Data Science Machine Learning Data Analysis Books

Complete Roadmap to Become a Data Scientist

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how transformers remember facts

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"Mathematical Foundations of Machine Learning"

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The Big Book of Large Language Models by Damien Benveniste

✅ Chapters:
1⃣ Introduction

🔢 Language Models Before Transformers

🔢 Attention Is All You Need: The Original Transformer Architecture

🔢 A More Modern Approach To The Transformer Architecture

🔢 Multi-modal Large Language Models

🔢 Transformers Beyond Language Models

🔢 Non-Transformer Language Models

🔢 How LLMs Generate Text

🔢 From Words To Tokens

1⃣0⃣ Training LLMs to Follow Instructions

1⃣1⃣ Scaling Model Training

1⃣🔢 Fine-Tuning LLMs

1⃣🔢 Deploying LLMs

Read it: https://book.theaiedge.io/

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26. Neural Networks and Deep Learning
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27. Supervised Machine Learning: Regression and Classification
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Embark on an exciting journey through the intricate world of Artificial Intelligence with our comprehensive learning map! ✅

1⃣ Artificial Intelligence (AI)
Dive into the vast universe of AI, where machines learn to perform tasks that typically require human intelligence. From Reinforcement Learning to Augmented Programming, this broad circle encompasses a wide array of techniques and applications. Whether you're interested in Speech Recognition or Algorithm Building, this is your starting point for understanding how machines can mimic human cognition. #AI #MachineIntelligence

🔢 Machine Learning (ML)
As we move inward, explore the fascinating realm of Machine Learning, a subset of AI focused on developing algorithms that enable machines to learn from data. Discover the power of Supervised and Unsupervised Learning, K-Means clustering, and Hypothesis Testing. This circle will equip you with the skills needed to analyze data and build predictive models. #MachineLearning #DataScience

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Next, delve into Neural Networks, computer models designed to simulate the workings of the human brain. These networks are used in various applications, from image recognition to natural language processing. Learn about Backpropagation, Feed Forward networks, and Support Vector Machines. This circle will provide you with the foundation to develop complex models that can solve real-world problems. #NeuralNetworks #DeepLearningBasics

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In the narrower circle, discover Deep Learning, an advanced branch of ML that uses multi-layered neural networks to tackle complex challenges. Explore Long Short-Term Memory (LSTM) networks, Transformers, and Auto Encoders. These techniques are at the forefront of modern AI applications like machine translation and medical diagnosis. Join us to master these cutting-edge technologies. #DeepLearning #AdvancedAI

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Finally, in the smallest and most specialized circle, uncover Generative AI, which focuses on creating new and innovative content using AI. Dive into Generative Adversarial Networks (GANs), Large Language Models (LLM), and Transfer Learning. This circle will empower you to generate creative content such as images and text using AI. #GenerativeAI #CreativeTech

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### Hugging Face Transformers: Unlock the Power of Open-Source AI in Python

Discover the limitless potential of Hugging Face Transformers, a robust Python library that empowers developers and data scientists to harness thousands of pretrained, open-source AI models. These state-of-the-art models are designed for a wide array of tasks across various modalities, including natural language processing (NLP), computer vision, audio processing, and multimodal learning.

#### Why Choose Hugging Face Transformers?

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#### Versatile Applications

Whether you're working on text classification, sentiment analysis, image recognition, speech-to-text conversion, or any other AI-driven task, Hugging Face Transformers provides the tools you need to succeed. The library's extensive collection of models ensures that you have access to cutting-edge technology without the need for extensive training resources.

#### Get Started Today!

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ChatGPT Cheat Sheet for Business - DataCamp

Unlock the full potential of AI with our comprehensive ChatGPT Cheat Sheet for Business! Tailored specifically for professionals and entrepreneurs, this guide offers actionable insights on leveraging ChatGPT to streamline workflows, enhance customer interactions, and drive business growth. Whether you're a marketing specialist, project manager, or CEO, this cheat sheet is your go-to resource for mastering conversational AI.

From crafting compelling content to automating routine tasks, learn how to harness the power of ChatGPT in real-world business scenarios. With clear examples and step-by-step instructions, you’ll be able to integrate ChatGPT seamlessly into your operations, improving efficiency and innovation.

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Title: ChatGPT Cheat Sheet for Data Science (2025)
Source: DataCamp

Description:
This comprehensive cheat sheet serves as an essential guide for leveraging ChatGPT in data science workflows. Designed for both beginners and seasoned practitioners, it provides actionable prompts, code examples, and best practices to streamline tasks such as data generation, analysis, modeling, and automation. Key features include:
- Code Generation: Scripts for creating sample datasets in Python using Pandas and NumPy (e.g., generating tables with primary keys, names, ages, and salaries) .
- Data Analysis: Techniques for exploratory data analysis (EDA), hypothesis testing, and predictive modeling, including visualization recommendations (bar charts, line graphs) and statistical methods .
- Machine Learning: Guidance on algorithm selection, hyperparameter tuning, and model interpretation, with examples tailored for Python and SQL .
- NLP Applications: Tools for text classification, sentiment analysis, and named entity recognition, leveraging ChatGPT’s natural language processing capabilities .
- Workflow Automation: Strategies for automating repetitive tasks like data cleaning (handling duplicates, missing values) and report generation .

The guide also addresses ChatGPT’s limitations, such as potential biases and hallucinations, while emphasizing best practices for iterative prompting and verification . Updated for 2025, it integrates the latest advancements in AI-assisted data science, making it a must-have resource for efficient, conversational-driven analytics.

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📚 Think Stats, 3rd Edition: Master Probability & Statistics with Python! 🐍

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- Real-World Case Studies: Analyze datasets from the National Institutes of Health and more. Learn by doing, not just theorizing!
- Jupyter Notebooks Included: Every chapter comes with interactive notebooks—run code, tweak examples, and solve exercises seamlessly. Ideal for Colab or local setups!
- Cutting-Edge Topics: Explore regression, time series analysis, survival analysis, and Bayesian methods. Level up your data science toolkit!
- Free & Open Source: Licensed under Creative Commons (CC BY-NC-SA). Download the PDF, fork the GitHub repo, and start learning today!

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