๐—ผ๐˜„๐Ÿ˜ Ready to level up your tech game without spending a rupee? These 6 full-length courses are beginner-friendly, 100% free, and packed with practical knowledge๐Ÿ“š๐Ÿง‘โ€๐ŸŽ“ Whether you want to code in Python, hack ethically, or build your first Android app โ€” these videos are your shortcut to real tech skills๐Ÿ“ฑ๐Ÿ’ป ๐‹๐ข๐ง๐ค๐Ÿ‘‡:- https://pdlink.in/42V73k4 Save this list and start crushing your tech goals today!โœ…๏ธ

List Comprehension in Python
+6
List Comprehension in Python

Fullstack Developer Skills & Technologies
+8
Fullstack Developer Skills & Technologies

Build Machine Learning Projects in Python โœ…
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Build Machine Learning Projects in Python โœ…

๐—”๐—ฐ๐—ฒ ๐—ฌ๐—ผ๐˜‚๐—ฟ ๐—ฆ๐—ค๐—Ÿ ๐—œ๐—ป๐˜๐—ฒ๐—ฟ๐˜ƒ๐—ถ๐—ฒ๐˜„ ๐˜„๐—ถ๐˜๐—ต ๐—ง๐—ต๐—ฒ๐˜€๐—ฒ ๐Ÿฏ๐Ÿฌ ๐— ๐—ผ๐˜€๐˜-๐—”๐˜€๐—ธ๐—ฒ๐—ฑ ๐—ค๐˜‚๐—ฒ๐˜€๐˜๐—ถ๐—ผ๐—ป๐˜€! ๐Ÿ˜ ๐Ÿคฆ๐Ÿปโ€โ™€๏ธStruggli
๐—”๐—ฐ๐—ฒ ๐—ฌ๐—ผ๐˜‚๐—ฟ ๐—ฆ๐—ค๐—Ÿ ๐—œ๐—ป๐˜๐—ฒ๐—ฟ๐˜ƒ๐—ถ๐—ฒ๐˜„ ๐˜„๐—ถ๐˜๐—ต ๐—ง๐—ต๐—ฒ๐˜€๐—ฒ ๐Ÿฏ๐Ÿฌ ๐— ๐—ผ๐˜€๐˜-๐—”๐˜€๐—ธ๐—ฒ๐—ฑ ๐—ค๐˜‚๐—ฒ๐˜€๐˜๐—ถ๐—ผ๐—ป๐˜€! ๐Ÿ˜ ๐Ÿคฆ๐Ÿปโ€โ™€๏ธStruggling with SQL interviews? Not anymore!๐Ÿ“ SQL interviews can be challenging, but preparation is the key to success. Whether youโ€™re aiming for a data analytics role or just brushing up, this resource has got your back!๐ŸŽŠ ๐‹๐ข๐ง๐ค๐Ÿ‘‡:- https://pdlink.in/4olhd6z Letโ€™s crack that interview together!โœ…๏ธ

The Only SQL You Actually Need For Your First Job (Data Analytics) The Learning Trap: What Most Beginners Fall Into When starting out, it's common to feel like you need to master every possible SQL concept. You binge YouTube videos, tutorials, and courses, yet still feel lost in interviews or when given a real dataset. Common traps: - Complex subqueries - Advanced CTEs - Recursive queries - 100+ tutorials watched - 0 practical experience Reality Check: What You'll Actually Use 75% of the Time Most data analytics roles (especially entry-level) require clarity, speed, and confidence with core SQL operations. Hereโ€™s what covers most daily work: 1. SELECT, FROM, WHERE โ€” The Foundation SELECT name, age FROM employees WHERE department = 'Finance'; This is how almost every query begins. Whether exploring a dataset or building a dashboard, these are always in use. 2. JOINs โ€” Combining Data From Multiple Tables SELECT e.name, d.department_name FROM employees e JOIN departments d ON e.department_id = d.id; Youโ€™ll often join tables like employee data with department, customer orders with payments, etc. 3. GROUP BY โ€” Summarizing Data SELECT department, COUNT(*) AS employee_count FROM employees GROUP BY department; Used to get summaries by categories like sales per region or users by plan. 4. ORDER BY โ€” Sorting Results SELECT name, salary FROM employees ORDER BY salary DESC; Helps sort output for dashboards or reports. 5. Aggregations โ€” Simple But Powerful Common functions: COUNT(), SUM(), AVG(), MIN(), MAX() SELECT AVG(salary) FROM employees WHERE department = 'IT'; Gives quick insights like average deal size or total revenue. 6. ROW_NUMBER() โ€” Adding Row Logic SELECT * FROM ( SELECT *, ROW_NUMBER() OVER(PARTITION BY customer_id ORDER BY order_date DESC) as rn FROM orders ) sub WHERE rn = 1; Used for deduplication, rankings, or selecting the latest record per group. Credits: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02 React โค๏ธ for more

๐Ÿณ ๐— ๐˜‚๐˜€๐˜-๐—ž๐—ป๐—ผ๐˜„ ๐—ฆ๐—ค๐—Ÿ ๐—–๐—ผ๐—ป๐—ฐ๐—ฒ๐—ฝ๐˜๐˜€ ๐—˜๐˜ƒ๐—ฒ๐—ฟ๐˜† ๐—”๐˜€๐—ฝ๐—ถ๐—ฟ๐—ถ๐—ป๐—ด ๐——๐—ฎ๐˜๐—ฎ ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜€๐˜ ๐—ฆ๐—ต๐—ผ๐˜‚๐—น๐—ฑ ๐— ๐—ฎ๐˜€๐˜๐—ฒ๐—ฟ๐Ÿ˜
๐Ÿณ ๐— ๐˜‚๐˜€๐˜-๐—ž๐—ป๐—ผ๐˜„ ๐—ฆ๐—ค๐—Ÿ ๐—–๐—ผ๐—ป๐—ฐ๐—ฒ๐—ฝ๐˜๐˜€ ๐—˜๐˜ƒ๐—ฒ๐—ฟ๐˜† ๐—”๐˜€๐—ฝ๐—ถ๐—ฟ๐—ถ๐—ป๐—ด ๐——๐—ฎ๐˜๐—ฎ ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜€๐˜ ๐—ฆ๐—ต๐—ผ๐˜‚๐—น๐—ฑ ๐— ๐—ฎ๐˜€๐˜๐—ฒ๐—ฟ๐Ÿ˜ If youโ€™re serious about becoming a data analyst, thereโ€™s no skipping SQL. Itโ€™s not just another technical skill โ€” itโ€™s the core language for data analytics.๐Ÿ“Š ๐‹๐ข๐ง๐ค๐Ÿ‘‡:- https://pdlink.in/44S3Xi5 This guide covers 7 key SQL concepts that every beginner must learnโœ…๏ธ

Machine Learning isn't easy! Itโ€™s the field that powers intelligent systems and predictive models. To truly master Machine Learning, focus on these key areas: 0. Understanding the Basics of Algorithms: Learn about linear regression, decision trees, and k-nearest neighbors to build a solid foundation. 1. Mastering Data Preprocessing: Clean, normalize, and handle missing data to prepare your datasets for training. 2. Learning Supervised Learning Techniques: Dive deep into classification and regression models, such as SVMs, random forests, and logistic regression. 3. Exploring Unsupervised Learning: Understand clustering techniques (K-means, hierarchical) and dimensionality reduction (PCA, t-SNE). 4. Mastering Model Evaluation: Use techniques like cross-validation, confusion matrices, ROC curves, and F1 scores to assess model performance. 5. Understanding Overfitting and Underfitting: Learn how to balance bias and variance to build robust models. 6. Optimizing Hyperparameters: Use grid search, random search, and Bayesian optimization to fine-tune your models for better performance. 7. Diving into Neural Networks and Deep Learning: Explore deep learning with frameworks like TensorFlow and PyTorch to create advanced models like CNNs and RNNs. 8. Working with Natural Language Processing (NLP): Master text data, sentiment analysis, and techniques like word embeddings and transformers. 9. Staying Updated with New Techniques: Machine learning evolves rapidlyโ€”keep up with emerging models, techniques, and research. Machine learning is about learning from data and improving models over time. ๐Ÿ’ก Embrace the challenges of building algorithms, experimenting with data, and solving complex problems. โณ With time, practice, and persistence, youโ€™ll develop the expertise to create systems that learn, predict, and adapt. Data Science & Machine Learning Resources: https://topmate.io/coding/914624 Credits: https://t.me/datasciencefun Like if you need similar content ๐Ÿ˜„๐Ÿ‘ Hope this helps you ๐Ÿ˜Š #datascience

๐—™๐—ฅ๐—˜๐—˜ ๐—ง๐—”๐—ง๐—” ๐——๐—ฎ๐˜๐—ฎ ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜๐—ถ๐—ฐ๐˜€ ๐—ฉ๐—ถ๐—ฟ๐˜๐˜‚๐—ฎ๐—น ๐—œ๐—ป๐˜๐—ฒ๐—ฟ๐—ป๐˜€๐—ต๐—ถ๐—ฝ ๐—ณ๐—ผ๐—ฟ ๐—•๐—ฒ๐—ด๐—ถ๐—ป๐—ป๐—ฒ๐—ฟ๐˜€ (๐—ช๐—ถ๐˜๐—ต ๐—–๐—ฒ๐—ฟ๏ฟฝ
๐—™๐—ฅ๐—˜๐—˜ ๐—ง๐—”๐—ง๐—” ๐——๐—ฎ๐˜๐—ฎ ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜๐—ถ๐—ฐ๐˜€ ๐—ฉ๐—ถ๐—ฟ๐˜๐˜‚๐—ฎ๐—น ๐—œ๐—ป๐˜๐—ฒ๐—ฟ๐—ป๐˜€๐—ต๐—ถ๐—ฝ ๐—ณ๐—ผ๐—ฟ ๐—•๐—ฒ๐—ด๐—ถ๐—ป๐—ป๐—ฒ๐—ฟ๐˜€ (๐—ช๐—ถ๐˜๐—ต ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ฒ)๐Ÿ˜ ๐ŸŽฏ Gain Real-World Data Analytics Experience with TATA โ€“ 100% Free!๐Ÿ“Šโœจ๏ธ Want to boost your resume and build real-world experience as a beginner? This free TATA Data Analytics Virtual Internship on Forage lets you step into the shoes of a data analyst โ€” no experience required!๐Ÿง‘โ€๐ŸŽ“๐Ÿ“Œ ๐‹๐ข๐ง๐ค๐Ÿ‘‡:- https://pdlink.in/3FyjDgp No application or selection process โ€” just sign up and start learning instantly!โœ…๏ธ

Python Roadmap for 2025 ๐Ÿ‘†
+3
Python Roadmap for 2025 ๐Ÿ‘†

๐Ÿš€๐—ง๐—ผ๐—ฝ ๐Ÿฏ ๐—™๐—ฟ๐—ฒ๐—ฒ ๐—š๐—ผ๐—ผ๐—ด๐—น๐—ฒ-๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฒ๐—ฑ ๐—ฃ๐˜†๐˜๐—ต๐—ผ๐—ป ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐Ÿฎ๐Ÿฌ๐Ÿฎ๐Ÿฑ๐Ÿ˜ Want to boost your tech career? L
๐Ÿš€๐—ง๐—ผ๐—ฝ ๐Ÿฏ ๐—™๐—ฟ๐—ฒ๐—ฒ ๐—š๐—ผ๐—ผ๐—ด๐—น๐—ฒ-๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฒ๐—ฑ ๐—ฃ๐˜†๐˜๐—ต๐—ผ๐—ป ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐Ÿฎ๐Ÿฌ๐Ÿฎ๐Ÿฑ๐Ÿ˜ Want to boost your tech career? Learn Python for FREE with Google-certified courses! Perfect for beginnersโ€”no expensive bootcamps needed. ๐Ÿ”ฅ Learn Python for AI, Data, Automation & More! ๐Ÿ“๐—ฆ๐˜๐—ฎ๐—ฟ๐˜ ๐—ก๐—ผ๐˜„๐Ÿ‘‡ https://pdlink.in/42okGqG โœ… Future You Will Thank You!

๐Ÿš€ Microsoft is offering some FREE courses ๐Ÿš€ 1๏ธโƒฃ AI for beginners Check this out ๐Ÿ‘‡ http://microsoft.github.io/AI-For-Beginners 2๏ธโƒฃ IOT Check this out ๐Ÿ‘‡ https://microsoft.github.io/IoT-For-Beginners 3๏ธโƒฃ Machine Learning Check this out๐Ÿ‘‡ http://microsoft.github.io/ML-For-Beginners/#/ 4๏ธโƒฃ Data Science Check this out๐Ÿ‘‡ http://microsoft.github.io/Data-Science-For-Beginners/#/ Free Coding Courses ๐Ÿ‘‡ https://t.me/programming_guide Few more courses โœ… ๐Ÿญ.๐——๐—ฎ๐˜๐—ฎ ๐—™๐˜‚๐—ป๐—ฑ๐—ฎ๐—บ๐—ฒ๐—ป๐˜๐—ฎ๐—น๐˜€ https://learn.microsoft.com/en-us/training/paths/azure-data-fundamentals-explore-non-relational-data/ ๐Ÿฎ.๐—ฆ๐—พ๐—น ๐—™๐˜‚๐—ป๐—ฑ๐—ฎ๐—บ๐—ฒ๐—ป๐˜๐—ฎ๐—น๐˜€ https://learn.microsoft.com/en-us/training/paths/azure-sql-fundamentals/ ๐Ÿฏ.๐—ฃ๐—ผ๐˜„๐—ฒ๐—ฟ ๐—•๐—œ https://learn.microsoft.com/en-us/training/paths/create-use-analvtics-reports-power-bi/ ๐Ÿฐ.๐—”๐˜‡๐˜‚๐—ฟ๐—ฒ ๐—ฐ๐—ผ๐˜€๐—บ๐—ผ๐˜€ ๐——๐—• https://learn.microsoft.com/en-us/training/paths/create-use-analytics-reports-power-bi/ ๐Ÿฑ.๐—”๐—œ ๐—ณ๐˜‚๐—ป๐—ฑ๐—ฎ๐—บ๐—ฒ๐—ป๐˜๐—ฎ๐—น๐˜€ https://learn.microsoft.com/en-us/training/paths/create-no-code-predictive-models-azure-machine-learning/

๐Ÿฒ ๐—™๐—ฟ๐—ฒ๐—ฒ ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐˜๐—ผ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป ๐˜๐—ต๐—ฒ ๐— ๐—ผ๐˜€๐˜ ๐—œ๐—ป-๐——๐—ฒ๐—บ๐—ฎ๐—ป๐—ฑ ๐—ง๐—ฒ๐—ฐ๐—ต ๐—ฆ๐—ธ๐—ถ๐—น๐—น๐˜€๐Ÿ˜ ๐Ÿš€ Want to future-proof
๐Ÿฒ ๐—™๐—ฟ๐—ฒ๐—ฒ ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐˜๐—ผ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป ๐˜๐—ต๐—ฒ ๐— ๐—ผ๐˜€๐˜ ๐—œ๐—ป-๐——๐—ฒ๐—บ๐—ฎ๐—ป๐—ฑ ๐—ง๐—ฒ๐—ฐ๐—ต ๐—ฆ๐—ธ๐—ถ๐—น๐—น๐˜€๐Ÿ˜ ๐Ÿš€ Want to future-proof your career without spending a single rupee?๐Ÿ’ต These 6 free online courses from top institutions like Google, Harvard, IBM, Stanford, and Cisco will help you master high-demand tech skills in 2025 โ€” from Data Analytics to Machine Learning๐Ÿ“Š๐Ÿง‘โ€๐Ÿ’ป ๐‹๐ข๐ง๐ค๐Ÿ‘‡:- https://pdlink.in/4fbDejW Each course is beginner-friendly, comes with certification, and helps you build your resume or switch careersโœ…๏ธ

Understanding Popular ML Algorithms: 1๏ธโƒฃ Linear Regression: Think of it as drawing a straight line through data points to predict future outcomes. 2๏ธโƒฃ Logistic Regression: Like a yes/no machine - it predicts the likelihood of something happening or not. 3๏ธโƒฃ Decision Trees: Imagine making decisions by answering yes/no questions, leading to a conclusion. 4๏ธโƒฃ Random Forest: It's like a group of decision trees working together, making more accurate predictions. 5๏ธโƒฃ Support Vector Machines (SVM): Visualize drawing lines to separate different types of things, like cats and dogs. 6๏ธโƒฃ K-Nearest Neighbors (KNN): Friends sticking together - if most of your friends like something, chances are you'll like it too! 7๏ธโƒฃ Neural Networks: Inspired by the brain, they learn patterns from examples - perfect for recognizing faces or understanding speech. 8๏ธโƒฃ K-Means Clustering: Imagine sorting your socks by color without knowing how many colors there are - it groups similar things. 9๏ธโƒฃ Principal Component Analysis (PCA): Simplifies complex data by focusing on what's important, like summarizing a long story with just a few key points. Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624 ENJOY LEARNING ๐Ÿ‘๐Ÿ‘

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Are you looking to become a machine learning engineer? The algorithm brought you to the right place! ๐Ÿ“Œ I created a free and comprehensive roadmap. Let's go through this thread and explore what you need to know to become an expert machine learning engineer: Math & Statistics Just like most other data roles, machine learning engineering starts with strong foundations from math, precisely linear algebra, probability and statistics. Here are the probability units you will need to focus on: Basic probability concepts statistics Inferential statistics Regression analysis Experimental design and A/B testing Bayesian statistics Calculus Linear algebra Python: You can choose Python, R, Julia, or any other language, but Python is the most versatile and flexible language for machine learning. Variables, data types, and basic operations Control flow statements (e.g., if-else, loops) Functions and modules Error handling and exceptions Basic data structures (e.g., lists, dictionaries, tuples) Object-oriented programming concepts Basic work with APIs Detailed data structures and algorithmic thinking Machine Learning Prerequisites: Exploratory Data Analysis (EDA) with NumPy and Pandas Basic data visualization techniques to visualize the variables and features. Feature extraction Feature engineering Different types of encoding data Machine Learning Fundamentals Using scikit-learn library in combination with other Python libraries for: Supervised Learning: (Linear Regression, K-Nearest Neighbors, Decision Trees) Unsupervised Learning: (K-Means Clustering, Principal Component Analysis, Hierarchical Clustering) Reinforcement Learning: (Q-Learning, Deep Q Network, Policy Gradients) Solving two types of problems: Regression Classification Neural Networks: Neural networks are like computer brains that learn from examples, made up of layers of "neurons" that handle data. They learn without explicit instructions. Types of Neural Networks: Feedforward Neural Networks: Simplest form, with straight connections and no loops. Convolutional Neural Networks (CNNs): Great for images, learning visual patterns. Recurrent Neural Networks (RNNs): Good for sequences like text or time series, because they remember past information. In Python, itโ€™s the best to use TensorFlow and Keras libraries, as well as PyTorch, for deeper and more complex neural network systems. Deep Learning: Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Convolutional Neural Networks (CNNs) Recurrent Neural Networks (RNNs) Long Short-Term Memory Networks (LSTMs) Generative Adversarial Networks (GANs) Autoencoders Deep Belief Networks (DBNs) Transformer Models Machine Learning Project Deployment Machine learning engineers should also be able to dive into MLOps and project deployment. Here are the things that you should be familiar or skilled at: Version Control for Data and Models Automated Testing and Continuous Integration (CI) Continuous Delivery and Deployment (CD) Monitoring and Logging Experiment Tracking and Management Feature Stores Data Pipeline and Workflow Orchestration Infrastructure as Code (IaC) Model Serving and APIs Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624 Credits: https://t.me/datasciencefun Like if you need similar content ๐Ÿ˜„๐Ÿ‘ Hope this helps you ๐Ÿ˜Š

๐Ÿฏ ๐—™๐—ฟ๐—ฒ๐—ฒ ๐—ฆ๐—ค๐—Ÿ ๐—ฌ๐—ผ๐˜‚๐—ง๐˜‚๐—ฏ๐—ฒ ๐—ฃ๐—น๐—ฎ๐˜†๐—น๐—ถ๐˜€๐˜๐˜€ ๐—ง๐—ต๐—ฎ๐˜ ๐—ช๐—ถ๐—น๐—น ๐— ๐—ฎ๐—ธ๐—ฒ ๐—ฌ๐—ผ๐˜‚ ๐—ฎ ๐—ค๐˜‚๐—ฒ๐—ฟ๐˜† ๐—ฃ๐—ฟ๐—ผ ๐—ถ๐—ป ๐Ÿฎ๐Ÿฌ๐Ÿฎ๐Ÿฑ๐Ÿ˜ S
๐Ÿฏ ๐—™๐—ฟ๐—ฒ๐—ฒ ๐—ฆ๐—ค๐—Ÿ ๐—ฌ๐—ผ๐˜‚๐—ง๐˜‚๐—ฏ๐—ฒ ๐—ฃ๐—น๐—ฎ๐˜†๐—น๐—ถ๐˜€๐˜๐˜€ ๐—ง๐—ต๐—ฎ๐˜ ๐—ช๐—ถ๐—น๐—น ๐— ๐—ฎ๐—ธ๐—ฒ ๐—ฌ๐—ผ๐˜‚ ๐—ฎ ๐—ค๐˜‚๐—ฒ๐—ฟ๐˜† ๐—ฃ๐—ฟ๐—ผ ๐—ถ๐—ป ๐Ÿฎ๐Ÿฌ๐Ÿฎ๐Ÿฑ๐Ÿ˜ Still stuck Googling โ€œWhat is SQL?โ€ every time you start a new project?๐Ÿ’ต Youโ€™re not alone. Many beginners bounce between tutorials without ever feeling confident writing SQL queries on their own.๐Ÿ‘จโ€๐Ÿ’ปโœจ๏ธ ๐‹๐ข๐ง๐ค๐Ÿ‘‡:- https://pdlink.in/4f1F6LU Letโ€™s dive into the ones that are actually worth your timeโœ…๏ธ

Writing Python Lists
Writing Python Lists

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๐Ÿฑ ๐—ฅ๐—ฒ๐—ฎ๐—น-๐—ช๐—ผ๐—ฟ๐—น๐—ฑ ๐—ง๐—ฒ๐—ฐ๐—ต ๐—ฃ๐—ฟ๐—ผ๐—ท๐—ฒ๐—ฐ๐˜๐˜€ ๐˜๐—ผ ๐—•๐˜‚๐—ถ๐—น๐—ฑ ๐—ฌ๐—ผ๐˜‚๐—ฟ ๐—ฅ๐—ฒ๐˜€๐˜‚๐—บ๐—ฒ โ€“ ๐—ช๐—ถ๐˜๐—ต ๐—™๐˜‚๐—น๐—น ๐—ง๐˜‚๐˜๐—ผ๐—ฟ๐—ถ๐—ฎ๐—น๐˜€!๐Ÿ˜ Are you ready to build real-world tech projects that donโ€™t just look good on your resume, but actually teach you practical, job-ready skills?๐Ÿง‘โ€๐Ÿ’ป๐Ÿ“Œ Hereโ€™s a curated list of 5 high-value development tutorials โ€” covering everything from full-stack development and real-time chat apps to AI form builders and reinforcement learningโœจ๏ธ๐Ÿ’ป ๐‹๐ข๐ง๐ค๐Ÿ‘‡:- https://pdlink.in/3UtCSLO Theyโ€™re real, portfolio-worthy projects you can start todayโœ…๏ธ