#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #SupervisedLearning #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming
https://t.me/CodeProgrammer🖥
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💰 Price:$20 or ₹1500
✅ 1 Year You.com Pro on Your Mail
💰 Price:$25 or ₹1800
💰Combo Offer:40$
Original Price:200$
How I activate ?
I activate account through voucher codes on your mail for 1 year.
💡 Features Included
✅Advanced AI Models:
• DeepResearch
•GPT-4o, o1, o3 mini(High)
• Deepseek r1[USA Hosted Uncensored]
• Llama 3.1
•Claude 3.5 Sonnet, Claude 3.5 Haiku
•Grok-2(Grok 3 coming too confirmed by its CEO)
•FILE ANALYSIS
•PRO SEARCH
✅Image Generation 🎥
•Flux, DALL-E 3
•Playground v3, Stable Diffusion XL
✔️ What You Get
•1 year of full access.
•A 12-month warranty is included.
💨 This post will be deleted/removed after 24 hours so save my username or contact immediately.
💰 Payment Method: Crypto[LTC or USDT] or UPI
✅ For Inquiry/Purchase DM: @AiChatBoss
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Link: https://amankharwal.medium.com/130-python-projects-with-source-code-61f498591bb
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https://t.me/CodeProgrammer🖥
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Find your location on Map using Python
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https://t.me/CodeProgrammer🖥
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📁 I've brought you 10 of the best portfolios from data science professionals, each of whom has followed a unique path! Check out these 10 and get inspired to build a strong portfolio of your own!👇
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https://t.me/CodeProgrammer🧠
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Generative vs. discriminative models in ML:
Generative models:
- learn the distribution so they can generate new samples.
- possess discriminative properties, we can use them for classification.
Discriminative models don't have generative properties.
Generative models:
- learn the distribution so they can generate new samples.
- possess discriminative properties, we can use them for classification.
Discriminative models don't have generative properties.
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https://t.me/CodeProgrammer
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Pen and Paper Exercises in MachineLearning
Free 211-page PDF: arxiv.org/abs/2206.13446
GitHub: https://github.com/michaelgutmann/ml-pen-and-paper-exercises
Free 211-page PDF: arxiv.org/abs/2206.13446
GitHub: https://github.com/michaelgutmann/ml-pen-and-paper-exercises
#DataScientist #AI #ML #DataScience #LLM #PYTHON #PYTORCH #DEEPLEARNING #GenerativeAI
https://t.me/CodeProgrammer
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It was a challenge - a marathon 300$ to 30.000$ on trading, together with Lisa!
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TensorFlow v2.0 Cheat Sheet
#TensorFlow is an open-source software library for highperformance numerical computation. Its flexible architecture enables to easily deploy computation across a variety of platforms (CPUs, GPUs, and TPUs), as well as mobile and edge devices, desktops, and clusters of servers. TensorFlow comes with strong support for machine learning and deep learning.
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https://t.me/CodeProgrammer
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Machine Learning Glossary
Brief visual explanations of machine learning concepts with diagrams, code examples and links to resources for learning more.
Link: https://ml-cheatsheet.readthedocs.io/en/latest/index.html
Brief visual explanations of machine learning concepts with diagrams, code examples and links to resources for learning more.
Link: https://ml-cheatsheet.readthedocs.io/en/latest/index.html
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https://t.me/CodeProgrammer
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The program covers topics of #NLP, #CV, #LLM and the use of technology in medicine, offering a full cycle of training - from theory to practical classes using current versions of libraries.
The course is designed even for beginners: if you know how to take derivatives and multiply matrices, everything else will be explained in the process.
The lectures are released for free on YouTube and the #MIT platform on Mondays, with the first one already available
.
All slides, #code and additional materials can be found at the link provided.
📌 Fresh lecture : https://youtu.be/alfdI7S6wCY?si=6682DD2LlFwmghew
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https://t.me/CodeProgrammer✅
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Numpy @CodeProgrammer.pdf
813.2 KB
👨🏻💻 For the past few days, I've been busy preparing this comprehensive tutorial on the NumPy library for data science, trying to cover all the tips and tricks of this library.
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https://t.me/CodeProgrammer✅
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