Data Science Roadmap.pdf
15.5 MB
π· Comprehensive Data Science Roadmap Notes
β This roadmap is exactly the secret recipe you need to get out of confusion and know how to step-by-step prepare yourself for the job market.
π‘ From mastering Python and SQL to cleaning data and working with cloud tools, which are prerequisites for any project.
π How to extract real analysis reports and strategies from raw data using statistics and visualization tools.
π You will learn everything from machine learning and advanced algorithms to precise model evaluation.
π Get familiar with neural networks, generative artificial intelligence, and language models to have a voice in today's modern world.
π§ How to build real projects and portfolios that are exactly what hiring managers and big companies are looking for.
π #DataScience #DataScience #pytorch #python #Roadmap
https://t.iss.one/CodeProgrammer
β This roadmap is exactly the secret recipe you need to get out of confusion and know how to step-by-step prepare yourself for the job market.
π‘ From mastering Python and SQL to cleaning data and working with cloud tools, which are prerequisites for any project.
π How to extract real analysis reports and strategies from raw data using statistics and visualization tools.
π You will learn everything from machine learning and advanced algorithms to precise model evaluation.
π Get familiar with neural networks, generative artificial intelligence, and language models to have a voice in today's modern world.
π§ How to build real projects and portfolios that are exactly what hiring managers and big companies are looking for.
π #DataScience #DataScience #pytorch #python #Roadmap
https://t.iss.one/CodeProgrammer
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γ»Free AI material and support toolsοΌhttps://bit.ly/46qvpDX
π Become Part of Our IT Learning Circle! resources and support:
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If you want to understand AI not through "vacuum" courses, but through real open-source projects - here's a top list of repos that really lead you from the basics to practice:
1) Karpathy β Neural Networks: Zero to Hero
The most understandable introduction to neural networks and backprop "in layman's terms"
https://github.com/karpathy/nn-zero-to-hero
2) Hugging Face Transformers
The main library of modern NLP/LLM: models, tokenizers, fine-tuning
https://github.com/huggingface/transformers
3) FastAI β Fastbook
Practical DL training through projects and experiments
https://github.com/fastai/fastbook
4) Made With ML
ML as an engineering system: pipelines, production, deployment, monitoring
https://github.com/GokuMohandas/Made-With-ML
5) Machine Learning System Design (Chip Huyen)
How to build ML systems in real business: data, metrics, infrastructure
https://github.com/chiphuyen/machine-learning-systems-design
6) Awesome Generative AI Guide
A collection of materials on GenAI: from basics to practice
https://github.com/aishwaryanr/awesome-generative-ai-guide
7) Dive into Deep Learning (D2L)
One of the best books on DL + code + assignments
https://github.com/d2l-ai/d2l-en
Save it for yourself - this is a base on which you can really grow into an ML/LLM engineer.
#Python #datascience #DataAnalysis #MachineLearning #AI #DeepLearning #LLMS
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A course from Microsoft with 21 lessons covering the basics of creating applications based on generative AI. Each lesson includes theory and practical examples in Python and TypeScript, allowing you to learn at a comfortable pace.
π Key features:
- 21 lessons on generative #AI
- Support for Python and TypeScript
- Lessons with theory and practical tasks
- Additional resources for in-depth study
- Multilingual support
π GitHub: https://github.com/microsoft/generative-ai-for-beginners
#python #LLMS #generative_Ai
https://t.iss.one/CodeProgrammer
A course from Microsoft with 21 lessons covering the basics of creating applications based on generative AI. Each lesson includes theory and practical examples in Python and TypeScript, allowing you to learn at a comfortable pace.
π Key features:
- 21 lessons on generative #AI
- Support for Python and TypeScript
- Lessons with theory and practical tasks
- Additional resources for in-depth study
- Multilingual support
π GitHub: https://github.com/microsoft/generative-ai-for-beginners
#python #LLMS #generative_Ai
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Pandas vs. Polars: A Complete Comparison of Syntax, Speed, and Memory
Need help choosing the right #Python dataframe library? This article compares #Pandas and #Polars to help you decide.
If you've been working with data in Python, you've almost certainly used pandas. It's been the go-to library for data manipulation for over a decade. But recently, Polars has been gaining serious traction. Polars promises to be faster, more memory-efficient, and more intuitive than pandas. But is it worth learning? And how different is it really?
In this article, we'll compare pandas and Polars side-by-side. You'll see performance benchmarks, and learn the syntax differences. By the end, you'll be able to make an informed decision for your next data project.
Read: https://www.kdnuggets.com/pandas-vs-polars-a-complete-comparison-of-syntax-speed-and-memory
https://t.iss.one/CodeProgrammer πΊ
Need help choosing the right #Python dataframe library? This article compares #Pandas and #Polars to help you decide.
If you've been working with data in Python, you've almost certainly used pandas. It's been the go-to library for data manipulation for over a decade. But recently, Polars has been gaining serious traction. Polars promises to be faster, more memory-efficient, and more intuitive than pandas. But is it worth learning? And how different is it really?
In this article, we'll compare pandas and Polars side-by-side. You'll see performance benchmarks, and learn the syntax differences. By the end, you'll be able to make an informed decision for your next data project.
Read: https://www.kdnuggets.com/pandas-vs-polars-a-complete-comparison-of-syntax-speed-and-memory
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Forwarded from Code With Python
Python Cheat Sheet: Beginner to Expert Guide
This #Python cheat sheet covers basics to advanced concepts, regex, list slicing, loops and more. Perfect for quick reference and enhancing your coding skills.
Read: https://www.almabetter.com/bytes/cheat-sheet/python
https://t.iss.one/DataScience4βοΈ
This #Python cheat sheet covers basics to advanced concepts, regex, list slicing, loops and more. Perfect for quick reference and enhancing your coding skills.
Read: https://www.almabetter.com/bytes/cheat-sheet/python
https://t.iss.one/DataScience4
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A full-fledged educational course has been published on the university's website: 24 lectures, practical assignments, homework, and a collection of materials for self-study.
The program includes modern neural network architectures, generative models, transformers, inference, and other key topics.
β‘οΈ Link to the course
tags: #Python #DataScience #DeepLearning #AI
A full-fledged educational course has been published on the university's website: 24 lectures, practical assignments, homework, and a collection of materials for self-study.
The program includes modern neural network architectures, generative models, transformers, inference, and other key topics.
β‘οΈ Link to the course
tags: #Python #DataScience #DeepLearning #AI
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Forwarded from Code With Python
The Ultimate 2026 Python Learning Roadmap: From Beginner to Expert
Start learning #Python in 2026 with a clear, structured #roadmap that takes you from beginner to expert. Build real-world skills through hands-on projects, master essential libraries, and prepare for in-demand careers in data science, web development, and #AI
Start: https://www.coursera.org/resources/python-learning-roadmap
Start learning #Python in 2026 with a clear, structured #roadmap that takes you from beginner to expert. Build real-world skills through hands-on projects, master essential libraries, and prepare for in-demand careers in data science, web development, and #AI
Start: https://www.coursera.org/resources/python-learning-roadmap
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Machine Learning in python.pdf
1 MB
Machine Learning in Python (Course Notes)
I just went through an amazing resource on #MachineLearning in #Python by 365 Data Science, and I had to share the key takeaways with you!
Hereβs what youβll learn:
π Linear Regression - The foundation of predictive modeling
π Logistic Regression - Predicting probabilities and classifications
π Clustering (K-Means, Hierarchical) - Making sense of unstructured data
π Overfitting vs. Underfitting - The balancing act every ML engineer must master
π OLS, R-squared, F-test - Key metrics to evaluate your models
https://t.iss.one/CodeProgrammer || Shareπ and Like π
I just went through an amazing resource on #MachineLearning in #Python by 365 Data Science, and I had to share the key takeaways with you!
Hereβs what youβll learn:
π Linear Regression - The foundation of predictive modeling
π Logistic Regression - Predicting probabilities and classifications
π Clustering (K-Means, Hierarchical) - Making sense of unstructured data
π Overfitting vs. Underfitting - The balancing act every ML engineer must master
π OLS, R-squared, F-test - Key metrics to evaluate your models
https://t.iss.one/CodeProgrammer || Share
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Python Tip: Operator Overloading
This is a very important concept in Python.
π https://t.iss.one/Python53
This is a very important concept in Python.
Have you ever wondered how #Python understands what the + operator means? For numbers, it's addition; for strings, it's concatenation; for lists, it's union. This is operator overloading in action.ο»Ώ
Operator overloading means defining special behavior for operators (+, -, *, ==, etc.) in your user-defined classes. You determine how these operators should work with your objects.
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