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Machine learning

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Machine learning

  1. 1. Machine Learning By Rajat Kumar
  2. 2. Overview Introduction What is the need ? Machine Learning Algorithms Applications Conclusions
  3. 3. Introduction Thanks to the likes of Google, Amazon, and Facebook, the terms artificial intelligence (AI) and machine learning have become much more widespread than ever before.They are often used interchangeably and promise all sorts from smarter home appliances to robots taking our jobs. But while AI and machine learning are very much related, they are not quite the same thing.
  4. 4. Introduction continued… • What is Artificial Intelligence ? Artificial intelligence (AI) is intelligence exhibited by machines. In computer science, the field of AI research defines itself as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of success at some goal. • What is Machine Learning ? Machine Learning is a subset of Artificial Intelligence that, according to Arthur Samuel in 1959, gives "computers the ability to learn without being explicitly programmed.” It evolved from the study of pattern recognition and computational learning theory in artificial intelligence.
  5. 5. Introduction continued… • Difference between AI and ML: AI is basically the intelligence – how we make machines intelligent, while machine learning is the implementation of the compute methods that support it. AI is the science and machine learning is the algorithm that make the machines smarter. So , Machine Learning is an approach to achieve Artificial Intelligence. • Definition of Machine Learning : In 1997, Tom Mitchell gave a “well-posed” definition of Machine Learning : “A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance onT, as measured by P, improves with experience E.”
  6. 6. Introduction continued… • So what is actually Machine Learning ? The machine learning approach, is to say, instead of writing each program by hand for each specific task, for particular task, we collect a lot of examples, and specify the correct output for given input. A machine learning algorithm then takes these examples and produces a program that does the job. The program produced by the linear algorithm may look very different from the typical handwritten program. For example, it might contain millions of numbers about how you weight different kinds of evidence. If we do it right, the program should work for new cases just as well as the ones it's trained on. And if the data changes, we should be able to change the program runs very easily by retraining it on the new data.
  7. 7. Introduction continued… • Examples of Machine Learning : Some examples of the things that are best done by using a learning algorithm are recognizing patterns, so for example objects in real scenes, or the identities or expressions of people's faces, or spoken words. Predicting future stock prices or currency exchange rates are also possible by using ML. Other examples of machine learning problems include, “Is this cancer?”, “What is the market value of this house?”, “Which of these people are good friends with each other?”, “Will this rocket engine explode on take off?”, “Will this person like this movie?”, “Who is this?”, “What did you say?”, and “How do you fly this thing?”. All of these problems are excellent targets for an ML project, and in fact ML has been applied to each of them with great success.
  8. 8. What is the need ? ML solves problems that cannot be solved by numerical means alone. Machine learning is needed for tasks that are too complex for humans to code directly. Some tasks are so complex that it is impractical, if not impossible, for humans to work out all of the nuances and code for them explicitly. So instead, we provide a large amount of data to a machine learning algorithm and let the algorithm work it out by exploring that data and searching for a model that will achieve what the programmers have set it out to achieve.
  9. 9. Algorithms of ML There are 3 types of Machine Learning techniques : • 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. • 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. • Reinforcement learning: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle or playing a game against an opponent).The program is provided feedback in terms of rewards and punishments as it navigates its problem space.
  10. 10. Algorithms All these 3 techniques are used in various Machine Learning Algorithms . We will discuss some of them : 1. Linear Regression : In simple linear regression, we predict scores on one variable from the scores on a second variable. Linear regression consists of finding the best-fitting straight line through the points. The best-fitting line is called a regression line.
  11. 11. Algorithms 2. Logistic Regression : Logistic Regression is used to estimate discrete values (usually binary values like 0/1) from a set of independent variables. It helps predict the probability of an event by fitting data to a logit function.
  12. 12. Algorithms 3. DecisionTree : One of the most popular machine learning algorithms in use today, this is a supervised learning algorithm that is used for classifying problems. It works well classifying for both categorical and continuous dependent variables. In this algorithm, we split the population into two or more homogeneous sets based on the most significant attributes/ independent variables.
  13. 13. Algorithms 4. K-Means Clustering : This is an unsupervised algorithm which solves clustering problems. Data sets are classified into a particular number of clusters (let's call that number K) in such a way that all the data points within a cluster are homogenous, and heterogeneous from the data in other clusters. How K-means forms clusters: • The K-means algorithm picks k number of points, called centroids, for each cluster • Each data point forms a cluster with the closest centroids i.e. k clusters. • It now creates new centroids, based on the existing cluster members. • With these new centroids, the closest distance for each data point is determined. This process is repeated until the centroids do not change.
  14. 14. Applications There are numerous applications of machine learning. It's actually hard to realize how much machine learning has achieved in real world applications. Machine learning is generally just a way of fine tuning a system with tunable parameters. It is a way of making a system better with examples, usually in a supervised or unsupervised manner. Machine learning is normally applied in the offline training phase .Thus machine learning is used to improve the following applications :- 1. Face detection:The face detection feature in mobile cameras is an example of what machine learning can do. Cameras can automatically snap a photo when someone smiles more accurately now than ever before because of advances in machine learning algorithms.
  15. 15. Applications 2. Face recognition:This is where a computer program can identify an individual from a photo. Facebook uses this feature for automatically tagging people in photos where they appear. Advances in machine learning means more accurate auto-face tagging soft wares . 3. Image classification: A good example is the application of deep learning to improve image classification or image categorization in apps such as Google photos. Google photos would not be possible without advances in deep learning.
  16. 16. Applications 4. Speech recognition: Another good example is Google now. Improvements in speech recognition systems has been made possible by machine learning specifically deep learning.
  17. 17. Applications 5. Google: Google defines itself as a machine learning company now. It is also a leader in this area because machine learning is a very important component to it's core advertising and search businesses. It applies machine learning to improve search results and search suggestions.
  18. 18. Applications 6. Fraud Detection: Machine learning is getting better and better at spotting potential cases of fraud across many different fields. PayPal, for example, is using machine learning to fight money laundering.The company has tools that compare millions of transactions and can precisely distinguish between legitimate and fraudulent transactions between buyers and sellers. 7. Anti-spam: Machine learning is also used to train better anti-spam software systems. 8. Weather forecast: Machine learning is applied in weather forecasting software to improve the quality of the forecast.
  19. 19. Applications 9. Smart Cars: IBM recently surveyed top auto executives, and 74% expected that we would see smart cars on the road by 2025. A smart car would not only integrate into the Internet ofThings, but also learn about its owner and its environment. It might adjust the internal settings — temperature, audio, seat position, etc. — automatically based on the driver, report and even fix problems itself, drive itself, and offer real time advice about traffic and road conditions. 10.Healthcare: It is used to diagnose deadly diseases (e.g. cancer) based on the symptoms of patients and tallying them with the past data of similar kind of patients.
  20. 20. Conclusion We have a simple overview of some techniques and algorithms in machine learning. Furthermore, there are more and more techniques that apply machine learning as a solution. In the future, machine learning will play an important role in our daily life. Clearly, Machine Learning is an incredibly powerful tool. In the coming years, it promises to help solve some of our most pressing problems, as well as open up whole new worlds of opportunity.

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