Start Date: 07/05/2020
Course Type: Common Course |
Course Link: https://www.coursera.org/learn/practical-machine-learning
One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.
Practical Machine Learning This course will teach you the basic concepts of machine learning, with an emphasis on applications, machine learning models, and deep learning.Week 1 Week 2 Week 3 Week 4 Practical Machine Learning in NN This course aims at explaining the most important concepts and applications for model generation and optimization: neural networks, recurrent neural networks, layer 2 layers, variational inference, Naive Bayes, and gradient boosting. It also covers model tuning, overfitting, performance evaluation, and offline validation.Networks & Models Tuning & Model Oftuning Model Tuning & Overfitting Model Tuning & Performance Evaluation Pre-Calculus: Functions This course covers functions, elementary addition, subtraction, multiplication, division, exponentiation, exponents, logarithms, and their applications. The course is designed to help prepare students to enroll for a first semester course in single variable calculus. Upon completing this course, you will be able to: 1) Summarize functions 2) Add and subtract functions 3) Multiply and divide functions 4) Solve linear and quadratic equations 5) Inverse and inverse equations 6) Inverse and inverse equations in Geometry 7) Inverse equations in Biology In order to be more precise, the
Article | Example |
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Rules Extraction System Family | [20] I. H. Witten, E. Frank, and M. A. Hall, Data Mining Practical Machine Learning Tools and Techniques, Third ed.: Morgan Kaufmann, 2011. |
Data mining | It also is a buzzword and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence. The book "Data mining: Practical machine learning tools and techniques with Java" (which covers mostly machine learning material) was originally to be named just "Practical machine learning", and the term "data mining" was only added for marketing reasons. Often the more general terms ("large scale") "data analysis" and "analytics" – or, when referring to actual methods, "artificial intelligence" and "machine learning" – are more appropriate. |
Active learning (machine learning) | Recent developments are dedicated to hybrid active learning and active learning in a single-pass (on-line) context, combining concepts from the field of Machine Learning (e.g., conflict and ignorance) with adaptive, incremental learning policies in the field of Online machine learning. |
Machine learning | Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves `rules’ to store, manipulate or apply, knowledge. The defining characteristic of a rule-based machine learner is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system. This is in contrast to other machine learners that commonly identify a singular model that can be universally applied to any instance in order to make a prediction. Rule-based machine learning approaches include learning classifier systems, association rule learning, and artificial immune systems. |
Machine learning | Some statisticians have adopted methods from machine learning, leading to a combined field that they call "statistical learning". |
Machine learning | Machine learning tasks are typically classified into three broad categories, depending on the nature of the learning "signal" or "feedback" available to a learning system. These are |
Machine learning | Another categorization of machine learning tasks arises when one considers the desired "output" of a machine-learned system: |
Machine learning | Machine Learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use, thus digitizing cultural prejudices. Responsible collection of data thus is a critical part of machine learning. |
Adversarial machine learning | Adversarial machine learning is a research field that lies at the intersection of machine learning and computer security. It aims to enable the safe adoption of machine learning techniques in adversarial settings like spam filtering, malware detection and biometric recognition. |
Machine learning | Software suites containing a variety of machine learning algorithms include the following : |
Outline of machine learning | [[Category:Artificial intelligence|Machine learning]] |
Machine learning | Learning classifier systems (LCS) are a family of rule-based machine learning algorithms that combine a discovery component (e.g. typically a genetic algorithm) with a learning component (performing either supervised learning, reinforcement learning, or unsupervised learning). They seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions. |
Machine learning | Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining, where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning. Machine learning can also be unsupervised and be used to learn and establish baseline behavioral profiles for various entities and then used to find meaningful anomalies. |
Quantum machine learning | Quantum machine learning is an emerging interdisciplinary research area at the intersection of quantum physics and machine learning. One can distinguish four different ways of merging the two parent disciplines. Quantum machine learning algorithms can use the advantages of quantum computation in order to improve classical methods of machine learning, for example by developing efficient implementations of expensive classical algorithms on a quantum computer. On the other hand, one can apply classical methods of machine learning to analyse quantum systems. Most generally, one can consider situations wherein both the learning device and the system under study are fully quantum. |
Machine learning | Machine learning and statistics are closely related fields. According to Michael I. Jordan, the ideas of machine learning, from methodological principles to theoretical tools, have had a long pre-history in statistics. He also suggested the term data science as a placeholder to call the overall field. |
International Conference on Machine Learning | The International Conference on Machine Learning (ICML) is the leading international academic conference in machine learning, attracting annually more than 2000 participants from all over the world. It is supported by the International Machine Learning Society (IMLS). |
Machine learning | Among other categories of machine learning problems, learning to learn learns its own inductive bias based on previous experience. Developmental learning, elaborated for robot learning, generates its own sequences (also called curriculum) of learning situations to cumulatively acquire repertoires of novel skills through autonomous self-exploration and social interaction with human teachers and using guidance mechanisms such as active learning, maturation, motor synergies, and imitation. |
Logic learning machine | Logic Learning Machine is implemented in the Rulex suite. |
Machine learning | Machine learning, reorganized as a separate field, started to flourish in the 1990s. The field changed its goal from achieving artificial intelligence to tackling solvable problems of a practical nature. It shifted focus away from the symbolic approaches it had inherited from AI, and toward methods and models borrowed from statistics and probability theory. It also benefited from the increasing availability of digitized information, and the possibility to distribute that via the Internet. |
Machine learning | Leo Breiman distinguished two statistical modelling paradigms: data model and algorithmic model, wherein 'algorithmic model' means more or less the machine learning algorithms like Random forest. |