Introduction to Machine learning ppt
- 1. NAME : SHUBHAM SHIRKE TOPIC : INTRODUCTION TO MACHINE LEARNING EMAIL ID : SHUBHAMSHIRKE31@GMAIL.C OM
- 2. AGENDA • Introduction • Basics • Classification • Clustering • Regression • Use-Cases
- 3. ABOUT • subfield of Artificial Intelligence (AI) • name is derived from the concept that it deals with “construction and study of systems that can learn from data” • can be seen as building blocks to make computers learn to behave more intelligently • It is a theoretical concept. There are various techniques with various implementations.
- 4. TERMINOLOGY • Features – The number of features or distinct traits that can be used to describe each item in a quantitative manner. • Samples – A sample is an item to process (e.g. classify). It can be a document, a picture, a sound, a video, a row in database or CSV file, or whatever you can describe with a fixed set of quantitative traits. • Feature vector – is an n-dimensional vector of numerical features that represent some object. • Feature extraction – Preparation of feature vector – transforms the data in the high-dimensional space to a space of fewer dimensions. • Training/Evolution set – Set of data to discover potentially predictive relationships.
- 5. APPLE (LEARNING AND TRAINING) • Features : red Features : Sky blue Features : Green • Type : fruit Type : Logo Type : fruit • shape : etc. Shape : etc. Shape : etc.
- 6. WORKFLOW
- 7. SOFTWARE USED FOR MACHINE LEARNING • Tensor Flow • Shogun • Apache Mahout • Apache Spark Mllib • Oryx 2
- 8. CATEGORIES • Supervised Learning • Unsupervised Learning • Semi-Supervised Learning • Reinforcement Learning
- 9. SUPERVISED LEARNING • The correct classes of the training data are known as
- 10. UNSUPERVISED LEARNING • The correct classes of the training data are not known as
- 11. SEMI-SUPERVISED LEARNING • A Mix of Supervised and Unsupervised learning
- 12. REINFORCEMENT LEARNING • It allows the machine or software agent to learn its behavior based on feedback from the environment
- 13. MACHINE LEARNING TECHNIQUES TECHNIQUES • classification: predict class from observations • clustering: group observations into •“meaningful” groups • regression (prediction): predict value from observations
- 14. CLASSIFICATION • Classify a document into a predefined category. • Documents can be text, images • Popular one is Naive Bayes Classifier. • Steps: – Step1 : Train the program (Building a Model) using a training set with a category for e.g. sports, cricket, news, – Classifier will compute probability for each word, the probability that it makes a document belong to each of considered categories – Step2 : Test with a test data set against this Model
- 15. • Clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar to each other • Objects are not predefined • For e.g. these keywords – “man’s shoe” – “women’s shoe” – “women’s t-shirt” – “man’s t-shirt” – can be cluster into 2 categories “shoe” and “t-shirt” or • “man” and “women” • Popular ones are K-means clustering and Hierarchical clustering
- 16. K-MEANS CLUSTERING • partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. • • http://pypr.sourceforge.net/kmeans.html
- 17. HIERARCHICAL CLUSTERING • Method of cluster analysis which seeks to build a hierarchy of clusters. • There can be two strategies •– Agglomerative: • This is a "bottom up" approach: each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy. • Time complexity is O(n^3) – Divisive: • This is a "top down" approach: all observations start in one cluster, and splits are performed recursively as one moves down the hierarchy. • Time complexity is O(2^n) • http://en.wikipedia.org/wiki/Hierarchical_clustering
- 18. REGRESSION • Is a measure of the relation between the mean value of one variable (e.g. output) and corresponding values of other variables (e.g. time and cost) • regression analysis is a statistical process for estimating the relationships among variables. • Regression means to predict the output value using training data. • Popular one is Logistic regression •(binary regression) • http://en.wikipedia.org/wiki/Logistic_regression •
- 19. USE-CASES (CONTD.) • Text Summarization - Google News • Rating a Review/Comment: Yelp • Fraud detection : Credit card Providers • Decision Making : e.g. Bank/Insurance sector • Sentiment Analysis • Speech Understanding – iPhone with Siri • Face Detection – Facebook’s Photo tagging •35