Get important resources from books and courses
The number is very limited
https://t.me/+r_Tcx2c-oVU1OWNi
The number is very limited
https://t.me/+r_Tcx2c-oVU1OWNi
Telegram
Data Science Premium (Books & Courses)
access to thousands of valuable resources, including essential books and courses.
Paid books
Paid courses from coursera and Udemy
Paid project
Paid books
Paid courses from coursera and Udemy
Paid project
❤8👍1👨💻1
Step-by-Step Guide to Deploying Machine Learning Models with FastAPI and Docker
https://machinelearningmastery.com/step-by-step-guide-to-deploying-machine-learning-models-with-fastapi-and-docker/
https://machinelearningmastery.com/step-by-step-guide-to-deploying-machine-learning-models-with-fastapi-and-docker/
✈️ Our Telegram channels⬅️ 📱 Our WhatsApp channel⬅️
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𝗬𝗼𝘂𝗿_𝗗𝗮𝘁𝗮_𝗦𝗰𝗶𝗲𝗻𝗰𝗲_𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄_𝗦𝘁𝘂𝗱𝘆_𝗣𝗹𝗮𝗻.pdf
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
#DataScience #InterviewPrep #MLInterviews #DataEngineering #SQL #Python #Statistics #MachineLearning #DataStorytelling #SystemDesign #CareerGrowth #DataScienceRoadmap #PortfolioBuilding #MockInterviews #JobHuntingTips
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rnn.pdf
5.6 MB
🔍 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!💡
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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Intent | AI-Enhanced Telegram
🌐 Supports real-time translation in 86 languages
💬 Simply swipe up during chat to let AI automatically generate contextual replies
🎙 Instant AI enhanced voice-to-text conversion
🧠 Built-in mainstream models including GPT-4o, Claude 3.7, Gemini 2, Deepseek, etc., activated with one click
🎁 Currently offering generous free AI credits
📱 Supports Android & iOS systems
🔎 Website | 📬 Download
🌐 Supports real-time translation in 86 languages
💬 Simply swipe up during chat to let AI automatically generate contextual replies
🎙 Instant AI enhanced voice-to-text conversion
🧠 Built-in mainstream models including GPT-4o, Claude 3.7, Gemini 2, Deepseek, etc., activated with one click
🎁 Currently offering generous free AI credits
📱 Supports Android & iOS systems
🔎 Website | 📬 Download
intentchat.app
Intent - Your Multilingual Telegram Messenger.
Real-time automatic translation and AI-powered assistant lets you type in your own language and effortlessly connect with people worldwide.
❤5
ds full archive.pdf.pdf
55.2 MB
Best Data Science Archive Notes
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𝗠𝗮𝘀𝘁𝗲𝗿_𝗣𝘆𝗦𝗽𝗮𝗿𝗸_𝗟𝗶𝗸𝗲_𝗮_𝗣𝗿𝗼_–_𝗔𝗹𝗹_𝗶𝗻_𝗢𝗻𝗲_𝗚𝘂𝗶𝗱𝗲_𝗳𝗼𝗿_𝗗𝗮𝘁𝗮_𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀.pdf
2.6 MB
𝗠𝗮𝘀𝘁𝗲𝗿 𝗣𝘆𝗦𝗽𝗮𝗿𝗸 𝗟𝗶𝗸𝗲 𝗮 𝗣𝗿𝗼 – 𝗔𝗹𝗹-𝗶𝗻-𝗢𝗻𝗲 𝗚𝘂𝗶𝗱𝗲 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀
If you're a data engineer, aspiring Spark developer, or someone preparing for big data interviews — this one is for you.
I’m sharing a powerful, all-in-one PySpark notes sheet that covers both fundamentals and advanced techniques for real-world usage and interviews.
𝗪𝗵𝗮𝘁'𝘀 𝗶𝗻𝘀𝗶𝗱𝗲? • Spark vs MapReduce
• Spark Architecture – Driver, Executors, DAG
• RDDs vs DataFrames vs Datasets
• SparkContext vs SparkSession
• Transformations: map, flatMap, reduceByKey, groupByKey
• Optimizations – caching, persisting, skew handling, salting
• Joins – Broadcast joins, Shuffle joins
• Deployment modes – Cluster vs Client
• Real interview-ready Q&A from top use cases
• CSV, JSON, Parquet, ORC – Format comparisons
• Common commands, schema creation, data filtering, null handling
𝗪𝗵𝗼 𝗶𝘀 𝘁𝗵𝗶𝘀 𝗳𝗼𝗿? Data Engineers, Spark Developers, Data Enthusiasts, and anyone preparing for interviews or working on distributed systems.
If you're a data engineer, aspiring Spark developer, or someone preparing for big data interviews — this one is for you.
I’m sharing a powerful, all-in-one PySpark notes sheet that covers both fundamentals and advanced techniques for real-world usage and interviews.
𝗪𝗵𝗮𝘁'𝘀 𝗶𝗻𝘀𝗶𝗱𝗲? • Spark vs MapReduce
• Spark Architecture – Driver, Executors, DAG
• RDDs vs DataFrames vs Datasets
• SparkContext vs SparkSession
• Transformations: map, flatMap, reduceByKey, groupByKey
• Optimizations – caching, persisting, skew handling, salting
• Joins – Broadcast joins, Shuffle joins
• Deployment modes – Cluster vs Client
• Real interview-ready Q&A from top use cases
• CSV, JSON, Parquet, ORC – Format comparisons
• Common commands, schema creation, data filtering, null handling
𝗪𝗵𝗼 𝗶𝘀 𝘁𝗵𝗶𝘀 𝗳𝗼𝗿? Data Engineers, Spark Developers, Data Enthusiasts, and anyone preparing for interviews or working on distributed systems.
#PySpark #DataEngineering #BigData #SparkArchitecture #RDDvsDataFrame #SparkOptimization #DistributedComputing #SparkInterviewPrep #DataPipelines #ApacheSpark #MapReduce #ETL #BroadcastJoin #ClusterComputing #SparkForEngineers
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🐍📰 This tutorial will give you an overview of LangGraph fundamentals through hands-on examples, and the tools needed to build your own LLM workflows and agents in LangGraph
Link: https://realpython.com/langgraph-python/
Link: https://realpython.com/langgraph-python/
#LangGraph #Python #LLMWorkflows #AIAgents #RealPython #PythonTutorials #LargeLanguageModels #AIAgents #WorkflowAutomation #PythonForA
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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
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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Forwarded from Python | Machine Learning | Coding | R
This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages
✅ https://t.me/addlist/8_rRW2scgfRhOTc0
✅ https://t.me/Codeprogrammer
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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/
Enroll Free: https://introtodeeplearning.com/
#DeepLearning #MITCourse #NeuralNetworks #CNN #RNN #LLMs #AIForBeginners #FreeCourse #MachineLearning #IntroToDeepLearning #AIProjects #LearnAI #AI2025
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New to Pandas?
Here's a cheat sheet you can download (2025)
Here's a cheat sheet you can download (2025)
#Pandas #Python #DataAnalysis #PandasCheatSheet #PythonForDataScience #LearnPandas #DataScienceTools #PythonLibraries #FreeResources #DataManipulation
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Top 50 LLM Interview Questions!
A comprehensive resource that covers traditional ML basics, model architectures, real-world case studies, and theoretical foundations.
👇👇👇👇👇👇
A comprehensive resource that covers traditional ML basics, model architectures, real-world case studies, and theoretical foundations.
👇👇👇👇👇👇
✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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❤3👍3
LLM Interview Questions.pdf
71.2 KB
Top 50 LLM Interview Questions!
#LLM #AIInterviews #MachineLearning #DeepLearning #NLP #LLMInterviewPrep #ModelArchitectures #AITheory #TechInterviews #MLBasics #InterviewQuestions #LargeLanguageModels
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This book covers foundational topics within computer vision, with an image processing and machine learning perspective. We want to build the reader’s intuition and so we include many visualizations. The audience is undergraduate and graduate students who are entering the field, but we hope experienced practitioners will find the book valuable as well.
Our initial goal was to write a large book that provided a good coverage of the field. Unfortunately, the field of computer vision is just too large for that. So, we decided to write a small book instead, limiting each chapter to no more than five pages. Such a goal forced us to really focus on the important concepts necessary to understand each topic. Writing a short book was perfect because we did not have time to write a long book and you did not have time to read it. Unfortunately, we have failed at that goal, too.
Read it online: https://visionbook.mit.edu/
Our initial goal was to write a large book that provided a good coverage of the field. Unfortunately, the field of computer vision is just too large for that. So, we decided to write a small book instead, limiting each chapter to no more than five pages. Such a goal forced us to really focus on the important concepts necessary to understand each topic. Writing a short book was perfect because we did not have time to write a long book and you did not have time to read it. Unfortunately, we have failed at that goal, too.
Read it online: https://visionbook.mit.edu/
#ComputerVision #ImageProcessing #MachineLearning #CVBook #VisualLearning #AIResources #ComputerVisionBasics #MLForVision #AcademicResources #LearnComputerVision #AIIntuition #DeepLearning
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❤4
Intent | AI-Enhanced Telegram
🚨 Breaking: Telegram’s translator is off-air!
🌐 Intent’s rock-solid translation—86 languages in real time
⬆️ Chat swipe summons AI for seamless context replies
🎤 AI voice-to-text, lightning fast
🤖 One-click hub for GPT-4o, Claude 3.7, Gemini 2 & more
🎁 Limited-time free AI credits
📱 Supports Android & iOS
📮Download
🚨 Breaking: Telegram’s translator is off-air!
🌐 Intent’s rock-solid translation—86 languages in real time
⬆️ Chat swipe summons AI for seamless context replies
🎤 AI voice-to-text, lightning fast
🤖 One-click hub for GPT-4o, Claude 3.7, Gemini 2 & more
🎁 Limited-time free AI credits
📱 Supports Android & iOS
📮Download
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Over the last year, several articles have been written to help candidates prepare for data science technical interviews. These resources cover a wide range of topics including machine learning, SQL, programming, statistics, and probability.
1️⃣ Machine Learning (ML) Interview
Types of ML Q&A in Data Science Interview
https://shorturl.at/syN37
ML Interview Q&A for Data Scientists
https://shorturl.at/HVWY0
Crack the ML Coding Q&A
https://shorturl.at/CDW08
Deep Learning Interview Q&A
https://shorturl.at/lHPZ6
Top LLMs Interview Q&A
https://shorturl.at/wGRSZ
Top CV Interview Q&A [Part 1]
https://rb.gy/51jcfi
Part 2
https://rb.gy/hqgkbg
Part 3
https://rb.gy/5z87be
2️⃣ SQL Interview Preparation
13 SQL Statements for 90% of Data Science Tasks
https://rb.gy/dkdcl1
SQL Window Functions: Simplifying Complex Queries
https://t.ly/EwSlH
Ace the SQL Questions in the Technical Interview
https://lnkd.in/gNQbYMX9
Unlocking the Power of SQL: How to Ace Top N Problem Questions
https://lnkd.in/gvxVwb9n
How To Ace the SQL Ratio Problems
https://lnkd.in/g6JQqPNA
Cracking the SQL Window Function Coding Questions
https://lnkd.in/gk5u6hnE
SQL & Database Interview Q&A
https://lnkd.in/g75DsEfw
6 Free Resources for SQL Interview Preparation
https://lnkd.in/ghhiG79Q
3️⃣ Programming Questions
Foundations of Data Structures [Part 1]
https://lnkd.in/gX_ZcmRq
Part 2
https://lnkd.in/gATY4rTT
Top Important Python Questions [Conceptual]
https://lnkd.in/gJKaNww5
Top Important Python Questions [Data Cleaning and Preprocessing]
https://lnkd.in/g-pZBs3A
Top Important Python Questions [Machine & Deep Learning]
https://lnkd.in/gZwcceWN
Python Interview Q&A
https://lnkd.in/gcaXc_JE
5 Python Tips for Acing DS Coding Interview
https://lnkd.in/gsj_Hddd
4️⃣ Statistics
Mastering 5 Statistics Concepts to Boost Success
https://lnkd.in/gxEuHiG5
Mastering Hypothesis Testing for Interviews
https://lnkd.in/gSBbbmF8
Introduction to A/B Testing
https://lnkd.in/g35Jihw6
Statistics Interview Q&A for Data Scientists
https://lnkd.in/geHCCt6Q
5️⃣ Probability
15 Probability Concepts to Review [Part 1]
https://lnkd.in/g2rK2tQk
Part 2
https://lnkd.in/gQhXnKwJ
Probability Interview Q&A [Conceptual Questions]
https://lnkd.in/g5jyKqsp
Probability Interview Q&A [Mathematical Questions]
https://lnkd.in/gcWvPhVj
🔜 All links are available in the GitHub repository:
https://lnkd.in/djcgcKRT
Types of ML Q&A in Data Science Interview
https://shorturl.at/syN37
ML Interview Q&A for Data Scientists
https://shorturl.at/HVWY0
Crack the ML Coding Q&A
https://shorturl.at/CDW08
Deep Learning Interview Q&A
https://shorturl.at/lHPZ6
Top LLMs Interview Q&A
https://shorturl.at/wGRSZ
Top CV Interview Q&A [Part 1]
https://rb.gy/51jcfi
Part 2
https://rb.gy/hqgkbg
Part 3
https://rb.gy/5z87be
13 SQL Statements for 90% of Data Science Tasks
https://rb.gy/dkdcl1
SQL Window Functions: Simplifying Complex Queries
https://t.ly/EwSlH
Ace the SQL Questions in the Technical Interview
https://lnkd.in/gNQbYMX9
Unlocking the Power of SQL: How to Ace Top N Problem Questions
https://lnkd.in/gvxVwb9n
How To Ace the SQL Ratio Problems
https://lnkd.in/g6JQqPNA
Cracking the SQL Window Function Coding Questions
https://lnkd.in/gk5u6hnE
SQL & Database Interview Q&A
https://lnkd.in/g75DsEfw
6 Free Resources for SQL Interview Preparation
https://lnkd.in/ghhiG79Q
Foundations of Data Structures [Part 1]
https://lnkd.in/gX_ZcmRq
Part 2
https://lnkd.in/gATY4rTT
Top Important Python Questions [Conceptual]
https://lnkd.in/gJKaNww5
Top Important Python Questions [Data Cleaning and Preprocessing]
https://lnkd.in/g-pZBs3A
Top Important Python Questions [Machine & Deep Learning]
https://lnkd.in/gZwcceWN
Python Interview Q&A
https://lnkd.in/gcaXc_JE
5 Python Tips for Acing DS Coding Interview
https://lnkd.in/gsj_Hddd
Mastering 5 Statistics Concepts to Boost Success
https://lnkd.in/gxEuHiG5
Mastering Hypothesis Testing for Interviews
https://lnkd.in/gSBbbmF8
Introduction to A/B Testing
https://lnkd.in/g35Jihw6
Statistics Interview Q&A for Data Scientists
https://lnkd.in/geHCCt6Q
15 Probability Concepts to Review [Part 1]
https://lnkd.in/g2rK2tQk
Part 2
https://lnkd.in/gQhXnKwJ
Probability Interview Q&A [Conceptual Questions]
https://lnkd.in/g5jyKqsp
Probability Interview Q&A [Mathematical Questions]
https://lnkd.in/gcWvPhVj
https://lnkd.in/djcgcKRT
#DataScience #InterviewPrep #MachineLearning #SQL #Python #Statistics #Probability #CodingInterview #AIBootcamp #DeepLearning #LLMs #ComputerVision #GitHubResources #CareerInDataScience
✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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10 GitHub repos to build a career in AI engineering:
(100% free step-by-step roadmap)
1️⃣ ML for Beginners by Microsoft
A 12-week project-based curriculum that teaches classical ML using Scikit-learn on real-world datasets.
Includes quizzes, lessons, and hands-on projects, with some videos.
GitHub repo → https://lnkd.in/dCxStbYv
2️⃣ AI for Beginners by Microsoft
This repo covers neural networks, NLP, CV, transformers, ethics & more. There are hands-on labs in PyTorch & TensorFlow using Jupyter.
Beginner-friendly, project-based, and full of real-world apps.
GitHub repo → https://lnkd.in/dwS5Jk9E
3️⃣ Neural Networks: Zero to Hero
Now that you’ve grasped the foundations of AI/ML, it’s time to dive deeper.
This repo by Andrej Karpathy builds modern deep learning systems from scratch, including GPTs.
GitHub repo → https://lnkd.in/dXAQWucq
4️⃣ DL Paper Implementations
So far, you have learned the fundamentals of AI, ML, and DL. Now study how the best architectures work.
This repo covers well-documented PyTorch implementations of 60+ research papers on Transformers, GANs, Diffusion models, etc.
GitHub repo → https://lnkd.in/dTrtDrvs
5️⃣ Made With ML
Now it’s time to learn how to go from notebooks to production.
Made With ML teaches you how to design, develop, deploy, and iterate on real-world ML systems using MLOps, CI/CD, and best practices.
GitHub repo → https://lnkd.in/dYyjjBGb
6️⃣ Hands-on LLMs
- You've built neural nets.
- You've explored GPTs and LLMs.
Now apply them. This is a visually rich repo that covers everything about LLMs, like tokenization, fine-tuning, RAG, etc.
GitHub repo → https://lnkd.in/dh2FwYFe
7️⃣ Advanced RAG Techniques
Hands-on LLMs will give you a good grasp of RAG systems. Now learn advanced RAG techniques.
This repo covers 30+ methods to make RAG systems faster, smarter, and accurate, like HyDE, GraphRAG, etc.
GitHub repo → https://lnkd.in/dBKxtX-D
8️⃣ AI Agents for Beginners by Microsoft
After diving into LLMs and mastering RAG, learn how to build AI agents.
This hands-on course covers building AI agents using frameworks like AutoGen.
GitHub repo → https://lnkd.in/dbFeuznE
9️⃣ Agents Towards Production
The above course will teach what AI agents are. Next, learn how to ship them.
This is a practical playbook for building agents covering memory, orchestration, deployment, security & more.
GitHub repo → https://lnkd.in/dcwmamSb
🔟 AI Engg. Hub
To truly master LLMs, RAG, and AI agents, you need projects.
This covers 70+ real-world examples, tutorials, and agent app you can build, adapt, and ship.
GitHub repo → https://lnkd.in/geMYm3b6
(100% free step-by-step roadmap)
A 12-week project-based curriculum that teaches classical ML using Scikit-learn on real-world datasets.
Includes quizzes, lessons, and hands-on projects, with some videos.
GitHub repo → https://lnkd.in/dCxStbYv
This repo covers neural networks, NLP, CV, transformers, ethics & more. There are hands-on labs in PyTorch & TensorFlow using Jupyter.
Beginner-friendly, project-based, and full of real-world apps.
GitHub repo → https://lnkd.in/dwS5Jk9E
Now that you’ve grasped the foundations of AI/ML, it’s time to dive deeper.
This repo by Andrej Karpathy builds modern deep learning systems from scratch, including GPTs.
GitHub repo → https://lnkd.in/dXAQWucq
So far, you have learned the fundamentals of AI, ML, and DL. Now study how the best architectures work.
This repo covers well-documented PyTorch implementations of 60+ research papers on Transformers, GANs, Diffusion models, etc.
GitHub repo → https://lnkd.in/dTrtDrvs
Now it’s time to learn how to go from notebooks to production.
Made With ML teaches you how to design, develop, deploy, and iterate on real-world ML systems using MLOps, CI/CD, and best practices.
GitHub repo → https://lnkd.in/dYyjjBGb
- You've built neural nets.
- You've explored GPTs and LLMs.
Now apply them. This is a visually rich repo that covers everything about LLMs, like tokenization, fine-tuning, RAG, etc.
GitHub repo → https://lnkd.in/dh2FwYFe
Hands-on LLMs will give you a good grasp of RAG systems. Now learn advanced RAG techniques.
This repo covers 30+ methods to make RAG systems faster, smarter, and accurate, like HyDE, GraphRAG, etc.
GitHub repo → https://lnkd.in/dBKxtX-D
After diving into LLMs and mastering RAG, learn how to build AI agents.
This hands-on course covers building AI agents using frameworks like AutoGen.
GitHub repo → https://lnkd.in/dbFeuznE
The above course will teach what AI agents are. Next, learn how to ship them.
This is a practical playbook for building agents covering memory, orchestration, deployment, security & more.
GitHub repo → https://lnkd.in/dcwmamSb
To truly master LLMs, RAG, and AI agents, you need projects.
This covers 70+ real-world examples, tutorials, and agent app you can build, adapt, and ship.
GitHub repo → https://lnkd.in/geMYm3b6
#AIEngineering #MachineLearning #DeepLearning #LLMs #RAG #MLOps #Python #GitHubProjects #AIForBeginners #ArtificialIntelligence #NeuralNetworks #OpenSourceAI #DataScienceCareers
✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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Master MCP: The Best Free Learning Resources
1️⃣ Everything you need to know about MCP: The first learning resource is a beginner-friendly introduction to MCP by Replit
https://lnkd.in/djVD73Gz
2️⃣ Model Context Protocol (MCP): A Guide With Demo Project: In this blog, you will be guided through building an MCP-powered PR review server that integrates with Claude Desktop
https://lnkd.in/dXDNbAat
3️⃣ Model Context Protocol (MCP) Hugging Face Course: This free course will take you on a journey, from beginner to informed, in understanding, using, and building applications with MCP
https://lnkd.in/dX5Ja_9m
4️⃣ MCP: Build Rich-Context AI Apps with Anthropic: In this hands-on course, you’ll learn the core concepts of MCP and how to implement it in your AI Application
https://lnkd.in/dxRyjRiW
5️⃣ Official MCP Documents: The official MCP docs are a good resource to learn the fundamentals, a tutorial to create your first MCP server, debugging, and inspection instructions
https://lnkd.in/dqkQ6e_b
6️⃣ Awesome MCP Servers: A curated list of awesome Model Context Protocol (MCP) servers
https://lnkd.in/d2AvkBmb
🌟 You can find more information about each learning resource in this article:
https://lnkd.in/dbDHJnNi
https://lnkd.in/djVD73Gz
https://lnkd.in/dXDNbAat
https://lnkd.in/dX5Ja_9m
https://lnkd.in/dxRyjRiW
https://lnkd.in/dqkQ6e_b
https://lnkd.in/d2AvkBmb
https://lnkd.in/dbDHJnNi
#MCP #ModelContextProtocol #AIApplications #ContextAwareAI #MCPLearning #Anthropic #HuggingFace #Replit #AIIntegration #AIFrameworks #OpenSourceAI #LearnMCP #AIEngineering #PromptEngineering #AIProtocols
✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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