Saturday, June 24, 2017

Welcome to AI/ML/DL

This post will be the driving factor for all the Artificial Intelligence, Machine Learning & Deep Learning Content. 
Well here you can checkout the differences.

  1. Part 0: Grooming Sessions
  2. Part 1: Data Preprocessing 
  3. Part 2: Regression 
  4. Part 3: Classification 
  5. Part 4: Clustering 
  6. Part 5: Association Rule Learning 
  7. Part 6: Reinforcement Learning 
  8. Part 7: Natural Language Processing 
  9. Part 8: Deep Learning 
  10. Part 9: Dimensionality Reduction 
  11. Part 10: Model Selection & Boosting 


Model Selection Technique - Link, Link, Link
Dataset available at - Link

Types of Machine Learning Algorithms
  1. Regression
  2. Classification
  3. Clustering
  4. Association Analysis
  5. Dimension reduction
  6.  Reinforcement learning
First two comes under - Supervised Learning and rest four under Un-Supervised Learning.

Reference - Video Link

1). Regression:
  • Goal - Predicting numeric values
  • Used for predicting continuous values.
  • E.g.
    • Determine Sales demand for next year
    • Predict next 24 hrs rain
    • Determine likelihood of medicine effectiveness of a patient 
    • Predicting grades of students studying from 0-6 hrs
2). Classification: 
  • Goal - Predict Category (Binary - Yes/No, Dog/Cat, Pass/Fail, Male/Female, Multiple - Sunny/Cloudy/Rainy/Windy, Risk - High/Medium/Low etc.
  • When the data are being used to predict a categorical variable, supervised learning is also called classification.
  • When there are only two labels, this is called Binary classification
  • When there are more than two categories, the problems are called Multi-class classification.
3). Clustering: 
  • Goal - Organize similar items into respective groups.
  • Grouping a set of data examples so that examples in one group (or one cluster) are more similar (according to some criteria) than those in other groups. 
  • Often used to segment the whole dataset into several groups
  • Analysis can be performed in each group to help users to find intrinsic patterns.
  • E.g. 
    • Customer Segmentation - Seniors,Adults, Teenagers, Kids
    • Areas of similar topography - Desert, Grass, Water etc.
4). Association Analysis: 
  • Goal - Capture association between items.
  • E.g.
    • Identify Items purchased together
    • Identify Web pages visited together
5). Dimension reduction: Reducing the number of variables under consideration. In many applications, the raw data have very high dimensional features and some features are redundant or irrelevant to the task. Reducing the dimensionality helps to find the true, latent relationship.

6). Reinforcement learning:
Reinforcement learning analyzes and optimizes the behavior of an agent based on the feedback from the environment.  Machines try different scenarios to discover which actions yield the greatest reward, rather than being told which actions to take. Trial-and-error and delayed reward distinguishes reinforcement learning from other techniques.

Supervised learning
With supervised learning, you have an input variable that consists of labeled training data and a desired output variable. You use an algorithm to analyze the training data to learn the function that maps the input to the output. This inferred function maps new, unknown examples by generalizing from the training data to anticipate results in unseen situations.

Semi-supervised learning
The challenge with supervised learning is that labeling data can be expensive and time consuming. If labels are limited, you can use unlabeled examples to enhance supervised learning. Because the machine is not fully supervised in this case, we say the machine is semi-supervised. With semi-supervised learning, you use unlabeled examples with a small amount of labeled data to improve the learning accuracy.

Unsupervised learning
When performing unsupervised learning, the machine is presented with totally unlabeled data. It is asked to discover the intrinsic patterns that underlies the data, such as a clustering structure, a low-dimensional manifold, or a sparse tree and graph.
Hope this helps!!

Arun Manglick

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