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pandas for Data Science - Guide
In this learning path, you’ll get started with pandas and get to know the ins and outs of how you can use it to analyze data with Python.
pandas is a game-changer for #datascience and analytics, particularly if you came to #Python because you were searching for something more powerful than #Excel and #VBA. #pandas uses fast, flexible, and expressive data structures designed to make working with relational or labeled data both easy and intuitive.
Read: https://realpython.com/learning-paths/pandas-data-science/
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In this learning path, you’ll get started with pandas and get to know the ins and outs of how you can use it to analyze data with Python.
pandas is a game-changer for #datascience and analytics, particularly if you came to #Python because you were searching for something more powerful than #Excel and #VBA. #pandas uses fast, flexible, and expressive data structures designed to make working with relational or labeled data both easy and intuitive.
Read: https://realpython.com/learning-paths/pandas-data-science/
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The Hundred-Page Language Models Book
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Generative AI for beginners by Microsoft
21 Lessons teaching everything you need to know to start building Generative AI applications
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Understanding Probability Distributions for Machine Learning with Python
In machine learning, probability distributions play a fundamental role for various reasons: modeling uncertainty of information and #data, applying optimization processes with stochastic settings, and performing inference processes, to name a few. Therefore, understanding the role and uses of probability distributions in machine learning is essential for designing robust machine learning models, choosing the right #algorithms, and interpreting outputs of a probabilistic nature, especially when building #models with #machinelearning-friendly programming languages like #Python.
This article unveils key #probability distributions relevant to machine learning, explores their applications in different machine learning tasks, and provides practical Python implementations to help practitioners apply these concepts effectively. A basic knowledge of the most common probability distributions is recommended to make the most of this reading.
Read Free: https://machinelearningmastery.com/understanding-probability-distributions-machine-learning-python/
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In machine learning, probability distributions play a fundamental role for various reasons: modeling uncertainty of information and #data, applying optimization processes with stochastic settings, and performing inference processes, to name a few. Therefore, understanding the role and uses of probability distributions in machine learning is essential for designing robust machine learning models, choosing the right #algorithms, and interpreting outputs of a probabilistic nature, especially when building #models with #machinelearning-friendly programming languages like #Python.
This article unveils key #probability distributions relevant to machine learning, explores their applications in different machine learning tasks, and provides practical Python implementations to help practitioners apply these concepts effectively. A basic knowledge of the most common probability distributions is recommended to make the most of this reading.
Read Free: https://machinelearningmastery.com/understanding-probability-distributions-machine-learning-python/
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#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
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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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Find your location on Map using Python
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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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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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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
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It was a challenge - a marathon 300$ to 30.000$ on trading, together with Lisa!
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