Upgrade to Pro — share decks privately, control downloads, hide ads and more …

Getting started with Machine Learning

Getting started with Machine Learning

Types of machine learning, features, training data, labels.

Krunal Kapadiya

December 07, 2016
Tweet

More Decks by Krunal Kapadiya

Other Decks in Technology

Transcript

  1. • What is Regression ? • What is Classification of

    data ? • What is Cluster ? Before we started
  2. What is Machine Learning ? A computer program is said

    to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E. - Tom M Mitchell
  3. Supervised Learning The computer is presented with example inputs and

    their desired outputs, given by a "teacher", and the goal is to learn a general rule that maps inputs to outputs.
  4. Unsupervised Learning No labels are given to the learning algorithm,

    leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning).
  5. A computer program interacts with a dynamic environment in which

    it must perform a certain goal (such as driving a vehicle), without a teacher explicitly telling it whether it has come close to its goal. Another example is learning to play a game by playing against an opponent. Reinforcement Learning
  6. Neural Network Neural networks (also referred to as connectionist systems)

    are a computational approach which is based on a large collection of neural units loosely modeling the way a biological brain solves problems with large clusters of biological neurons connected by axons.
  7. Weight Texture Label 150g Bumpy Orange 170g Bumpy Orange 140g

    Smooth Apple 130g Smooth Apple Feature Feature Training Data
  8. Weight Texture Label 150g Bumpy Orange 170g Bumpy Orange 140g

    Smooth Apple 130g Smooth Apple Feature Feature Examples Training Data
  9. 1. Import Dataset 2. Train Classifier 3. Predict Label for

    new flower 4. Visualize the tree Goals