Knowledge Base
Artificial Intelligence
Algorithms
Artificial Neural Networks (ANN)
Boltzmann Machines (BM)
Boltzmann Machines (BM)
Deep Boltzmann Machines (DBM)
Restricted Boltzmann Machines (RBM)
Convolutional Neural Networks (CNN)
Convolutional Neural Networks (CNN)
Deconvolutional networks (DN)
Long-Short Term Memory (LSTM)
Gated Recurrent Units (GRU)
Long-Short Term Memory (LSTM)
Neural Turing machines (NTM)
Artificial Neural Networks (ANN)
Autoencoders (AE)
Deep Belief Network (DBN)
FeedForward Neural Networks (FF or FFNN)
Generative Adversarial Networks (GAN)
Graph Convolutional Networks (GCN)
Hopfield Networks (HN)
Radial Basis Function (RBF)
Recurrent Neural Networks (RNN)
Transformers
Bayesian Models
Naive Bayes
Bernoulli Naive Bayes
Gaussian Naive Bayes
Multinomial Naive Bayes
Naive Bayes
Averaged One-Dependence Estimators (AODE)
Bayesian Belief Network (BBN)
Bayesian Models
Bayesian Non-Parametrics (BNP)
Ensemble Methods
Random Forest(Random Decision Forest)
Extra Trees
Random Forest
AdaBoost (Adaptive Boosting)
Ensemble Methods
Gradient Boosted Regression Trees (GBRT)
Gradient Boosting (GB)
XGBoost
Hierarchical Clustering
Hierarchical Clustering
K-Means
K-Means
k-Nearest Neighbours (KNN)
k-Nearest Neighbours (KNN)
KNN in Python
Regression Models
Decision Tree Regression (DTR)
Elastic-Net
Gaussian Process Regression
Lasso Regression
Linear Regression
Logistic Regression
Polynomial Regression
Random Forest Regression (RFR)
Ridge Regression
Robust Regression (RANSAC)
SVM Regression
Rule-Based Machine Learning (RBML)
Apriori Algorithm
ASSOC Algorithm
Eclat Algorithm
FP-growth Algorithm
Inductive Learning Algorithm (ILA)
OPUS Search Algorithm
Rule-Based Machine Learning (RBML)
Sequential Covering
Support Vector Machine (SVM)
Kernel functions
Support Vector Machine (SVM)
SVM Classification
Tree-based Models
C4.5 and C5.0
Chi-squared Automatic Interaction Detection (CHAID)
Classification and Regression Tree (CART)
Conditional Decision Trees
Decision Stump
Decision Trees
Iterative Dichotomiser 3 (ID3)
M5
Tree-based Models
Algorithms
BIRCH
DBSCAN
Deep Q Learning (DQN)
Gaussian mixture model (GMM)
Hidden Markov Model (HMM)
Linear Discriminant Analysis (LDA)
Mean-Shift
OPTICS
Principal Components Analysis (PCA)
Quadratic Discriminant Analysis
Recursive Feature Elimination (RFE)
Applications
Machine Vision
Diffusion Models
Image Generation
Intelligent Document Processing (IDP)
Machine Vision
Optical Character Recognition (OCR)
Natural Language Processing (NLP)
Algorithms and Tasks
Bag of Words
Bag of Words
Paraphrase Generation
Paraphrase Generation
Question answering
Question answering
Relation prediction
Relation prediction
Relationship extraction
Relationship extraction
Semantic textual similarity
Semantic textual similarity
Sentiment Analysis
Sentiment Analysis
Shallow syntax
Shallow syntax
Simplification
Simplification
Stance detection
Stance detection
Summarization
Summarization
Taxonomy learning
Taxonomy learning
Temporal processing
Temporal processing
Text Classification
Text Classification
Word Embedding Techniques
FastText
FastText
Probabilistic FastText
Word2Vec
GloVe
Neural Probabilistic Model
Word2Vec
Co-Occurrence Matrix
Continuous Bag of Words (CBOW)
Continuous Skip-Gram
ELMo
Embedding Layer
One-Hot Encoding (Word Count Vectorizing)
Poincare Embeddings (Hierarchal Representations)
TF-IDF Transform
Word Embedding Techniques
Algorithms and Tasks
Information Extraction (IE)
Natural Language Generation (NLG)
Natural Language Patterns
Topic Modelling
Applications
Applications
Chatbots
Grammar Checkers
Grammatical Error Correction
Machine Translation
Rephrasing - Paraphrasing
Spell checking
Text Mining
Knowledge Systems
Automated Knowledge Base
Commonsense Knowledge Bases
Head Entity
Knowledge base, Knowledge Graph, and Ontologies
Knowledge Fusion
Knowledge Systems
Long Tail
Organizational Data(firmographic or firm demographic)
Linguistics
Discourse
Lexical Semantics
Lexicon
Morphology
Phonetics
Phonology
Phrase Structure Grammar
Semantics
Syntax
Natural Language Understanding (NLU)
Intent Recognition
Named Entity Linking (NEL)
Natural Language Inference (NLI)
Natural Language Understanding (NLU)
Pragmatics
Semantic Role Labelling (SRL)
Slot Filling
NLP Concepts
Chunking
Corpus
Document
Induction
Inductive Reasoning
Large Language Model (LLM)
Multimodal NLP
N-grams
NLP Concepts
Stop Words
Term Frequency
Text Analytics
Text structure analysis
Token
Vocabulary
Ontologies
Ontology Concepts
Attempto Controlled English
Concept-Based Representation
Ontology Learning
Ontology Learning
Ontologies
Pre-Processing
Constituency Parsing
Context-free Grammar(CFG)
Coreference Resolution
Dependency Parsing
Entity Linking (EL)
Lemmatizing
Named Entity Recognition (NER)
Part-of-speech Tagging (POS)
Positional Encoding
Pre-Processing
Semantic Parsing
Stemming
Tokenization
Word Embedding
Word Sense Disambiguation
Benchmarks
Challenges in NLP
Libraries
Natural Language Processing (NLP)
NLP WorkFlow
Recommender Systems
Collaborative filtering
Content-based filtering System
Evaluating recommender systems
Recommender Systems
Applications
Checklists and Processes
Checklists and Processes
Steps in mastering AI
Concepts
Evaluation Metrics
Confusion matrix
Cross Entropy
Features
Feature Creation
Feature Engineering
Feature Selection
Feature Transformation
Objective function
Cost Function
Stochastic Gradient Descent(SGD)
Loss Functions
Hinge Loss
Huber Loss
Mean Absolute Error Loss(L1 regularization)
Mean Square Error Loss(L2 regularization)
Cost Function
Loss Functions
Responsible AI
eXplainable Artificial Intelligence (XAI)
Rule-Based System
Artificial Immune Systems (AIS)
Association Rule Learning
Association Rule Mining
Association Rules
Associative Classifier (AC)
Learning Classifier Systems (LCS)
Rule Extraction
Rule Induction
Rule-Based Classification
Rule-Based System
Active Learning
Concepts
Curriculum Learning
Embedding
Evaluation Metrics
Expert System
Generative AI
Knowledge Graphs (KGs)
Markov chains (MC)
Objective function
Optimization Problem
Pattern Recognition
PipeLine
Post Training
Responsible AI
Machine Learning
Machine Learning Concepts
Activation Function
Activation Function
Argmax
Binary Step Activation Functions
Elliot
Linear Activation Function
Non-linear Activation Functions
ReLU(Rectified Linear Unit)
Sigmoid
Softmax
Softplus
Swish
Tanh(Hyperbolic Tangent)
Automated Machine Learning (AutoML)
Automated Machine Learning (AutoML)
Neural Architecture Search
Bias and Variance (ML)
Bias and Variance (ML)
Deep learning
Deep learning
Backpropagation
Cross-Validation
Data Splitting
Diffusion Models
Discriminative Models
Generative Models
Hyperparameter Tuning
Machine Learning Concepts
Maxout
Model Evaluation
Model Resource Management
Model Selection
Overfitting
Underfitting
MLOps
CRISP-ML(Q)
Machine Learning Development Lifecycle (MLDLC)
Machine Learning Pipeline
Machine Learning Workflow
MLOps
Deep Reinforcement Learning (Deep RL)
Machine Learning
Reinforcement Learning
Self-supervised Learning
Semi-supervised Learning
Supervised Machine Learning
Unsupervised Machine Learning
Mathematics of AI
Algebra
Matrices
Matrices
Matrix Multiplication in Python
Algebra
Dot Product
Relational algebra
The Hessian Matrix
The Jacobian Matrix
Calculus
Calculus
Differential Calculus
Extrema
Gradient
Integral Calculus
Statistics
Descriptive Statistics
Descriptive Statistics
Probability
Bayesian Statistics
Bayes Theorem
Bayesian Statistics
Markov Chain Monte-Carlo (MCMC)
Variational Inference
Conditional Probability
Probability
Statistics Concepts
Bias and Variance(Statistics)
Bias and Variance(Statistics)
Causation
Confidence Interval
Continuous Random Variable
Cross-Entropy
Cross-Validation (Statistics)
Discrete Random Variable
Distribution Curves
Expectation
Hypothesis
Hypothesis Testing
Kurtosis
Log of the distribution
Mean
Median
Mode
Monotonic Relationship
Outliers
P-value
Percentiles
Population
Probability Density Function (PDF)
Random Experiment
Random Variables
Sample
Sample Space
Standard Deviation
Standard Error
Statistically Significant Effect
Statistics Concepts
T-Distribution
T-test
T-Value
Test Statistic
Variance
Z-test
Inferential Statistics
Probability Distribution
Statistics
Mathematics of AI
Regression Mathematics
Tasks & Techniques
Classification
Classification
Clustering
Clustering
Dimensionality Reduction
Dimensionality Reduction
Feature Selection Techniques
Feature Selection Techniques
Regression
Regression
Bagging (Technique)
Boosting (Technique)
Regularization (Technique)
Tips & Tricks
Selecting Feature Selection Method
Tips & Tricks
When to use, or not use Machine Learning
AI for Business
Artificial Intelligence
Optimization
BlockChain
Concepts
Blockchain Architecture
Chain of Blocks
Concepts
Consensus
Decentralized Application (DApp)
Distributed Ledger
Mining
Node
Nonce
Plasma Chain
Smart Contract
Zero-Knowledge Proof
Consensus Algorithms
Consensus Algorithms
Delegated Proof-of-Stake (DPoS)
Proof of Work (PoW)
Proof-of-Authority (PoA)
Proof-of-Importance (PoI)
Proof-of-Stake (PoS)
Blockchain
Business & Startups
Business Concepts
Balance Sheet
Bootstrapping
Business Concepts
Business Planning Questionnaire
Cash Flow Statement
Cash management & Accounting
Competition
Crowdfunding
Fund Raising
Income Statement
Mission Statement
OKR Framework
Point of Sale (POS)
Profit Margin
Seed Capital
Business Skills
Business writing
Startups
Startup Ideas
Startup Ideas
Types of Startups Ideas
Validating Startup Ideas
Startup Metrics
Churn rate
Retention rate
Startup Metrics
Raising Capital
Startup Failure
Startup Marketing
Startup Mistakes
Startup Survival
Business & Startups
Business Ideas
Business Plan
Market Research
Starting a Business - The Process
Cyber Security
Application Security
Secure Software Development Life Cycle(S-SDLC)
Security CI-CD
API Security
Data Flow Diagram
Secure Software Development Life Cycle(S-SDLC)
Security QA
Source Code Scan
Cryptography
Key & Secret Management
HMS
Vaulting
Certificate Management
Encryption Standards
Key & Secret Management
Data Protection
Data Leakage Prevention
Insider Threats
Malware
Adware
Dropper
File infector
Malware
Ransomware
Spyware
Trojan (trojan horse)
Worm
Network Security
DDOS Prevention
Physical Security
IOT Security
Privacy & Personal Security
Privacy & Personal Security
Self-hosted VPN Server
Risk Assessment
Penetration Test(PenTest)
Dynamic Application Security Testing
Infrastructure
Social Engineering
Threat Inteligence
Internal
Indicators of Compromise(IOC)
Intel-Sharing
Training (User Education)
External
3rd party risk assessment
Risk Monitoring Services
Vulnerability
Security Operations(SecOps)
Active Defense
Incident Response
Security Information and Event Management (SEIM)
Security Operations(SecOps)
Security Orchestration, Automation, and Response(SOAR)
Thread Hunting
Vulnerability Management
Web Security
Web App - Pen Testing Tools
Web Security
Web Server
Cloud Security
Cyber Security
General Laws of Cybersecurity
Mobile App
Penetration Testing(PenTest)
Phishing
Reconnaissance (Information Gathering)
Security Control
Security Mistakes
Data Science
Data Analysis
Data Analysis
Data Analysis Methods
Data Analysis Process
Data Mining
Data Integration
Data Mining
Data Preparation
Data Cleaning
Data Preparation
Data Reduction
Data Transformation
Label Encoding
Missing Values
One-Hot Encoding
Data Science Career
Data Science Career
Data Science Career VS Academia
Data Science Skills
Learning Data Science
Data Science Concepts
Bias(DS)
Data Dictionary
Data literacy
Data Pipeline
Data Quality
Data Science Concepts
Data Structures
Data Types
Data Science Lifecycle
Hierarchy of Needs
Hierarchy of Needs
Exploratory Data Analysis (EDA)
Tools and Technologies
Numpy
Pandas
Visualization
Data Visualization Libraries
Data Visualization Libraries
Matplotlib
Pandas for Data Visualization
Seaborn
Visualization
Big Data
Business Analytics
Business Intelligence (BI)
Business Understanding
Data Science
Data Science Lifecycle
Fundamentals
Development
Performance Optimization
Performance Optimization
Python
Cheat Sheet
Cheat Sheet
Development Environment
Conda
Cheatsheet
Conda
conda install
conda list
conda update
Development Environment
pip freeze
pip vs anaconda
Python Virtual Environment
Python Windows Shortcuts
Django
Django Patterns
Django Pagination
HTMX - Forms in Bootstrap
HTMX - Partial page load
Packages
django_components
Ajax & HTMX Compatibility
django_components
django-debug-toolbar
django-extensions
django-mptt
HTMX
Snippets
Create Admin
upload_to in FileField
Django
Django - Project Shortcuts for Windows
Django Commands
Django Model Managers and Querysets
Django Project Structure
Django Settings
Django Settings Best Practices
Performance Improvements
Setup a Django Project
Templates
Module Review
Apache Kafka
Flet - Flutter apps in python
Module Review
NumPy
Scikit-Learn
SciPy
TensorFlow
Snippets
Basic Python
Accessing the elements of the list
Accessing values and keys in dictionary
Case capitalization
Changing elements in a list
Creating a dictionary
Creating a list
Data Classes
Enumerate function
Enumeration class
Filter function
Handling Sets
Handling Tuples
List and strings
List Comprehension
List Methods
Map function
Nested lists
Operations on list
Slicing a list
Solving counting problems with dictionary
Split text
The setdefault() method
Traversing a dictionary
Traversing a list
Zip Function
Snippets
Python
Python Settings Best Practices
Python Tips
Software Engineering
Objected Oriented Programming(OOP)
SOLID Principles
Software Business Models
DaaS
IaaS
PaaS
SaaS
Software Engineering Methodologies
Agile
Lean
Waterfall
Architecture Characteristics
Design Patterns
DevOps
Engineering Principles
Objected Oriented Programming(OOP)
Refactoring
Software Life Cycle
Task Management
UI UX
Responsive Design
Code Quality
Development
Query Optimization
Software Engineering
Tips, tricks and habits
UI-UX
Confusion Matrix
Confusion matrix is used to measure classification metrics:
Confusion matrix/confusion-matrix.jpg
True Positives(TP):
Number of samples that are correctly classified as positive, and their actual label is positive.
False Positives (FP):
Number of samples that are incorrectly classified as positive, when in fact their actual label is negative.
True Negatives (TN):
Number of samples that are correctly classified as negative, and their actual label is negative.
False Negatives (FN):
Number of samples that are incorrectly classified as negative, when in fact their actual label is positive.
Interactive Graph
Table Of Contents
Confusion Matrix