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AI and Machine Learning Content

Intro to Machine Learning
  • • General Ledger Master
  • • What is ML
  • • Overview about sci-kit learn
  • • Types of ML
  • • ML Algorithms
  • • ML Examples
Regression Based Learning
  • • 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
  • • 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
  • • Clustering Introduction
  • • Handling Non-Numerical Data for Machine Learning
  • • K-Means with Titanic Dataset
Natural Language Processing
  • • Installing NLTK
  • • Tokenize words
  • • Tokenizing sentences
  • • Stop words with NLTK
  • • Naïve bayes classifier with NLTK
Working with OPENCV
  • • Setting up opencv
  • • Loading and displaying images
  • • Applying image filters
  • • Image Operations
  • • Image arithmetics and Logic
  • • Thresholding
  • • Color Filtering
  • • Face recognition

 

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