Data Science Machine Learning Data Analysis
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This channel is for Programmers, Coders, Software Engineers.

1- Data Science
2- Machine Learning
3- Data Visualization
4- Artificial Intelligence
5- Data Analysis
6- Statistics
7- Deep Learning

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Using NotebookLM as Your Machine Learning Study Guide

Introducing NotebookLM
Learning machine learning can be challenging. There’s a lot of theory, math, and code to understand, while keeping track of concepts from different sources can get overwhelming. That’s where NotebookLM comes in handy.

If you haven’t heard, NotebookLM is a digital notebook powered by AI. It helps you study smarter by turning your materials into a personalized tutor. You can upload research papers, notes, and textbooks, and then ask questions directly based on those documents. It gives answers based on the uploaded sources.

With NotebookLM, you don’t need to search the entire internet, as it narrows the focus to your own study content. This makes it easier to grasp topics like supervised learning, neural networks, and model evaluation.

In this article, you’ll learn how to use NotebookLM to support your machine learning journey.

Enjoy: https://machinelearningmastery.com/using-notebooklm-as-your-machine-learning-study-guide/
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Encoders and Decoders in Transformer Models

Transformer models have revolutionized natural language processing (NLP) with their powerful architecture. While the original transformer paper introduced a full encoder-decoder model, variations of this architecture have emerged to serve different purposes. In this article, we will explore the different types of transformer models and their applications.

Let’s get started.
This article is divided into three parts; they are:

Full Transformer Models: Encoder-Decoder Architecture
Encoder-Only Models
Decoder-Only Models

Enjoy:
https://machinelearningmastery.com/encoders-and-decoders-in-transformer-models/
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deep learning book.pdf
14.5 MB
A beautiful booklet for learning deep learning in a smooth and concise way without diving into the world of complexity.

I highly recommend reading this enjoyable booklet.

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python_basics.pdf
212.3 KB
🚀 Master Python with Ease!

I've just compiled a set of clean and powerful Python Cheat Sheets to help beginners and intermediates speed up their coding workflow.

Whether you're brushing up on the basics or diving into data science, these sheets will save you time and boost your productivity.

📌 Topics Covered:
Python Basics
Jupyter Notebook Tips
Importing Libraries
NumPy Essentials
Pandas Overview

Perfect for students, developers, and anyone looking to keep essential Python knowledge at their fingertips.

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😀 Introduction to Machine Learning – Laurent Younes

Looking for a clear and concise introduction to machine learning? This book by Laurent Younes provides a solid foundation in ML concepts, from theory to practical applications.

Perfect for students, researchers, and enthusiasts aiming to build a strong understanding of the core principles behind modern machine learning.

📄 Read the Book (PDF)
🔗 Shared via @DataScinceM

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Polars.pdf
391.5 KB
📖 A comprehensive cheat sheet for working with Polars


🌟 Have you ever worked with pandas and thought that was the fastest way? I thought the same thing until I worked with Polars.

✏️ This cheat sheet explains everything about Polars in a concise and simple way. Not just theory! But also a bunch of real examples, practical experience, and projects that will really help you in the real world.

🐻‍❄️ Polars Cheat Sheet
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📖 Doc

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This channels is for Programmers, Coders, Software Engineers.

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5️⃣ Data Analysis
6️⃣ Statistics
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🥇 40+ Real and Free Data Science Projects

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🐍Looking to get started with Deep Learning using PyTorch?

This well-structured GitHub repository is a goldmine for beginners who want to learn PyTorch with hands-on examples and clear explanations📖.

🗂 What’s Inside?
🈂 Jupyter Notebooks with interactive code.
🧠 Step-by-step tutorials on Tensors, Autograd, and Neural Networks.
🖼 Real-world mini-projects like image classification.
Practical guides on using GPU with PyTorch.
Beginner-friendly but also great for revision.


💡If you're serious about learning AI, this is one of the best free resources to kick off your journey🤝.

🖥 GitHub

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7.7 MB
1. Master the fundamentals of Statistics

Understand probability, distributions, and hypothesis testing

Differentiate between descriptive vs inferential statistics

Learn various sampling techniques

2. Get hands-on with Python & SQL

Work with data structures, pandas, numpy, and matplotlib

Practice writing optimized SQL queries

Master joins, filters, groupings, and window functions

3. Build real-world projects

Construct end-to-end data pipelines

Develop predictive models with machine learning

Create business-focused dashboards

4. Practice case study interviews

Learn to break down ambiguous business problems

Ask clarifying questions to gather requirements

Think aloud and structure your answers logically

5. Mock interviews with feedback

Use platforms like Pramp or connect with peers

Record and review your answers for improvement

Gather feedback on your explanation and presence

6. Revise machine learning concepts

Understand supervised vs unsupervised learning

Grasp overfitting, underfitting, and bias-variance tradeoff

Know how to evaluate models (precision, recall, F1-score, AUC, etc.)

7. Brush up on system design (if applicable)

Learn how to design scalable data pipelines

Compare real-time vs batch processing

Familiarize with tools: Apache Spark, Kafka, Airflow

8. Strengthen storytelling with data

Apply the STAR method in behavioral questions

Simplify complex technical topics

Emphasize business impact and insight-driven decisions

9. Customize your resume and portfolio

Tailor your resume for each job role

Include links to projects or GitHub profiles

Match your skills to job descriptions

10. Stay consistent and track progress

Set clear weekly goals

Monitor covered topics and completed tasks

Reflect regularly and adapt your plan as needed


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