General - Machine Learning (ML)

Main Ingredients for Machine Learning

1. Data
2. Training
3. Hardware

CPU - Central Processing Units

• Used to all the tasks that are performed on a computer. GPU - Graphics Processing Unit
• Dedicated high performance chip used for machine learning

Features

• Input Data Label
• Output Training DataSet
• A large group of lableled examples. ML Training Phase
• ML Trained Model

Inference

Under-fitting Model

• Reasons:
  1. Too Simple Model
  2. Training data is not enough Over-fitting Model
• Reasons:
  1. Training dataaset is a SAMPLE
  2. Too Complex Model

Classification of ML Systems:

1. Supervised Learning
   • Classification Classifier Support Vector Machines (SVM’s)
   • Regression Linear Regression Logistic Regression Polynomial Regression
2. Unsupervised Learning
   • Clustering
   • Task of identifying similar instances with shared attributes in a dataset and group them together into clusters.
   • The output of the algorithm would be a set of labels assigning each data point to one of the identified clusters. - Dimension Reduction
3. Reinforcement Learning