A Popular Interview Question: Discriminative vs. Generative Models

More Details: https://blog.dailydoseofds.com/p/a-popular-interview-question-discriminative

📂 Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses

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Convert PDF to docx using Python

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📹 3blue1brown presented the shortest and most understandable lecture on neural networks!

In the new episode, he talks about the mechanism of attention and transformers. The lecture has become even more concise and exciting!

Ideal for absolute beginners and even those who are far from technical.

The author managed to explain the key aspects of the neural network in just 9 minutes using bright graphics and simple examples.

📌 Original

📂 Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Transformer

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📕 Python Basics Made Simple!

📷 Course: AI Python for Beginners

👨‍💻 Instructor: Andrew Ng

In the #AIPythonforBeginners course series you'll learn how to identify strings, integers, and floats with the type() function, and build a solid Python foundation for your AI journey.

Enroll Free: https://learn.deeplearning.ai/courses/ai-python-for-beginners

📂 Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Transformer

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💠 All free Kaggle courses for data science
📁 Along with the course completion certificate

✅ Python ⬅️ link

✅ An introduction to machine learning ⬅️ link

✅ Pandas ⬅️ link

✅ Medium machine learning ⬅️ link

✅ Data visualization ⬅️ link

✅ Feature engineering ⬅️ link

✅ An introduction to the SQL language ⬅️ link

✅ Advanced SQL language ⬅️ link

✅ An introduction to deep learning ⬅️ link

✅ Computer vision ⬅️ link

✅ Time series ⬅️ link

✅ Data cleanup ⬅️ link

✅ Geographical analysis ⬅️ link

✅ Explainability of machine learning ⬅️ link

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Python List Methods clearly Explained

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🐱 7 of the best GitHub repos
✅ To enter the world of data analysis and data science


1⃣ 100 Days of ML Code repo

✍️ A hundred-day program for learning and practicing machine learning coding.


🔢 Awesome Data Science repo

✍️ A curated list of great data science resources such as books, software, and tools.


🔢 Data Science for Beginners repo

✍️ A repository from Microsoft that has a 10-week course with 20 lessons for beginners. Each lesson includes videos, quizzes, challenges and more.


🔢 Data Science Interviews Repo

✍️ A repository of questions and answers for science job interviews.


🔢 ML Technical Interviews repo

✍️ A good guide for machine learning and artificial intelligence technical interviews.


🔢 ML Interviews repo

✍️ A repository containing machine learning interview questions from basic topics to complex topics such as neural networks and reinforcement learning.


🔢 Data Science Python Notebooks repo

✍️ A collection of notebooks in various fields of data science such as deep learning, machine learning, data analysis and Python topics.

📂 Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Transformer

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List of Running Processes using Python

📂 Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Transformer

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Pandas is getting outdated.

5 reasons you should move to FireDucks 👇

1. Requires changing ONLY ONE line of code:
↳ Replace "𝗶𝗺𝗽𝗼𝗿𝘁 𝗽𝗮𝗻𝗱𝗮𝘀 𝗮𝘀 𝗽𝗱" with "𝗶𝗺𝗽𝗼𝗿𝗲 𝗳𝗶𝗿𝗲𝗱𝘂𝗰𝗸𝘀.𝗽𝗮𝗻𝗱𝗮𝘀 𝗮𝘀 𝗽𝗱"
↳ The rest of the entire code remains the same.
↳ So, if you know Pandas, you already know how to use FireDucks.
↳ Done!

2. Ridiculously faster as per official benchmarks:
↳ Modin had an average speed-up of 0.9x over Pandas.
↳ Polars had an average speed-up of 39x over Pandas.
↳ But FireDucks had an average speed-up of 50x over Pandas.

3. Pandas is single-core; FireDucks is multi-core.

4. Pandas follows eager execution; FireDucks is based on lazy execution. This way, FireDucks can build a logical execution plan and apply possible optimizations.

5. That said, even under eager execution, FireDucks is way faster than Pandas, as depicted in the image below.

📂 Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Pandas #FireDucks

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Master #DataAnalytics for real-world problems. I have curated a list of 40 Data Analytics Projects (solved & explained) that will help you build analytics skills using #Python.

Includes projects like:

1. Rainfall Trends in India Analysis
2. Netflix Content Strategy Analysis
3. Creating a Mutual Fund Plan
4. Stock Market Portfolio Optimization
5. Metro Operations Optimization
6. Analyzing the Impact of Carbon Emissions

Find this list of projects here:
https://thecleverprogrammer.com/2024/11/01/data-analytics-projects-with-python/

📂 Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Pandas #deeplearning #Projects

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✅ A comprehensive playlist to step into and master the world of machine learning and data science!


1️⃣ Data Science Principles:

😉 Essential Mathematics for Machine Learning: Link
😉 Overview and commonly used terms: Link
😉 Current interview trends: Link
😉 Linear Regression Guide: Link
😉 Logistic Regression Playlist: Link
😉 Classification criteria: Link
😉 Simple Bayes Classifier: Link
😉 Types of variables: Link
😉 Dimension reduction: Link
😉 Entropy, mutual entropy, KL divergence: link
😉 Dynamic Pricing Overview: Link


2️⃣ Building recommender systems:

😉 Netflix Calibrated Recommendations: Link
😉 Netflix Integrated Recommendation Model: Link
😉 The Evolution of Recommender Systems: Link
😉 Embedding tutorial: Link
😉 Annoy library for approximate nearest neighbor: link
😉 Reducer product for ANN: Link
😉 Model-based account recommendations: Link
😉 PID controller for diversity: link
😉 Instagram Recommender System: Link
😉 LinkedIn CTR Modeling: Link
😉 Meituan's two-tower recommendation model: Link
😉 Scalable Two Tower Model Question-Item: Link
😉 Twitter Recommender Algorithm: Link
😉 eBay language model for recommender system: link
😉 Overcoming biases for recommender systems: Link


3️⃣ Advanced Model Techniques and Applications:

😉 Importance of Model Calibration: Link
😉 Detect and monitor data changes: Link
😉 Neural Networks Training: Link
😉 Analytics-based advertising with Pinterest: Link
😉 Using Pre-trained Bert: Link
😉 Model Compression with Knowledge Distillation: Link
😉 Multi-Armed Bandit Strategies: Link


4️⃣ The world of large language models (LLMs):

😉 Conversational AI: Link
😉 The dual nature of conversational language models: link
😉 Frontier Developments in LLM: Link
😉 Improving the performance of open source LLMs: Link
😉 Building artificial intelligence in Shah Rukh Khan style: Link

📂 Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Pandas

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Data Visualization Cheat sheets and Resources

Corpus of 32 DV cheat sheets, 32 DV charts and 7 recommended DV books

📂 Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Pandas #DV

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IP Address Information using Python 🖥

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🚀 Free Python Course with Certificate🚀

Unlock the world of coding with our
Python Fundamentals for Beginners course—completely FREE! 🚀 Whether you're new to programming or want to brush up on your skills, this course has everything you need to get started.

Why Choose This Course?
💡 100% FREE—No hidden fees
💡 Flexible learning at your own pace
💡 Certificate of completion to boost your resume
💡 Practical, hands-on learning with real examples

👉 Apply now - https://t.ly/6IvgN

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Python Cheatsheets ⭐️

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Python Notes ⭐️

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🎥 Free course "Computational Thinking and Data Science"
📊 MIT University

👨🏻‍💻 One of the best resources I've found for learning computational thinking and data science is this free course from MIT. It covers concepts like data analysis, computational modeling, and using algorithms to solve complex problems. I've included the links to the slides and videos from the course below:👇

📄 Slides link: Lecture Slides and Files

📹 Video links: Lecture Videos

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👨🏻‍💻 One of the most popular GitHub repositories for "learning and using algorithms in Python" is The Algorithms - Python repo with 196K stars.

✏️ It has a lot of organized and categorized code that you can use to find, read, and run different algorithms. Everything you can think of is here; from simple algorithms like sorting to advanced algorithms for machine learning, artificial intelligence, neural networks, and more.

✅ Why should we use it?

🔢 For learning: If you're looking to learn algorithms in action, this is great.

🔢 For practice: You can take the codes, run them, and modify them to better understand.

🔢 For projects : You can even use the codes here in real-life or academic projects.

🔢 For interviews: If you're preparing for data science interviews, this is full of practical algorithms.


┌ 🏳️‍🌈 The Algorithms - Python
└ 🐱 GitHub-Repos

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15 ways to optimize neural network training

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