AI AND MACHINE LEARNING CONTENT
- General Ledger Master
- What is ML
- Overview about sci-kit learn
- Types of ML
- ML Algorithms
- ML Examples
- Simple Regression
- Linear Regression
- Features and Labels
- Training and Testing
- How to program the Best Fit Slope
- How to program the Best Fit Line
- R Squared and Coefficient of Determination Theory
- Classification Intro with K Nearest Neighbors
- Applying K Nearest Neighbors to Data
- Euclidean Distance theory
- Creating a K Nearest Neighbors Classifier from scratch
- Testing our K Nearest Neighbors classifier
- Clustering Introduction
- Handling Non-Numerical Data for Machine Learning
- K-Means with Titanic Dataset
- Installing NLTK
- Tokenize words
- Tokenizing sentences
- Stop words with NLTK
- Naïve bayes classifier with NLTK
- Setting up opencv
- Loading and displaying images
- Applying image filters
- Image Operations
- Image arithmetics and Logic
- Thresholding
- Color Filtering
- Face recognition
