5 Minutes Engineering
About the teacher: 5 Minutes EngineeringTeacher के बारे में: 5 Minutes Engineering
5 Minutes Engineering: Data structures, machine learning, network security and natural language processing.
Hindi explanations that simplify engineering concepts using examples. Suits: Engineering students and beginners studying computer science.
5 Minutes Engineering के videos YUNOVA पर Affiliate Marketing, Agile and Scrum, AI Agents आदि सीखने में शामिल हैं।
YUNOVA topics taught with this channel's videosइस channel के videos से पढ़ाए जाने वाले YUNOVA topics
- Comparing on use case rather than declaring a winner · Affiliate Marketing
- Where a recommendation fits naturally · Affiliate Marketing
- Agile methods: Scrum, Kanban, XP · Agile and Scrum
- The sprint · Agile and Scrum
- Definition of done · Agile and Scrum
- Why a model needs your documents · AI Agents
- When RAG is the wrong tool · AI Agents
- What LangChain and LangGraph are · AI Agents
- Chains and runnables · AI Agents
- Building a graph of steps · AI Agents
- State and checkpoints in LangGraph · AI Agents
- Large Language Model kya hai · AI Tools & Prompting
- IaaS, PaaS and SaaS · AWS
- Regions, availability zones and edge locations · AWS
- AMIs and instance types · AWS
- Elastic IPs · AWS
- Storage classes · AWS
- What a VPC is · AWS
- Domain 3: Cloud Technology and Services · AWS
- The problem a blockchain solves · Blockchain Fundamentals
- Blocks and the chain · Blockchain Fundamentals
- Public, private and consortium blockchains · Blockchain Fundamentals
- Hash functions and SHA-256 · Blockchain Fundamentals
- Properties of a hash · Blockchain Fundamentals
- Digital signatures · Blockchain Fundamentals
- Why consensus is needed · Blockchain Fundamentals
- Proof of work and mining · Blockchain Fundamentals
- Proof of stake · Blockchain Fundamentals
- Byzantine fault tolerance · Blockchain Fundamentals
- The double-spend problem · Blockchain Fundamentals
- Nodes and the peer-to-peer network · Blockchain Fundamentals
- What a wallet is · Blockchain Fundamentals
- How a transaction moves · Blockchain Fundamentals
- Forks: soft and hard · Blockchain Fundamentals
- What a smart contract is · Blockchain Fundamentals
- Ethereum and the EVM · Blockchain Fundamentals
- Gas · Blockchain Fundamentals
- Tokens and NFTs: the technology · Blockchain Fundamentals
- dApps · Blockchain Fundamentals
- Hyperledger Fabric · Blockchain Fundamentals
- Setting up Remix · Blockchain Fundamentals
- Requirements validation and sign-off · Business Analysis
- Linked lists in C · C Programming
- Linked lists · C Programming
- The problem: it works on my machine · CI/CD & GitHub Actions
- A pipeline, a stage and a job · CI/CD & GitHub Actions
- History: from grids to the cloud · Cloud Computing Fundamentals
- IaaS, PaaS and SaaS · Cloud Computing Fundamentals
- Serverless and FaaS · Cloud Computing Fundamentals
- Public, private and hybrid cloud · Cloud Computing Fundamentals
- Community and multi-cloud · Cloud Computing Fundamentals
- What virtualisation is · Cloud Computing Fundamentals
- Hypervisors: type 1 and type 2 · Cloud Computing Fundamentals
- Virtual machines · Cloud Computing Fundamentals
- Containers and Docker: the idea · Cloud Computing Fundamentals
- Virtualisation versus containers · Cloud Computing Fundamentals
- Amazon S3 as an example · Cloud Computing Fundamentals
- Virtual private cloud · Cloud Computing Fundamentals
- Load balancing · Cloud Computing Fundamentals
- Auto scaling · Cloud Computing Fundamentals
- CDN · Cloud Computing Fundamentals
- Cloud architecture questions · Cloud Computing Fundamentals
- Virtualisation questions · Cloud Computing Fundamentals
- Cloud security questions · Cloud Computing Fundamentals
- IaaS, PaaS and SaaS · Cloud Security
- Virtual networks and subnets · Cloud Security
- Public and private subnets · Cloud Security
- Correlation concepts · Commodity Markets
- Von Neumann architecture · Computer Organisation & Architecture
- RISC and CISC · Computer Organisation & Architecture
- Levels of the memory hierarchy · Computer Organisation & Architecture
- Cache memory · Computer Organisation & Architecture
- Cache mapping: direct, associative, set-associative · Computer Organisation & Architecture
- Cache replacement and write policies · Computer Organisation & Architecture
- Main memory and virtual memory · Computer Organisation & Architecture
- How a router works · Computer Networking
- Pixels channels and image shapes · Computer Vision
- RGB grayscale and colour spaces · Computer Vision
- Image histograms · Computer Vision
- Image file formats and compression · Computer Vision
- Thresholding · Computer Vision
- Reading classification errors · Computer Vision
- Train validation test for images · Computer Vision
- Precision recall for detection · Computer Vision
- Intersection over Union · Computer Vision
- Confusion matrix for image classes · Computer Vision
- Visual error analysis · Computer Vision
- Barcode and QR detection · Computer Vision
- Running inference on a camera feed · Computer Vision
- Vision Transformer basics · Computer Vision
- Image embeddings · Computer Vision
- Zero-shot image classification concepts · Computer Vision
- Image captioning basics · Computer Vision
- Multimodal model basics · Computer Vision
- Collecting and labelling images · Computer Vision
- Handling class imbalance · Computer Vision
- Choosing image resolution · Computer Vision
- Documenting failure cases · Computer Vision
- Templates · C++
- Linked lists · C++
- The attack lifecycle · Cybersecurity Fundamentals
- Viruses, worms and trojans · Cybersecurity Fundamentals
- Ransomware · Cybersecurity Fundamentals
- Spyware and keyloggers · Cybersecurity Fundamentals
- Rootkits and botnets · Cybersecurity Fundamentals
- How malware spreads · Cybersecurity Fundamentals
- Defending against malware · Cybersecurity Fundamentals
- Symmetric encryption · Cybersecurity Fundamentals
- Asymmetric encryption · Cybersecurity Fundamentals
- Digital signatures and certificates · Cybersecurity Fundamentals
- Firewalls · Cybersecurity Fundamentals
- Security policies and awareness · Cybersecurity Fundamentals
- Missing value ko kaise handle karein · Data Analysis Fundamentals
- Matplotlib aur Seaborn se chart kaise banayein · Data Analysis Fundamentals
- OLTP vs OLAP · Data Engineering
- Batch vs streaming data · Data Engineering
- Data pipeline stages · Data Engineering
- Structured semi-structured and unstructured data · Data Engineering
- Data quality dimensions · Data Engineering
- ETL vs ELT · Data Engineering
- Cleaning and transforming records · Data Engineering
- Producer and consumer concepts · Data Engineering
- Data warehouse concepts · Data Engineering
- Loading files to object storage · Data Engineering
- Incremental pipeline design · Data Engineering
- Kafka consumer groups · Data Engineering
- Streaming sink design · Data Engineering
- Documenting lineage and ownership · Data Engineering
- The data science life cycle · Data Science Fundamentals
- pandas Series and DataFrame · Data Science Fundamentals
- Missing values · Data Science Fundamentals
- Duplicates · Data Science Fundamentals
- Data types and conversion · Data Science Fundamentals
- Outliers · Data Science Fundamentals
- Grouping and aggregation · Data Science Fundamentals
- Correlation · Data Science Fundamentals
- Univariate and bivariate analysis · Data Science Fundamentals
- Histograms and box plots · Data Science Fundamentals
- Mean, median and spread · Data Science Fundamentals
- Probability basics · Data Science Fundamentals
- The normal distribution · Data Science Fundamentals
- Hypothesis testing: the idea · Data Science Fundamentals
- Train and test split · Data Science Fundamentals
- Evaluating a model · Data Science Fundamentals
- Overfitting · Data Science Fundamentals
- Decision trees and random forests · Data Science Fundamentals
- Clustering with k-means · Data Science Fundamentals
- Feature engineering · Data Science Fundamentals
- Entities and attributes · Database Management Systems
- Relations, tuples and attributes · Database Management Systems
- Relational algebra · Database Management Systems
- BCNF · Database Management Systems
- Lossless join and dependency preservation · Database Management Systems
- ACID properties · Database Management Systems
- Transaction states · Database Management Systems
- Conflict serialisability and the precedence graph · Database Management Systems
- File organisation · Database Management Systems
- Primary and secondary indexes · Database Management Systems
- B-trees · Database Management Systems
- B+ trees · Database Management Systems
- Forward propagation · Deep Learning
- Backpropagation intuition · Deep Learning
- Train validation and test splits · Deep Learning
- Batch size epochs and learning rate · Deep Learning
- Optimizers SGD Adam and RMSprop · Deep Learning
- Overfitting and regularization · Deep Learning
- RNN and hidden state · Deep Learning
- LSTM and GRU basics · Deep Learning
- Preparing tensors and dataloaders · Deep Learning
- Reading learning curves · Deep Learning
- Evaluating precision recall and F1 · Deep Learning
- Autoencoders and latent representations · Deep Learning
- GAN generator and discriminator · Deep Learning
- Diffusion model intuition · Deep Learning
- Embeddings in generative models · Deep Learning
- Evaluating generated outputs · Deep Learning
- Choosing a baseline model · Deep Learning
- Documenting model limitations · Deep Learning
- Firewalls and security groups · DevOps
- Designing a counter step by step · Digital Electronics
- Digital to analog converter · Digital Electronics
- Product form and ergonomics · Drawing
- Why data structures and algorithms · Data Structures & Algorithms
- Arrays: traversal, insert and delete · Data Structures & Algorithms
- Singly, doubly and circular linked lists · Data Structures & Algorithms
- Stack: push, pop and balanced parentheses · Data Structures & Algorithms
- Queue, circular queue and deque · Data Structures & Algorithms
- Priority queue and heap · Data Structures & Algorithms
- Shortest paths: Dijkstra · Data Structures & Algorithms
- What DP is: overlapping subproblems · Data Structures & Algorithms
- Dry-running an algorithm on paper · Data Structures & Algorithms
- Common university questions · Data Structures & Algorithms
- Cloud Load Balancing: the idea · Google Cloud
- Scenario/sensitivity · Investing
- What 'large' means: parameters and data · LLM Fundamentals
- What an LLM can do and cannot · LLM Fundamentals
- What a token is · LLM Fundamentals
- Prompt patterns that hold up · LLM Fundamentals
- Why retrieval · LLM Fundamentals
- Retrieve, then generate · LLM Fundamentals
- Citations and grounding · LLM Fundamentals
- What machine learning is · Machine Learning with Python
- Supervised versus unsupervised learning · Machine Learning with Python
- pandas DataFrames · Machine Learning with Python
- Cleaning missing values · Machine Learning with Python
- Encoding categorical data · Machine Learning with Python
- Feature scaling · Machine Learning with Python
- Visualising data with Matplotlib and Seaborn · Machine Learning with Python
- Linear regression · Machine Learning with Python
- Multiple linear regression · Machine Learning with Python
- Polynomial regression · Machine Learning with Python
- Regularisation: Ridge and Lasso · Machine Learning with Python
- Logistic regression · Machine Learning with Python
- K-nearest neighbours · Machine Learning with Python
- Decision trees · Machine Learning with Python
- Random forest · Machine Learning with Python
- Support vector machines · Machine Learning with Python
- Naive Bayes · Machine Learning with Python
- K-means clustering · Machine Learning with Python
- Hierarchical clustering · Machine Learning with Python
- PCA · Machine Learning with Python
- Train-test split · Machine Learning with Python
- Confusion matrix · Machine Learning with Python
- Precision, recall and F1 · Machine Learning with Python
- Cross-validation · Machine Learning with Python
- Overfitting and underfitting · Machine Learning with Python
- Bias-variance questions · Machine Learning with Python
- Metric questions · Machine Learning with Python
- Algorithm comparison questions · Machine Learning with Python
- Feature engineering questions · Machine Learning with Python
- Activation functions and backpropagation · Machine Learning with Python
- Sharding: the idea · MongoDB
- What we are defending and against what · Network Security
- Common network attacks · Network Security
- IDS versus IPS · Network Security
- Signature and anomaly detection · Network Security
- Reading an alert · Network Security
- Where sensors sit on a network · Network Security
- A simple written security policy · Network Security
- WebSockets through Nginx · Nginx
- Tokens sentences and corpora · Natural Language Processing
- Stemming and lemmatization · Natural Language Processing
- Stop words and punctuation · Natural Language Processing
- Part of speech tagging · Natural Language Processing
- Named entity recognition · Natural Language Processing
- Text normalization · Natural Language Processing
- Tokenization with common libraries · Natural Language Processing
- Bag of words · Natural Language Processing
- TF-IDF · Natural Language Processing
- Word embeddings intuition · Natural Language Processing
- Text classification · Natural Language Processing
- Keyword and keyphrase extraction · Natural Language Processing
- Information extraction · Natural Language Processing
- Sequence models for text · Natural Language Processing
- Attention for language · Natural Language Processing
- Train validation test for text · Natural Language Processing
- Accuracy precision recall and F1 · Natural Language Processing
- Confusion matrix for text classes · Natural Language Processing
- Handling imbalanced text data · Natural Language Processing
- Error analysis by examples · Natural Language Processing
- Intent classification · Natural Language Processing
- Semantic similarity · Natural Language Processing
- Question answering basics · Natural Language Processing
- Building a simple NLP pipeline · Natural Language Processing
- Subword tokenization · Natural Language Processing
- Retrieval augmented generation basics · Natural Language Processing
- Defining labels and annotation guidelines · Natural Language Processing
- Avoiding data leakage in text · Natural Language Processing
- Monitoring language model errors · Natural Language Processing
- Documenting language and dataset limits · Natural Language Processing
- this and self · Object-Oriented Programming
- Validation inside a class · Object-Oriented Programming
- Why diagrams · Object-Oriented Programming
- The class diagram · Object-Oriented Programming
- Relationships in a class diagram · Object-Oriented Programming
- Sequence diagrams · Object-Oriented Programming
- The syllabus: OOP with Java or C++ · Object-Oriented Programming
- Process states and the PCB · Operating Systems
- Context switching · Operating Systems
- Inter-process communication · Operating Systems
- Peterson's solution · Operating Systems
- Semaphores · Operating Systems
- Deadlock conditions · Operating Systems
- Contiguous allocation and fragmentation · Operating Systems
- Page tables and the TLB · Operating Systems
- Segmentation · Operating Systems
- Multilevel paging · Operating Systems
- Demand paging and page faults · Operating Systems
- FIFO page replacement · Operating Systems
- Belady's anomaly · Operating Systems
- Thrashing · Operating Systems
- Scheduling numericals with Gantt charts · Operating Systems
- Inter process communication pipes · Operating Systems
- Real time operating system · Operating Systems
- What SCADA is · PLC and Industrial Automation
- Variance callouts · PowerPoint
- Bar/line/scatter choice · PowerPoint
- Small multiples concepts · PowerPoint
- Contract types · Project Management
- Running a sprint planning meeting · Project Management
- Python me arithmetic operators · Python programming
- Pandas me groupby kaise use kare · Python programming
- train_test_split se data split karna · Python programming
- Machine learning model ki accuracy kaise check kare · Python programming
- Mean, median and standard deviation · Six Sigma
- Histograms and box plots · Six Sigma
- Normal distribution · Six Sigma
- Scatter plots and correlation · Six Sigma
- Hypothesis testing basics · Six Sigma
- Phases of the SDLC · Software Engineering
- Waterfall model · Software Engineering
- Iterative and incremental models · Software Engineering
- Spiral model · Software Engineering
- V-model · Software Engineering
- Choosing a model · Software Engineering
- Functional and non-functional requirements · Software Engineering
- Requirement elicitation · Software Engineering
- Data flow diagrams · Software Engineering
- Architectural design · Software Engineering
- UML class and sequence diagrams · Software Engineering
- Verification and validation · Software Engineering
- Levels: unit, integration, system, acceptance · Software Engineering
- Black-box testing · Software Engineering
- Test cases · Software Engineering
- The Agile manifesto · Software Engineering
- Scrum: roles, sprints and ceremonies · Software Engineering
- Extreme programming · Software Engineering
- DevOps: the idea · Software Engineering
- Drawing DFDs for a case · Software Engineering
- Long-answer questions on models · Software Engineering
- Viva questions · Software Engineering
- What software testing is · Software Testing
- Error, defect, bug and failure · Software Testing
- Verification and validation · Software Testing
- Unit, integration, system and acceptance testing · Software Testing
- Functional and non-functional testing · Software Testing
- Alpha and beta testing · Software Testing
- Black-box and white-box testing · Software Testing
- Writing a test case · Software Testing
- Test summary report · Software Testing
- Histograms with a normal curve · SPSS
- Box plots · SPSS
- What statistics is · Statistics
- Population and sample · Statistics
- Graphs: histogram and ogive · Statistics
- Mean, median and mode · Statistics
- Quartiles and percentiles · Statistics
- Skewness and kurtosis · Statistics
- Conditional probability · Statistics
- Bayes' theorem · Statistics
- Random variables and expectation · Statistics
- Normal distribution · Statistics
- Z-scores and the standard normal table · Statistics
- Central limit theorem · Statistics
- Confidence intervals · Statistics
- Null and alternative hypotheses · Statistics
- Type I and type II errors · Statistics
- p-value · Statistics
- Z-test and t-test · Statistics
- Chi-square test · Statistics
- Scatter plots and correlation · Statistics
- Hypothesis test numericals · Statistics
- Regression numericals · Statistics
- Hypothesis tests in Python with SciPy · Statistics
- Multiple linear regression · Statistics
- Functional and non-functional requirements · System Design
- Client server architecture · System Design
- Load balancers · System Design
- CDN basics · System Design
- SQL vs NoSQL tradeoffs · System Design
- Replication · System Design
- Partitioning and sharding · System Design
- Caching patterns · System Design
- Message queues · System Design
- Distributed locks concepts · System Design
- Redundancy and failover · System Design
- Search service design · System Design
- Large file upload design · System Design
- Reviewing capacity assumptions · System Design
- Scatter plots · Tableau
- Usability testing a document · Technical Writing
- Micro-interactions and motion · UI/UX Design
- Planning a usability test · UI/UX Design
- Running a test with five people · UI/UX Design
- Reading the results and fixing the design · UI/UX Design
- Iterating · UI/UX Design
- What a schema is · Vibe Coding with AI
- Architecture basics · Vibe Coding with AI
- Testing · Vibe Coding with AI
- Working in an existing codebase · Vibe Coding with AI
- SQL versus NoSQL · Website Development
- Testing name resolution · Windows Administration & Server
- Explaining clearly on camera · YouTube Channel Skills