Welcome to AI hubs
Artificial Intelligence
Data Analysis and Statistical Inference
- Introduction to Data
- Data Basics
- Observational studies and experiments, sampling and source bias
- Experimental Design and Random Sample Assignment
- Visualizing Numerical Data
- Visualizing Categorical Data
- Measure of Center
- Measure of Spread
- Robust Statistics
- Data Transformation
- Introduction to Inference
- Probability and Distribution
- Disjoint Events, Non-disjoint Events and Sample Space
- Independence
- Disjoint vs. Independence
- Conditional Probability
- Probability Tree
- Bayesian Inference
- Normal Distribution
- Working with the Normal Distribution
- Standardized score - Z score
- Percentile
- Evaluating the Normal Distribution
- Binomial Distribution
- Introduction to inference
Machine Learning
- Confusion Matrix
- Supervised & Unsupervised Learning Modes
- Unsupervised Learning
- Fuzzy c-Means
- k-Means algorithm
- Hierarchical clustering algorithm
- Self-organising Feature Mapping (SOM)
- Supervised Learning
- k-Nearest Neighbours
- Artificial Neural Network (ANN)
- Decision Tree
- Support Vector Machine
- Naïve Bayes
- Association-rule Learning
- What is Artificial Intelligence
- AI Glossary
Latest Post
- Dependency injection
- Directives and Pipes
- Data binding
- HTTP Get vs. Post
- Node.js is everywhere
- MongoDB root user
- Combine JavaScript and CSS
- Inline Small JavaScript and CSS
- Minify JavaScript and CSS
- Defer Parsing of JavaScript
- Prefer Async Script Loading
- Components, Bootstrap and DOM
- What is HEAD in git?
- Show the changes in Git.
- What is AngularJS 2?
- Confidence Interval for a Population Mean
- Accuracy vs. Precision
- Sampling Distribution
- Working with the Normal Distribution
- Standardized score - Z score
- Percentile
- Evaluating the Normal Distribution
- What is Nodejs? Advantages and disadvantage?
- How do I debug Nodejs applications?
- Sync directory search using fs.readdirSync