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The Essentials of Machine Learning

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Machine Learning is the unknown friend of ours that has crept in to our daily lives without most of us knowing it. But to enthusiasts of the more technical aspects of smart devices or even to those with a passing interest in the subject the term Machine Learning is no way alien. But if you are interested in knowing what it means and entails to a little detail then this video is a must watch. Read more at: http://www.dexlabanalytics.com/

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The Essentials of Machine Learning

  1. 1. As technology marches on with its conquest of miracles we find Machine Learning becoming more and more ubiquitous. From smartphones to chatbots (remember the recent controversy surround an AI chatbot of Microsoft) machine learning is fast becoming a part and parcel of our everyday lives in which technology plays a pivotal role. Machine Learning
  2. 2. What is Machine Learning? Machine Learning may present itself in the humblest of fashions like when cameras of smart phones are able to recognize faces of people. There are even simpler examples of our day to day interactions with machine learning. Suppose you have added the names and phone numbers of friends and acquaintances in your. And what happens when you start to dial a number the suggested contacts are displayed automatically. Here you unknowingly are teaching the phone to detect keywords and patterns.
  3. 3. Machine Learning Algorithms and Their Types • There are number of ways in which Machine learning may take place and these are known as Machine Learning Algorithms. • Three many types of Machine Learning Algorithms that dominate this evolving field. They are: • Supervised Learning • Semi-supervised Learning • Unsupervised Learning
  4. 4. Important Keywords in Machine Learning • However, before we move on with details of machine learning algorithms there are a number of keywords which we should be well aware of. These are: • Training Data • Bayes Theorem and • K-Means
  5. 5. Training Data Training Data: The data made available to the machine through input is known as Training Data as this data is used by the machine to further develop patterns. It consists of known labels technically called categorical variables like, ratings, gender and the like.
  6. 6. Bayes Theorem Bayes Theorem: According to the Bayes Theorem, the product of probability of occurrence of event B and occurrence of event A when B has already occurred is equal to product of event A and occurrence of B when A has already occurred
  7. 7. K-Means • Through the method of K- Means clustering based on Euclidean distance which is • Here K is the no. of clusters.
  8. 8. Supervised Learning Preparation of models are done through a process of training where the machine makes predictions and are corrected when they err. This process of training goes on till desirable levels of accuracy are achieved on the training data. Labels in training data are also present.
  9. 9. Example of Supervised Learning To cite an example, past GRE scores and GPA of students in indicate that a score of 720 and a GPA of 4.2 will help them secure admission to good colleges. Inputting scores result in you being given feedback regarding whether you are rejected or selected. As abnormalities are present, this learning process is continuous. They also make use of Logistic Regression.
  10. 10. Unsupervised Learning In case of unsupervised learning there is an absence of input data and the results are known beforehand. The preparation of the model occurs through deducing structures that are present in the input data. One of the goals may be to chance upon general rules. This process may occur in a mathematical manner so that redundancy is reduced or data is organized by similarity.
  11. 11. Example of Unsupervised Learning To explain Unsupervised Learning we may cite the following example- You are engaged in cluster analysis in order to figure out the particular data points that form part of particular clusters. When a new data point is introduced the machines deduces it to be part of one of the clusters.
  12. 12. Semi-Supervised Learning Here the input data is a blend of examples that may or may not have labels. A desired prediction problem is present but the model needs to learn the structures required for organizing data in addition to making predictions.
  13. 13. Thank You DexLab Analytics would like to thank the viewers of this presentation for going through the same. For details visit: http://www.dexlabanalytics.com

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