Daily Archives: January 12, 2020

Supervised Learning

Supervised machine learning builds a model that makes predictions based on evidence in the presence of uncertainty. A supervised learning algorithm takes a known set of input data and known responses to the data (output) and trains a model to generate reasonable predictions for the response to new data. Use supervised learning if you have known data for the output you are trying to predict.

Supervised learning uses classification and regression techniques to develop predictive models.

Classification techniques predict discrete responsesfor example, whether an email is genuine or spam, or whether a tumor is cancerous or benign. Classification models classify input data into categories. Typical applications include medical imaging, speech recognition, and credit scoring.

Use classification if your data can be tagged, categorized, or separated into specific groups or classes. For example, applications for hand-writing recognition use classification to recognize letters and numbers. In image processing and computer vision, unsupervised pattern recognition techniques are used for object detection and image segmentation.

Common algorithms for performing classification include support vector machine (SVM), boosted and bagged decision trees, k-nearest neighbor, Nave Bayes, discriminant analysis, logistic regression, and neural networks.

Regression techniques predict continuous responsesfor example, changes in temperature or fluctuations in power demand. Typical applications include electricity load forecasting and algorithmic trading.

Use regression techniques if you are working with a data range or if the nature of your response is a real number, such as temperature or the time until failure for a piece of equipment.

Common regression algorithms include linear model, nonlinear model, regularization, stepwise regression, boosted and bagged decision trees, neural networks, and adaptive neuro-fuzzy learning.

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What Is Machine Learning? | How It Works, Techniques ...

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.

The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.

Machine learning algorithms are often categorized as supervised or unsupervised.

Machine learning enables analysis of massive quantities of data. While it generally delivers faster, more accurate results in order to identify profitable opportunities or dangerous risks, it may also require additional time and resources to train it properly. Combining machine learning with AI and cognitive technologies can make it even more effective in processing large volumes of information.

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What is Machine Learning? A definition - Expert System

Machine-learning techniques are required to improve the accuracy of predictive models. Depending on the nature of the business problem being addressed, there are different approaches based on the type and volume of the data. In this section, we discuss the categories of machine learning.

Supervised learning

Supervised learning typically begins with an established set of data and a certain understanding of how that data is classified. Supervised learning is intended to find patterns in data that can be applied to an analytics process. This data has labeled features that define the meaning of data. For example, you can create a machine-learning application that distinguishes between millions of animals, based onimages and written descriptions.

Unsupervised learning

Unsupervised learning is used when the problem requires a massive amount of unlabeled data. For example, social media applications, such as Twitter, Instagram and Snapchat, all have large amounts of unlabeled data. Understanding the meaning behind this data requires algorithms that classify the data based on the patterns or clusters it finds. Unsupervised learning conducts an iterative process, analyzing data without human intervention. It is used with email spam-detecting technology. There are far too many variables in legitimate and spam emails for an analyst to tag unsolicited bulk email. Instead, machine-learning classifiers, based on clustering and association, are applied to identify unwanted email.

Reinforcement learning

Reinforcement learning is a behavioral learning model. The algorithm receives feedback from the data analysis, guiding the user to the best outcome. Reinforcement learning differs from other types of supervised learning, because the system isnt trained with the sample data set. Rather, the system learns through trial and error. Therefore, a sequence of successful decisions will result in the process being reinforced, because it best solves the problem at hand.

Deep learning

Deep learning is a specific method of machine learning that incorporates neural networks in successive layers to learn from data in an iterative manner. Deep learning is especially useful when youre trying to learn patterns from unstructured data. Deep learning complex neural networks are designed to emulate how the human brain works, so computers can be trained to deal with poorly defined abstractions and problems. The average five-year-old child can easily recognize the difference between his teachers face and the face of the crossing guard. In contrast, the computer must do a lot of work to figure out who is who. Neural networks and deep learning are often used in image recognition, speech, and computer vision applications.

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Machine Learning | IBM

Amazon SageMaker enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. It removes the complexity that gets in the way of successfully implementing machine learning across use cases and industriesfrom running models for real-time fraud detection, to virtually analyzing biological impacts of potential drugs, to predicting stolen-base success in baseball.

Amazon SageMaker Studio: Experience the first fully integrated development environment (IDE) for machine learning with Amazon SageMaker Studio, where you can perform all ML development steps. You can quickly upload data, create and share new notebooks, train and tune ML models, move back and forth between steps to adjust experiments, debug and compare results, and deploy and monitor ML models all in a single visual interface, making you much more productive.

Amazon SageMaker Autopilot: Automatically build, train, and tune models with full visibility and control, using Amazon SageMaker Autopilot. It is the industrys first automated machine learning capability that gives you complete control and visibility into how your models were created and what logic was used in creating these models.

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Machine Learning on AWS

Tom M. Mitchell, a machine learning pioneer and Carnegie Mellon University (CMU) professor, predicted the evolution and synergy of human and machine learning. Today's Facebook News Feed is a perfect example. The News Feed is programmed to display user friend content. If a user frequently tags or writes on the wall of a particular friend, the News Feed changes its behavior to display more content from that friend.

Other machine learning applications include syntactic pattern recognition, natural language processing, search engines, computer vision and machine perception.

It's difficult to replicate human intuition in a machine, primarily because human beings often learn and execute decisions unconsciously.

Like children, machines require an extended training period when developing broad algorithms geared toward the dictation of future behavior. Training techniques include rote learning, parameter adjustment, macro-operators, chunking, explanation-based learning, clustering, mistake correction, case recording, multiple model management, back propagation, reinforcement learning and genetic algorithms.

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What is Machine Learning? - Definition from Techopedia

Description

"Artificial Intelligence is the new electricity."

- Andrew Ng, Stanford Adjunct Professor

Please note: the course capacity is limited.To be considered for enrollment, join the wait list and be sure to complete your NDO application. Only applicants with completed NDO applications will be admitted should a seat become available.This course will be also available next quarter.

Computers are becoming smarter, as artificial intelligence and machine learning, a subset of AI, make tremendous strides in simulating human thinking. Creating computer systems that automatically improve with experience has many applications including robotic control, data mining, autonomous navigation, and bioinformatics.

This course provides a broad introduction to machine learning and statistical pattern recognition. Learn about both supervised and unsupervised learning as well as learning theory, reinforcement learning and control. Explore recent applications of machine learning and design and develop algorithms for machines.

Linear algebra, basic probability and statistics.

We strongly recommend that you review the first problem set before enrolling. If this material looks unfamiliar or too challenging, you may find this course too difficult.

This course is typically offered Autumn quarter.

The course schedule is displayed for planning purposes courses can be modified, changed, or cancelled. Course availability will be considered finalized on the first day of open enrollment. For quarterly enrollment dates, please refer to our graduate certificate homepage.

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Machine Learning | Stanford Online

This Machine Learning basics video will help you understand what is Machine Learning, what are the types of Machine Learning - supervised, unsupervised & reinforcement learning, how Machine Learning works with simple examples, and will also explain how Machine Learning is being used in various industries. Machine learning is a core sub-area of artificial intelligence; it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. So, put simply, the iterative aspect of machine learning is the ability to adapt to new data independently. This is possible as programs learn from previous computations and use pattern recognition to produce reliable results. Machine learning is starting to reshape how we live, and its time we understood what it is and why it matters. Now, let us deep dive into this short video and understand the basics of Machine Learning.

Below topics are explained in this Machine Learning basics video:1. What is Machine Learning? ( 00:21 )2. Types of Machine Learning ( 02:43 )2. What is Supervised Learning? ( 02:53 )3. What is Unsupervised Learning? ( 03:46 )4. What is Reinforcement Learning? ( 04:37 )5. Machine Learning applications ( 06:25 )

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Download the Machine Learning Career Guide to explore and step into the exciting world of Machine Learning, and follow the path towards your dream career- https://www.simplilearn.com/machine-l...

Watch more videos on Machine Learning: https://www.youtube.com/watch?v=7JhjI...

#MachineLearning #MachineLearningAlgorithms #DataScience #SimplilearnMachineLearning #MachineLearningCourse

About Simplilearn Machine Learning course:A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all peoples digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with the knowledge and hands-on skills required for certification and job competency in Machine Learning.

Why learn Machine Learning?Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine LearningThe Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.

What skills will you learn from this Machine Learning course?

By the end of this Machine Learning course, you will be able to:

1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.3. Acquire a thorough knowledge of the mathematical and heuristic aspects of Machine Learning.4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems

We recommend this Machine Learning training course for the following professionals in particular:1. Developers aspiring to be a data scientist or Machine Learning engineer2. Information architects who want to gain expertise in Machine Learning algorithms 3. Analytics professionals who want to work in Machine Learning or artificial intelligence4. Graduates looking to build a career in data science and Machine Learning

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Machine Learning Basics | What Is Machine Learning? | Introduction To Machine Learning | Simplilearn

Machine Learning (ML) is basically that field of computer science with the help of which computer systems can provide sense to data in much the same way as human beings do. In simple words, ML is a type of artificial intelligence that extract patterns out of raw data by using an algorithm or method. The key focus of ML is to allow computer systems to learn from experience without being explicitly programmed or human intervention.

This tutorial will be useful for graduates, postgraduates, and research students who either have an interest in this subject or have this subject as a part of their curriculum. The reader can be a beginner or an advanced learner. This tutorial has been prepared for the students as well as professionals to ramp up quickly. This tutorial is a stepping stone to your Machine Learning journey.

The reader must have basic knowledge of artificial intelligence. He/she should also be aware of Python, NumPy, Scikit-learn, Scipy, Matplotlib. If you are new to any of these concepts, we recommend you to take up tutorials concerning these topics, before you dig further into this tutorial.

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Machine Learning with Python Tutorial - Tutorialspoint