Flow Diagram of Data Lakehouse
While Data Lake is excels for Machine Learning , Data warehouse is used for Business Intelligence , Data Lakehouse excels at both.
Data Lakehouse supports :
Flow Diagram of Data Warehouse
Flow Diagram of Data Lake
Medallion Architecture
Kafka for Fan Out Design Pattern
where one stream is converted to multiple allowing more than one consumer
If you're preparing for the AWS Certified AI Practitioner (AIF-C01) exam, focus on these major domains.
Machines performing tasks that normally require human intelligence.
Subset of AI where systems learn patterns from data.
Uses neural networks with multiple layers.
Creates new content such as:
| Term | Meaning |
|---|---|
| Training | Model learns from data |
| Inference | Model makes predictions using learned knowledge |
Examples:
Text is broken into small units called tokens.
Example:
I love AWS
May become:
[I] [love] [AWS]
Model generates incorrect information while sounding confident.
Amount of information an LLM can consider at once.
Large pretrained model that can be adapted for many tasks.
Examples:
Retraining model with domain-specific data.
Instead of retraining:
Benefits:
| RAG Step | Purpose | Model Type | Example Models |
|---|---|---|---|
| 1. Document Ingestion | Read PDFs, DOCX, HTML, Images | OCR / Document AI | Tesseract, LayoutLM, Donut |
| 2. Chunking | Split documents into passages | Rule-based / NLP | Sentence Splitter, Recursive Text Splitter |
| 3. Text → Embeddings | Convert chunks into vectors | Embedding Model (Encoder-only Transformer) | BERT, Sentence-BERT, E5, BGE |
| 4. Vector Storage | Store embeddings | Vector Database | FAISS, Milvus, Weaviate, Pinecone |
| 5. Query → Embedding | Convert user query to vector | Same Embedding Model | BGE, E5, SBERT |
| 6. Retrieval | Find nearest chunks | ANN Search Algorithm | HNSW, IVF, Flat Search |
| 7. Re-ranking (Optional) | Improve retrieved results | Cross Encoder | MonoBERT, Cohere Rerank, BGE Reranker |
| 8. Context Construction | Build prompt with retrieved chunks | Prompt Builder | Template Engine |
| 9. Answer Generation | Generate final answer | Decoder-only LLM | GPT-4o, Claude Sonnet, Llama 3, Mistral |
| 10. Citation Generation (Optional) | Show sources | LLM / Metadata Layer | GPT-4o, Claude |
| Step | Transformer Type |
|---|---|
| Embedding Generation | Encoder-only |
| Re-ranking | Encoder-only (Cross Encoder) |
| Answer Generation | Decoder-only |
| Translation (optional) | Encoder-Decoder |
| Summarization (optional) | Encoder-Decoder |
| OCR Understanding | Encoder or Encoder-Decoder |
| Multimodal RAG | Vision Encoder + LLM Decoder |
| Transformer Family | Example Models | Used For |
|---|---|---|
| Encoder-only | BERT, RoBERTa, SBERT, E5, BGE | Embeddings, Retrieval |
| Decoder-only | GPT, Llama, Claude, Mistral, Qwen | Generation |
| Encoder-Decoder | T5, FLAN-T5, BART | Summarization, Translation |
| Vision Encoder | ViT, CLIP Vision Encoder | Image Embeddings |
| Vision-Language | LLaVA, Qwen-VL, GPT-4o | Multimodal RAG |
| Layer | Common Choice |
|---|---|
| Chunking | LangChain Recursive Splitter |
| Embeddings | BGE-large, E5-large |
| Vector DB | FAISS, Milvus |
| Retrieval | HNSW |
| Re-ranker | BGE-Reranker |
| Generator | GPT-4o, Claude, Llama 3 |
| Orchestration | LangChain, LlamaIndex |
Documents
↓
Chunking
↓
Encoder Model
(BERT / E5 / BGE)
↓
Embeddings
↓
Vector DB
↓
End
User Query
↓
Encoder Model
(BERT / E5 / BGE)
↓
Similarity Search
↓
Top K Chunks
↓
Cross Encoder Re-ranker
(Optional)
↓
Prompt Construction
↓
Decoder LLM
(GPT / Claude / Llama)
↓
Final Answer
| Task | Model Type |
|---|---|
| Create Embeddings | Encoder-only |
| Retrieve Documents | Vector Search |
| Re-rank Results | Cross Encoder |
| Generate Answer | Decoder-only LLM |
| Summarize Documents | Encoder-Decoder |
| Multimodal Retrieval | CLIP / Vision Encoder |
| Multimodal Generation | GPT-4o / Gemini / Qwen-VL |
Translate this sentence to French.
Provide one example.
Provide multiple examples.
Ask model to reason step-by-step.
Avoid bias.
Understand why model produced output.
Protect user data.
Model behaves reliably.
Users know AI is involved.
Evaluation
Here’s the table reorganized the way you asked:
Model Group
Evaluation
Description of the Evaluation
Agentic Models
AgentBench
Evaluates autonomous task execution, planning, tool usage, and multi-step reasoning.
Agentic Models
GAIA
Tests real-world assistant capabilities like searching, tool calling, and reasoning.
Agentic Models
SWE-bench
Measures ability to solve real GitHub issues by editing codebases.
Bi-Encoder
BEIR
Evaluates embedding-based retrieval across multiple datasets and domains.
Bi-Encoder
MTEB
Measures embedding quality across retrieval, clustering, classification, and reranking tasks.
Bi-Encoder
MS MARCO
Evaluates dense retrieval and passage ranking performance.
Cross-Encoder
BEIR
Measures pairwise query-document relevance scoring quality.
Cross-Encoder
MS MARCO
Evaluates reranking precision for query-passage relevance.
Cross-Encoder
TREC Deep Learning Track
Measures ranking quality for search relevance tasks.
Decoder-only
GSM8K
Evaluates arithmetic and multi-step mathematical reasoning.
Decoder-only
HellaSwag
Measures commonsense reasoning and next-sentence prediction.
Decoder-only
HumanEval
Evaluates code generation correctness using executable unit tests.
Decoder-only
MMLU
Measures broad knowledge and reasoning across many academic domains.
Decoder-only
MT-Bench
Tests instruction following and conversational quality.
Decoder-only
Needle-in-a-Haystack
Measures ability to retrieve specific information from long contexts.
Decoder-only
TruthfulQA
Tests factual consistency and resistance to hallucinations.
Encoder-decoder
BLEU
Measures overlap between generated text and reference text, mainly for translation.
Encoder-decoder
ROUGE
Evaluates summarization quality based on n-gram overlap.
Encoder-decoder
SQuAD
Measures extractive question-answering accuracy.
Encoder-only
GLUE
Evaluates language understanding tasks like sentiment, entailment, and similarity.
Encoder-only
MTEB
Measures embedding performance across multiple NLP tasks.
Encoder-only
STS-B
Measures how well embeddings capture sentence similarity.
Encoder-only
SuperGLUE
Harder version of GLUE for advanced reasoning tasks.
Long-context Models
InfiniteBench
Evaluates memory retention and reasoning over very long contexts.
Long-context Models
LongBench
Tests summarization, retrieval, and reasoning on long documents.
Long-context Models
Needle-in-a-Haystack
Measures retrieval accuracy from large contexts.
Multimodal Models
MMBench
Evaluates image understanding and multimodal reasoning.
Multimodal Models
MMMU
Measures multimodal reasoning across academic and professional domains.
Multimodal Models
MMVet
Tests advanced visual reasoning and perception.
RAG Systems
CRUD-RAG
Measures retrieval robustness and update handling in RAG pipelines.
RAG Systems
RAGAS
Evaluates faithfulness, context precision, context recall, and answer relevance in RAG.
Reward Models
RewardBench
Evaluates preference model quality and alignment performance.
Tool-use Models
ToolBench
Measures correctness in tool selection, API usage, and tool chaining.
Here’s the table reorganized the way you asked:
Model Group Evaluation Description of the Evaluation
Agentic Models AgentBench Evaluates autonomous task execution, planning, tool usage, and multi-step reasoning.
Agentic Models GAIA Tests real-world assistant capabilities like searching, tool calling, and reasoning.
Agentic Models SWE-bench Measures ability to solve real GitHub issues by editing codebases.
Bi-Encoder BEIR Evaluates embedding-based retrieval across multiple datasets and domains.
Bi-Encoder MTEB Measures embedding quality across retrieval, clustering, classification, and reranking tasks.
Bi-Encoder MS MARCO Evaluates dense retrieval and passage ranking performance.
Cross-Encoder BEIR Measures pairwise query-document relevance scoring quality.
Cross-Encoder MS MARCO Evaluates reranking precision for query-passage relevance.
Cross-Encoder TREC Deep Learning Track Measures ranking quality for search relevance tasks.
Decoder-only GSM8K Evaluates arithmetic and multi-step mathematical reasoning.
Decoder-only HellaSwag Measures commonsense reasoning and next-sentence prediction.
Decoder-only HumanEval Evaluates code generation correctness using executable unit tests.
Decoder-only MMLU Measures broad knowledge and reasoning across many academic domains.
Decoder-only MT-Bench Tests instruction following and conversational quality.
Decoder-only Needle-in-a-Haystack Measures ability to retrieve specific information from long contexts.
Decoder-only TruthfulQA Tests factual consistency and resistance to hallucinations.
Encoder-decoder BLEU Measures overlap between generated text and reference text, mainly for translation.
Encoder-decoder ROUGE Evaluates summarization quality based on n-gram overlap.
Encoder-decoder SQuAD Measures extractive question-answering accuracy.
Encoder-only GLUE Evaluates language understanding tasks like sentiment, entailment, and similarity.
Encoder-only MTEB Measures embedding performance across multiple NLP tasks.
Encoder-only STS-B Measures how well embeddings capture sentence similarity.
Encoder-only SuperGLUE Harder version of GLUE for advanced reasoning tasks.
Long-context Models InfiniteBench Evaluates memory retention and reasoning over very long contexts.
Long-context Models LongBench Tests summarization, retrieval, and reasoning on long documents.
Long-context Models Needle-in-a-Haystack Measures retrieval accuracy from large contexts.
Multimodal Models MMBench Evaluates image understanding and multimodal reasoning.
Multimodal Models MMMU Measures multimodal reasoning across academic and professional domains.
Multimodal Models MMVet Tests advanced visual reasoning and perception.
RAG Systems CRUD-RAG Measures retrieval robustness and update handling in RAG pipelines.
RAG Systems RAGAS Evaluates faithfulness, context precision, context recall, and answer relevance in RAG.
Reward Models RewardBench Evaluates preference model quality and alignment performance.
Tool-use Models ToolBench Measures correctness in tool selection, API usage, and tool chaining.
Most important service for the exam.
Provides access to foundation models from:
Features:
Enterprise chatbot over company data.
Developer coding assistant.
Build, train and deploy ML models.
AWS secures:
Customer secures:
Identity and access management.
Data:
Spam or Not Spam
Positive / Negative
Long article -> Short summary
Customer support
Generate Java/Python code
Invoice extraction
| Service | Purpose |
|---|---|
| Bedrock | Generative AI |
| SageMaker AI | Build/train ML models |
| Comprehend | NLP analysis |
| Rekognition | Image analysis |
| Textract | Document extraction |
| Transcribe | Speech to text |
| Polly | Text to speech |
| Translate | Translation |
| Topic | Remember |
|---|---|
| Generative AI | Creates new content |
| Foundation Model | Large pretrained model |
| Hallucination | Confident wrong answer |
| RAG | Retrieve + Generate |
| Bedrock | Managed GenAI platform |
| SageMaker | ML lifecycle |
| Guardrails | Safety controls |
| Fine-Tuning | Retrain model |
| Inference | Model prediction |
| Token | Small text unit |
For exam success, spend extra time on:
These areas account for a large percentage of the AWS AI Practitioner questions.
If you're preparing for the NVIDIA Agentic AI and LLMs Certification, expect questions around LLM fundamentals, RAG, agents, vector databases, orchestration, tool calling, evaluation, deployment, and NVIDIA's AI stack.
LLM Fundamentals
Q1. What is the difference between pre-training and fine-tuning?
A: Pre-training learns general language patterns from large corpora; fine-tuning adapts the model to a specific task using labeled data.
Q2. What is a token?
A: A token is the basic unit processed by an LLM, representing words, subwords, or characters.
Q3. What causes hallucinations in LLMs?
A: Missing knowledge, ambiguous prompts, outdated training data, and probabilistic text generation.
Q4. What is the context window?
A: The maximum number of tokens an LLM can process in a single request.
---
RAG (Retrieval-Augmented Generation)
Q5. Why use RAG instead of fine-tuning?
A: RAG injects up-to-date knowledge without retraining the model.
Q6. What are the main components of a RAG pipeline?
A: Ingestion, chunking, embedding, vector store, retrieval, reranking, and generation.
Q7. Why is chunking important?
A: It improves retrieval accuracy by breaking documents into semantically meaningful sections.
Q8. What is embedding?
A: A numerical vector representation capturing semantic meaning of text.
Q9. How does semantic search differ from keyword search?
A: Semantic search retrieves based on meaning, while keyword search matches exact terms.
Q10. What metrics are used to evaluate retrieval quality?
A: Recall@K, Precision@K, MRR, and NDCG.
---
Vector Databases
Q11. Why use a vector database?
A: To efficiently store and search embeddings using nearest-neighbor algorithms.
Q12. What is ANN search?
A: Approximate Nearest Neighbor search trades slight accuracy for faster retrieval.
Q13. Why not store embeddings in a traditional database like MongoDB?
A: MongoDB is optimized for key-value/document retrieval, not high-dimensional similarity search.
Q14. What is cosine similarity?
A: A measure of similarity based on the angle between two vectors.
---
Agentic AI
Q15. What is an AI Agent?
A: An autonomous system that reasons, plans, uses tools, and executes actions to achieve goals.
Q16. How is Agentic AI different from a chatbot?
A: Agents can perform actions and interact with external systems; chatbots mainly generate responses.
Q17. What are the key components of an agent?
A: LLM, memory, planning, tools, and execution loop.
Q18. What is tool calling?
A: Allowing an LLM to invoke external APIs, databases, or functions.
Q19. What is agent memory?
A: Mechanisms for storing conversation history or long-term knowledge.
Q20. What is a planner agent?
A: An agent that decomposes complex tasks into executable subtasks.
---
Multi-Agent Systems
Q21. What is a multi-agent architecture?
A: Multiple specialized agents collaborating to solve a task.
Q22. How do agents communicate?
A: Through messages, shared memory, event buses, or orchestration frameworks.
Q23. When should you use multiple agents instead of one?
A: When tasks require specialized expertise or parallel execution.
Q24. What is a supervisor agent?
A: An agent that routes tasks and coordinates worker agents.
Q25. What are common multi-agent patterns?
A: Supervisor-worker, hierarchical, peer-to-peer, blackboard, and swarm.
---
Prompt Engineering
Q26. What is chain-of-thought prompting?
A: Prompting the model to reason through intermediate steps.
Q27. What is few-shot prompting?
A: Providing examples to guide model behavior.
Q28. What is prompt injection?
A: Malicious instructions intended to manipulate agent behavior.
Q29. How can prompt injection attacks be mitigated?
A: Input validation, instruction hierarchy, and tool access controls.
---
Evaluation
Q30. How do you evaluate an LLM application?
A: Measure answer quality, groundedness, latency, cost, and retrieval effectiveness.
Q31. What is groundedness?
A: The extent to which responses are supported by retrieved evidence.
Q32. Name hallucination benchmarks.
A: HaluEval, HaluBench, and RAGTruth.
---
NVIDIA-Specific Questions
Q33. What is NVIDIA NIM?
A: A containerized inference microservice for deploying AI models.
Q34. What is NVIDIA NeMo?
A: NVIDIA's framework for training, customizing, and deploying generative AI models.
Q35. What is NVIDIA TensorRT-LLM?
A: An inference optimization framework for accelerating LLMs on NVIDIA GPUs.
Q36. What is quantization?
A: Reducing numerical precision (FP16 → INT8/INT4) to improve inference efficiency.
Q37. Why use TensorRT-LLM?
A: Lower latency, higher throughput, and optimized GPU utilization.
Q38. What is KV Cache?
A: Cached attention states reused during generation to speed inference.
Q39. What is speculative decoding?
A: Using a smaller model to generate candidate tokens that a larger model verifies.
Q40. What is model parallelism?
A: Splitting a model across multiple GPUs to handle large parameter sizes.
---
Scenario-Based Questions
Q41. Your RAG system retrieves irrelevant chunks. What would you improve?
A: Chunking strategy, embeddings, metadata filtering, and reranking.
Q42. An agent repeatedly calls the same tool. How would you fix it?
A: Add memory, loop detection, and tool usage constraints.
Q43. Latency is too high in production. What optimizations can you apply?
A: Quantization, batching, KV caching, TensorRT-LLM, and smaller models.
Q44. When would you choose fine-tuning over RAG?
A: When changing model behavior or domain-specific reasoning rather than adding knowledge.
Q45. Design an agentic system for QE automation.
A: Supervisor agent → Requirement Analysis Agent → Test Case Generator → Automation Script Generator → Review Agent → Execution Agent → Reporting Agent.
These 45 questions cover roughly 80–90% of the concepts typically tested in Agentic AI, RAG, LLMs, and NVIDIA deployment-focused certifications.
Data modeling is the process of creating a visual blueprint of your business data to structure how it is collected, stored, and related. It translates real-world business rules into organized technical schemas, ensuring consistency, scalability, and efficiency in databases and data warehouses. [1, 2]
The 3 Levels of Data Modeling
Data models progress from abstract business ideas to concrete technical blueprints.
• Conceptual Data Model: The highest level. It defines what data is needed (e.g., customers, products, orders) and general business rules. It acts as a shared language between technical teams and business stakeholders.
• Logical Data Model: The middle layer. It outlines detailed data structures, attributes, and exact relationships. It is independent of any specific database management system.
• Physical Data Model: The technical implementation layer. It details how data will be physically stored in a specific system (e.g., SQL Server, Oracle, data lakehouse), including data types, indexes, and partitions. [1, 2]
Core Modeling Components
Regardless of the model, these are the fundamental building blocks:
• Entities: The "things" or concepts you want to track (e.g., Customer, Employee, Product). These typically become tables in a database.
• Attributes: The specific characteristics of an entity. For example, a Customer entity might have attributes like Name, Email, and Phone Number.
• Relationships: How entities interact with each other. For example, a Customer "places" an Order.
• Cardinality: Defines the numerical relationship between entities (e.g., One-to-One, One-to-Many, or Many-to-Many).
• Primary & Foreign Keys: Unique identifiers. A Primary Key uniquely identifies a specific record (like a Customer ID), while a Foreign Key is an attribute that links back to the primary key in another table, establishing a relationship. [1, 11, 12, 13, 14]
Key Methodologies
Depending on whether you are building a transactional application or an analytical dashboard, you'll use different modeling styles:
• Entity-Relationship (ER) Modeling: Used primarily for Operational/Transactional systems (OLTP). It focuses on reducing data redundancy through a process called normalization, ensuring every piece of data is stored in exactly one place.
• Dimensional Modeling: Used for Data Warehouses and Analytics (OLAP). It organizes data into Facts (quantitative events like sales transactions) and Dimensions (descriptive contexts like store locations or dates). [2]
Best Practices
• Understand the Business Purpose: Technical design must always serve business needs; knowing exactly what metrics the business wants to track dictates the model's structure.
• Avoid Fact-to-Fact Joins: In dimensional modeling, joining two fact tables directly often indicates an error in the model.
• Use Surrogate Keys: When building data warehouses, professionals on Reddit generally agree that using artificial, integer-based keys (surrogate keys) simplifies joining tables and managing historical data. [19, 20, 21]
AI can make mistakes, so double-check responses
[1] https://www.databricks.com/blog/what-is-data-modeling
[2] https://www.sap.com/resources/what-is-data-modeling
[3] https://www.mongodb.com/resources/basics/databases/data-modeling
[4] https://www.geeksforgeeks.org/data-analysis/data-modeling-a-comprehensive-guide-for-analysts/
[5] https://www.scribd.com/document/610970256/DATA-MODELLING
[6] https://learning.sap.com/courses/becoming-an-sap-data-architect/transforming-business-concepts-with-data-modeling
[7] https://community.sap.com/t5/technology-q-a/conceptual-logical-physical-modeling/qaq-p/11584240
[8] https://agiledata.org/essays/datamodeling101.html
[9] https://atlan.com/what-is/data-modeling-concepts/
[10] https://www.quest.com/learn/conceptual.aspx
[11] https://medium.com/business-architected/conceptual-data-modelling-start-with-business-use-cases-10b3f2670d47
[12] https://www.datamation.com/big-data/types-of-data-modeling/
[13] https://www.workday.com/en-us/perspectives/ai/intro-to-data-modeling.html
[14] https://jcsites.juniata.edu/faculty/rhodes/dbms/ermodel.htm
[15] https://www.packtpub.com/en-us/learning/how-to-tutorials/implementing-data-modeling-techniques-in-qlik-sense-tutorial
[16] https://www.sciencedirect.com/topics/computer-science/normalized-model
[17] https://atlan.com/what-is-data-modeling/
[18] https://www.red-gate.com/blog/database-design-patterns/
[19] https://www.reddit.com/r/dataengineering/comments/1onxcfo/data_modeling_what_is_the_most_important_concept/
[20] https://www.reddit.com/r/dataengineering/comments/1onxcfo/data_modeling_what_is_the_most_important_concept/
[21] https://www.reddit.com/r/dataengineering/comments/1onxcfo/data_modeling_what_is_the_most_important_concept/
You should learn these five classical machine learning topics in the following order: Linear Regression $\rightarrow$ Logistic Regression $\rightarrow$ Naive Bayes $\rightarrow$ Support Vector Machines (SVM) $\rightarrow$ Matrix Factorization. [1, 2]
This specific sequence builds a smooth mathematical and conceptual path, moving from basic lines to probabilities, optimization boundaries, and finally unsupervised matrix decompositions.
------------------------------
## 1. Linear Regression (Start Here)
* Why first: It is the foundational stepping stone of all parametric machine learning.
* Core Concepts to Learn: You will master Loss Functions (Mean Squared Error), Gradient Descent (how weights update), and Regularization (L1/L2 or Lasso/Ridge).
* Math required: Basic algebra and simple derivatives. [3, 4, 5, 6, 7]
## 2. Logistic Regression
* Why second: As established, it uses the exact same core linear combination ($wx + b$) as Linear Regression but introduces a Sigmoid function to transform outputs into probabilities.
* Core Concepts to Learn: You will learn about Classification, Log Loss (Binary Cross-Entropy), and decision boundaries.
* Math required: Logarithms and exponent math. [3, 4, 8, 9, 10]
## 3. Naive Bayes
* Why third: This shifts your perspective from optimization (finding the best line) to pure probabilistic classification.
* Core Concepts to Learn: You will learn Bayes' Theorem, conditional probability, and text classification (like spam filtering). Learning this right after Logistic Regression allows you to easily compare Discriminative models (Logistic) with Generative models (Naive Bayes).
* Math required: Basic probability and conditional probability rules. [3, 4, 11, 12, 13]
## 4. Support Vector Machines (SVM)
* Why fourth: SVMs handle classification like Logistic Regression but use a much more advanced geometric concept. Instead of finding any line that separates the data, it finds the line with the absolute maximum margin. [11, 14, 15, 16, 17]
* Core Concepts to Learn: You will learn about Hyperplanes, Margin Maximization, and the Kernel Trick (which allows the model to project flat data into higher-dimensional spaces to find non-linear separations). [18, 19, 20]
* Math required: Vector geometry and optimization theory.
## 5. Matrix Factorization (End Here)
* Why last: This is a distinct shift into Unsupervised Learning and recommendation systems. It breaks a single large matrix down into smaller component matrices to find hidden relationships. [21, 22, 23, 24]
* Core Concepts to Learn: You will learn about Latent Factors, Collaborative Filtering (how Netflix or Spotify recommend content), and Singular Value Decomposition (SVD). [21, 25, 26, 27]
* Math required: Advanced Linear Algebra (matrix multiplication, dimensions, and rank). [28, 29]
------------------------------
Would you like a curated list of hands-on projects or Python libraries to practice as you go through this learning path?
[1] [https://www.ncbi.nlm.nih.gov](https://www.ncbi.nlm.nih.gov/books/NBK597496/)
[2] [https://dokumen.pub](https://dokumen.pub/linear-algebra-and-optimization-for-machine-learning-a-textbook-1nbsped-3030403432-9783030403430.html)
[3] [https://www.linkedin.com](https://www.linkedin.com/posts/amit-shekhar-iitbhu_ai-machinelearning-activity-7415244847399460864-5c0g)
[4] [https://www.youtube.com](https://www.youtube.com/watch?v=E0Hmnixke2g&t=141)
[5] [https://cs-114.org](https://cs-114.org/wp-content/uploads/2025/01/LogisticRegression-1.pdf)
[6] [https://www.linkedin.com](https://www.linkedin.com/pulse/supervised-machine-learning-python-regression-simple-linear-maharaj-fwmjc)
[7] [https://www.craw.in](https://www.craw.in/machine-learning-interview-questions-and-answers-in-india)
[8] [https://www.youtube.com](https://www.youtube.com/watch?v=63Kr3HFECHM&t=122)
[9] [https://medium.com](https://medium.com/analytics-vidhya/math-behind-logistic-regression-that-will-make-you-a-data-scientist-2bce20ea53fd)
[10] [https://medium.com](https://medium.com/@prajun_t/linear-classifiers-7e46869844cc)
[11] [https://mrcet.com](https://mrcet.com/downloads/digital_notes/CSE/IV%20Year/MACHINE%20LEARNING%28R17A0534%29.pdf)
[12] [https://raman-singh-13-09.medium.com](https://raman-singh-13-09.medium.com/introduction-to-linear-regression-c98aca3a08f1)
[13] [https://www.cognixia.com](https://www.cognixia.com/blog/everything-you-need-to-know-about-the-naive-bayes-algorithm/)
[14] [https://link.springer.com](https://link.springer.com/protocol/10.1007/978-1-0716-3195-9_2)
[15] [https://www.geeksforgeeks.org](https://www.geeksforgeeks.org/machine-learning/machine-learning-algorithms/)
[16] [https://www.upgrad.com](https://www.upgrad.com/tutorials/ai-ml/machine-learning-tutorial/)
[17] [https://methods.sagepub.com](https://methods.sagepub.com/foundations/machine-learning)
[18] [https://www.upgrad.com](https://www.upgrad.com/blog/support-vector-machines/)
[19] [https://python.plainenglish.io](https://python.plainenglish.io/deep-dive-into-support-vector-machines-svms-for-efficient-data-classification-by-hand-8d3afce90d4a)
[20] [https://webmobtech.com](https://webmobtech.com/blog/understanding-ai-algorithms/)
[21] [https://www.sciencedirect.com](https://www.sciencedirect.com/topics/computer-science/machine-learning)
[22] [https://www.shaped.ai](https://www.shaped.ai/blog/matrix-factorization-the-bedrock-of-collaborative-filtering-recommendations)
[23] [https://saturncloud.io](https://saturncloud.io/glossary/matrix-factorization/)
[24] [https://www.lexalytics.com](https://www.lexalytics.com/blog/machine-learning-natural-language-processing/)
[25] [https://medium.com](https://medium.com/the-andela-way/foundations-of-machine-learning-singular-value-decomposition-svd-162ac796c27d)
[26] [https://www.simplilearn.com](https://www.simplilearn.com/tutorials/pyspark-tutorial/pyspark-mllib-for-ml)
[27] [https://bostoninstituteofanalytics.org](https://bostoninstituteofanalytics.org/blog/how-machine-learning-powers-recommendation-systems-netflix-amazon-spotify/)
[28] [https://wikidocs.net](https://wikidocs.net/216015)
[29] [https://vinuni.edu.vn](https://vinuni.edu.vn/data-science-skills/)
Flow Diagram of Data Lakehouse While Data Lake is excels for Machine Learning , Data warehouse is used for Business Intelligence , Data Lak...
