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Machine Learning is a branch of Artificial intelligence and it uses computing-based systems to make sense of data. ML is a subset of Artificial intelligence that allows machines to learn from data without being programmed whereas Artificial intelligence is a concept of creating intelligent machines that simulate human behavior.
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Machine Learning with Python. Anvesh Assistant Professor Dept. Topics to be covered…. What is Machine Learning?. Machine Learning with Python K. Anvesh, Dept. Topics to be covered….. What is Machine Learning? Instead of writing code, you feed data to the generic algorithm, and it builds logic based on the data given. It is a vast language with number of modules, packages and libraries that provides multiple ways of achieving a task.
They are also extensively used for creating scalable machine learning algorithms. Python implements popular machine learning techniques such as Classification, Regression, Recommendation, and Clustering. Data mining and Bayesian analysis are trending and this is adding the demand for machine learning.
Machine learning is a discipline that deals with programming the systems so as to make them automatically learn and improve with experience. To solve this problem, algorithms are developed that build knowledge from a specific data and past experience by applying the principles of statistical science, probability, logic, mathematical optimization, reinforcement learning, and control th K.
Themain purpose of machine learning is to explore and construct algorithms that can learn from the previous data and make predictions on new inputdata. Machine learning computers can change and improve their algorithms all by themselves. On the other hand, in unsupervised learning, the system attempts to find the patterns directly from the example given. When an algorithm learns from example data and associated target responses that can consist of numeric values or string labels, such as classes or tags, in order to later predict the correct response when posed with new examples comes under the category of Supervised learning.
The teacher provides good examples for the student to memorize, and the student then derives general rules from these specific examples. Unsupervised learning is used to detect anomalies, outliers, such as fraud or defective equipment, or to group customers with similar behaviours for a sales campaign. It is the opposite of supervised learning. There is no labelled data here. This kind of learning data is called unlabeled data. The program is provided feedback in terms of rewards and punishments as it navigates its problem space.
It works this way: the machine is exposed to an environment where it continuously trains itself using trial and error method. Here learning data gives feedback so that the system adjusts to dynamic conditions in order to achieve a certain objective.
The system evaluates its performance based on the feedback responses and reacts accordingly. The best known instances include self-driving cars and chess master algorithm AlphaGo. It makes use of a large amount of unlabeled data for training and a small amount of labelled data for testing. Semi-supervised learning is applied in cases where it is expensive to acquire a fully labelled dataset while more practical to label a small subset.
Here an incomplete training signal is given: a training set with some often many of the target outputs missing. There is a special case of this principle known as Transduction where the entire set of problem instances is known at learning time, except that part of the targets are missing. This is typically tackled in a supervised way. Unlike in classification, the groups are not known beforehand, making this typically an unsupervised task.
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