Machine Learning Notes with Real Project and Amazing discussion

#MachineLearning #AI #DataScience #MLAlgorithms #DeepLearning

https://t.me/CodeProgrammer 🌟

🤗 HuggingFace is offering 9 AI courses for FREE!

These 9 courses covers LLMs, Agents, Deep RL, Audio and more

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Best Machine Learning Notes

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9 machine learning concepts for ML engineers!

(explained as visually as possible)

Here's a recap of several visual summaries posted in the Daily Dose of Data Science newsletter.

1️⃣ 4 strategies for Multi-GPU Training.

- Training at scale? Learn these strategies to maximize efficiency and minimize model training time.
- Read here: https://lnkd.in/gmXF_PgZ

2️⃣ 4 ways to test models in production

- While testing a model in production might sound risky, ML teams do it all the time, and it isn’t that complicated.
- Implemented here: https://lnkd.in/g33mASMM

3️⃣ Training & inference time complexity of 10 ML algorithms

Understanding the run time of ML algorithms is important because it helps you:
- Build a core understanding of an algorithm.
- Understand the data-specific conditions to use the algorithm
- Read here: https://lnkd.in/gKJwJ__m

4️⃣ Regression & Classification Loss Functions.

- Get a quick overview of the most important loss functions and when to use them.
- Read here: https://lnkd.in/gzFPBh-H

5️⃣ Transfer Learning, Fine-tuning, Multitask Learning, and Federated Learning.

- The holy grail of advanced learning paradigms, explained visually.
- Learn about them here: https://lnkd.in/g2hm8TMT

6️⃣ 15 Pandas to Polars to SQL to PySpark Translations.

- The visual will help you build familiarity with four popular frameworks for data analysis and processing.
- Read here: https://lnkd.in/gP-cqjND

7️⃣ 11 most important plots in data science

- A must-have visual guide to interpret and communicate your data effectively.
- Explained here: https://lnkd.in/geMt98tF

8️⃣ 11 types of variables in a dataset

Understand and categorize dataset variables for better feature engineering.
- Explained here: https://lnkd.in/gQxMhb_p

9️⃣ NumPy cheat sheet for data scientists

- The ultimate cheat sheet for fast, efficient numerical computing in Python.
- Read here: https://lnkd.in/gbF7cJJE

#MachineLearning #DataScience #MLEngineering #DeepLearning #AI #MLOps #BigData #Python #NumPy #Pandas #Visualization

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📕 Mastering NumPy – From Basics to Advanced

NumPy is an essential library in the world of data science, widely recognized for its efficiency in numerical computations and data manipulation. This powerful tool simplifies complex operations with arrays, offering a faster and cleaner alternative to traditional Python lists and loops.

The "Mastering NumPy" booklet provides a comprehensive walkthrough—from array creation and indexing to mathematical/statistical operations and advanced topics like reshaping and stacking. All concepts are illustrated with clear, beginner-friendly examples, making it ideal for anyone aiming to boost their data handling skills.

#NumPy #Python #DataScience #MachineLearning #AI #BigData #DeepLearning #DataAnalysis

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⚡ 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.

#DeepLearning #AI #MachineLearning #LearnAI #DeepLearningForBeginners

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🔥 How to become a data scientist in 2025?


1️⃣ First of all, strengthen your foundation (math and statistics) .

✏️ If you don't know math, you'll run into trouble wherever you go. Every model you build, every analysis you do, there's a world of math behind it. You need to know these things well:

✅ Linear Algebra: Link

✅ Calculus: Link

✅ Statistics and Probability: Link

➖➖➖➖➖➖

2️⃣ Then learn programming !

✏️ Without further ado, get started learning Python and SQL.

✅ Python: Link

✅ SQL language: Link

✅ Data Structures and Algorithms: Link

➖➖➖➖➖➖

3️⃣ Learn to clean and analyze data!

✏️ Data is always messy, and a data scientist must know how to organize it and extract insights from it.

✅ Data cleansing: Link

✅ Data visualization: Link

➖➖➖➖➖➖

4️⃣ Learn machine learning !

✏️ Once you've mastered the basic skills, it's time to enter the world of machine learning. Here's what you need to know:

◀️ Supervised learning: regression, classification

◀️ Unsupervised learning: clustering, dimensionality reduction

◀️ Deep learning: neural networks, CNN, RNN

✅ Stanford University CS229 course: Link

➖➖➖➖➖➖

5️⃣ Get to know big data and cloud computing !

✏️ Large companies are looking for people who can work with large volumes of data.

◀️ Big data tools (e.g. Hadoop, Spark, Dask)

◀️ Cloud services (AWS, GCP, Azure)

➖➖➖➖➖➖

6️⃣ Do a real project and build a portfolio !

✏️ Everything you've learned so far is worthless without a real project!

◀️ Participate in Kaggle and work with real data.

◀️ Do a project from scratch (from data collection to model deployment)

◀️ Put your code on GitHub.

✅ Open Source Data Science Projects: Link

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7️⃣ It's time to learn MLOps and model deployment!

✏️ Many people just build models but don't know how to deploy them. But companies want someone who can put the model into action!

◀️ Machine learning operationalization (monitoring, updating models)

◀️ Model deployment tools: Flask, FastAPI, Docker

✅ Stanford University MLOps Course: Link

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8️⃣ Always stay up to date and network!

✏️ Follow research articles on arXiv and Google Scholar.

✅ Papers with Code website: link

✅ AI Research at Google website: link

#DataScience #HowToBecomeADataScientist #ML2025 #Python #SQL #MachineLearning #MathForDataScience #BigData #MLOps #DeepLearning #AIResearch #DataVisualization #PortfolioProjects #CloudComputing #DSCareerPath



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Anyone trying to deeply understand Large Language Models.

Checkout

Foundations of Large Language Models

by Tong Xiao & Jingbo Zhu. It’s one of the clearest, most comprehensive resource.

⭐️ Paper Link: arxiv.org/pdf/2501.09223

#LLMs #LargeLanguageModels #AIResearch #DeepLearning #MachineLearning #AIResources #NLP #AITheory #FoundationModels #AIUnderstanding



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Self-attention in LLMs, clearly explained

#SelfAttention #LLMs #Transformers #NLP #DeepLearning #MachineLearning #AIExplained #AttentionMechanism #AIConcepts #AIEducation

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🥇 40+ Real and Free Data Science Projects

👨🏻‍💻 Real learning means implementing ideas and building prototypes. It's time to skip the repetitive training and get straight to real data science projects!

🔆 With the DataSimple.education website, you can access 40+ data science projects with Python completely free ! From data analysis and machine learning to deep learning and AI.

✏️ There are no beginner projects here; you work with real datasets. Each project is well thought out and guides you step by step. For example, you can build a stock forecasting model, analyze customer behavior, or even study the impact of major global events on your data.

┌🏳️‍🌈 40+ Python Data Science Projects
└ 🌎 Website

#DataScience #PythonProjects #MachineLearning #DeepLearning #AIProjects #RealWorldData #OpenSource #DataAnalysis #ProjectBasedLearning #LearnByBuilding

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🔍 Understanding Recurrent Neural Networks (RNNs) Cheat Sheet!
Recurrent Neural Networks are a powerful type of neural network designed to handle sequential data. They are widely used in applications like natural language processing, speech recognition, and time-series prediction. Here's a quick cheat sheet to get you started:

📘 Key Concepts:
Sequential Data: RNNs are designed to process sequences of data, making them ideal for tasks where order matters.
Hidden State: Maintains information from previous inputs, enabling memory across time steps.
Backpropagation Through Time (BPTT): The method used to train RNNs by unrolling the network through time.

🔧 Common Variants:
Long Short-Term Memory (LSTM): Addresses vanishing gradient problems with gates to manage information flow.
Gated Recurrent Unit (GRU): Similar to LSTMs but with a simpler architecture.

🚀 Applications:
Language Modeling: Predicting the next word in a sentence.
Sentiment Analysis: Understanding sentiments in text.
Time-Series Forecasting: Predicting future data points in a series.

🔗 Resources:
Dive deeper with tutorials on platforms like Coursera, edX, or YouTube.
Explore open-source libraries like TensorFlow or PyTorch for implementation.
Let's harness the power of RNNs to innovate and solve complex problems! 💡

#RNN #RecurrentNeuralNetworks #DeepLearning #NLP #LSTM #GRU #TimeSeriesForecasting #MachineLearning #NeuralNetworks #AIApplications #SequenceModeling #MLCheatSheet #PyTorch #TensorFlow #DataScience

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AI vs ML vs Deep Learning vs Generative AI

#ArtificialIntelligence #MachineLearning #DeepLearning #GenerativeAI #AIVsML #AITechnology #LearnAI #AIExplained



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A curated collection of Kaggle notebooks showcasing how to build end-to-end AI applications using Hugging Face pretrained models, covering text, speech, image, and vision-language tasks — full tutorials and code available on GitHub:

1️⃣ Text-Based Applications

1.1. Building a Chatbot Using HuggingFace Open Source Models

https://lnkd.in/dku3bigK

1.2. Building a Text Translation System using Meta NLLB Open-Source Model

https://lnkd.in/dgdjaFds

2️⃣ Speech-Based Applications

2.1. Zero-Shot Audio Classification Using HuggingFace CLAP Open-Source Model

https://lnkd.in/dbgQgDyn

2.2. Building & Deploying a Speech Recognition System Using the Whisper Model & Gradio

https://lnkd.in/dcbp-8fN

2.3. Building Text-to-Speech Systems Using VITS & ArTST Models

https://lnkd.in/dwFcQ_X5

3️⃣ Image-Based Applications

3.1. Step-by-Step Guide to Zero-Shot Image Classification using CLIP Model

https://lnkd.in/dnk6epGB

3.2. Building an Object Detection Assistant Application: A Step-by-Step Guide

https://lnkd.in/d573SvYV

3.3. Zero-Shot Image Segmentation using Segment Anything Model (SAM)

https://lnkd.in/dFavEdHS

3.4. Building Zero-Shot Depth Estimation Application Using DPT Model & Gradio

https://lnkd.in/d9jjJu_g

4️⃣ Vision Language Applications

4.1. Building a Visual Question Answering System Using Hugging Face Open-Source Models

https://lnkd.in/dHNFaHFV

4.2. Building an Image Captioning System using Salesforce Blip Model

https://lnkd.in/dh36iDn9

4.3. Building an Image-to-Text Matching System Using Hugging Face Open-Source Models

https://lnkd.in/d7fsJEAF

➡️ You can find the articles and the codes for each article in this GitHub repo:

https://lnkd.in/dG5jfBwE

#HuggingFace #Kaggle #AIapplications #DeepLearning #MachineLearning #ComputerVision #NLP #SpeechRecognition #TextToSpeech #ImageProcessing #OpenSourceAI #ZeroShotLearning #Gradio

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The 2025 MIT deep learning course is excellent, covering neural networks, CNNs, RNNs, and LLMs. You build three projects for hands-on experience as part of the course. It is entirely free. Highly recommended for beginners.

Enroll Free: https://introtodeeplearning.com/

#DeepLearning #MITCourse #NeuralNetworks #CNN #RNN #LLMs #AIForBeginners #FreeCourse #MachineLearning #IntroToDeepLearning #AIProjects #LearnAI #AI2025

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Top 50 LLM Interview Questions!

#LLM #AIInterviews #MachineLearning #DeepLearning #NLP #LLMInterviewPrep #ModelArchitectures #AITheory #TechInterviews #MLBasics #InterviewQuestions #LargeLanguageModels

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