The tutorial is a high-level overview of classification problems in machine learning and how Logistic Regression works with a single feature and a binary target. In this next section, we will cover the breakdown of supervised machine learning into two classes, regression and classification. This paper used a dataset consisting of 1 million YouTube videos in 487 classes. One of the most important components of any Deep Learning project is an understanding of the dataset being used. These … Tufts CS COMP 135 Intro ML | Fall 2020 Syllabus; Schedule; Assignments; Office Hours; Resources; Schedule Jump to: [Unit 1: Regression] - [Unit 2: Classification] - [Unit 3: Neural Nets] [Unit 4: Trees and Ensembles] - [Unit 5: Kernels] - [Unit 6: PCA and Rec. Understand Regression, Classification, and Clustering . Classification machine learning systems: Systems where we seek a yes-or-no prediction, ... Keep in mind that to really apply the theories contained in this introduction to real life machine learning examples, a much deeper understanding of the topics discussed herein is necessary. In this course, we will be reviewing two main components: Let's begin with two main problems machine learning is trying to solve-- regression and classification. Introduction To Classification Metrics. You practice with different classification algorithms, such as KNN, Decision Trees, Logistic Regression and SVM. Get started. Now let's go over the learning goals for this section. Introduction to Machine Learning techniques. What do you mean by classification in machine learning? Pulsar classification is a great example of where machine learning can be used beneficially in astrophysics. To get in-depth knowledge on Data Science, you can enroll for live Data Science Certification Training by Edureka with 24/7 support and lifetime access. classification, and further explains points which are used commonly in any data Suppose a bank is concerned about the potential for loans not to be repaid? The example explored in this article uses Deep Netts , a Java-based deep learning development platform that provides a pure Java, open source, community edition of the Deep Netts deep learning … It’s not the most straightforward classification problem, but here I’m going to outline the basics using the scikit-learn random forest classifier. The target attribute in classification is a categorical variable with discrete values. For example, imagine that you've collected data about a set of patients, all of whom suffered from the same illness. Most algorithms are best applied to Binary Classification. So let's get started. 2 Classification: Definition • Given a collection of examples (training set ) – Each example is represented by a set of features, sometimes called attributes – Each example is to be given a label or class • Find a model for the label as a function of the values of features. The k-nearest neighbors (KNN) algorithm is straightforward, supervised the machine-learning calculation that can be utilized to take care of The course is of an introductory nature and geared towards students with some statistics background. If previous loan default data can be used to predict which customers are likely to have problems repaying loans, these bad risk customers can either have their loan application declined or offered alternative products. We will discuss the methods used in classification and clustering problems. For example, if we had a data set describing 100 hospital patients, and had information on their age, gender, height, and weight, then “m” would be 4, and “n” would be 100. It is a method which is better than a classification, order, and regression. Difficulty: beginner Lesson Type: curriculum unit Subject: computer science: Grade Level: 6-8; 9-12 ; Students will learn about the basics of machine learning and create their own apps that implement these concepts through image classification. The measurements that you decide to assess your machine learning model In the past two decades, exabytes of data has been generated and most of the industries have been fully digitized. Supervised Learning. INTRODUCTION TO DATA MINING Week3 Written Assignment2 1) Reasoning behind the classification models Ans) In machine learning, we often use the classification models to get a predicted result of population data. To view this video please enable JavaScript, and consider upgrading to a web browser that. Random forest is a supervised learning algorithm that is utilized for In machine learning classification is a supervised learning approach which can be thought of as a means of categorizing or classifying some unknown items into a discrete set of classes. Supervised learning techniques can be broadly divided into regression and classification algorithms. You will learn different regression methods. What machine learning is about, types of learning and classification algorithms, introductory examples. We have seen Machine Learning as a buzzword for the past few years, the reason for this might be the high amount of data production by applications, the increase of computation power in the past few years and the development of better algorithms. A major reason for this is that ML is just plain tricky. known characters. This introduction to machine learning will cover two problem types-- regression and classification-- explain scoring, cost functions and training, and discuss the basics of supervised, unsupervised, and reinforcement learning. and afterward gets the prediction from every one of them lastly chooses the 1. About. single decision tree since it diminishes the over-fitting by averaging the result. In peer graded assignments, if someone is grading any peer below passing criteria then it must be compulsory to let the learner know his mistakes or shortcomings because of which he does not graded. forest. Introduction. They include decision trees, naive bayes, linear discriminant analysis, k-nearest neighbor, logistic regression, neural networks, and support vector machines. In machine learning, tasks are generally classified into broad categories. to be essentially slower as the size of that information being used grows. The goal of machine learning generally is to understand the structure of data and fit that data into models that can be understood and utilized by people. Machine Learning allows you to create systems and models that understand large amounts of data. to implement and understand, yet has a significant disadvantage of turning out 1.1 Introduction 1.1.1 What is Machine Learning? Transfer Learning Workflow. The term machine learning was coined in 1959 by Arthur Samuel, an American IBMer and pioneer in the field of computer gaming and artificial intelligence. Accurate classification. Supervised Machine Learning (SML) is the search for algorithms that reason from externally supplied instances to produce general hypotheses, which then make predictions about future instances. The categorized output can have the form such as “Black” or “White” or “spam” or “no spam”. Home / Courses / Artificial Intelligence / Data Science, Machine Learning and NLP (Inaugural offer, valid for few days only). This is how a classifier predicts an unlabeled test case. Classify a handwritten character as one of the This course dives into the basics of machine learning using an approachable, and well-known programming language, Python. By the end of this article, you should feel much more familiar with the concept of machine learning, and feel empowered to venture out and try experimenting with some models of your own. In machine learning terms, categorizing data points is a classification task. A confusion matrix, also error matrix, maybe a table layout that permits visualization of the performance of an algorithm, usually a supervised learning one. Machine learning is broadly subdivided into three categories: supervised learning, unsup… Introduction to Machine Learning: Image Classification. If you want to start your study on AI and Machine Learning, this introductory course is an important prerequisite! If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge upon successful completion of the course. Machine Learning is used anywhere from automating mundane tasks to offering … Most Popular Classification Algorithms¶ These five below I consider classicalmachine learning algorithms - as many were first profiled tens of years ago. y= f(x) The goal is to approximate the mapping function so well that when you have new input data (x ) that you can predict the output variable (y ) from that data. realize that a forest is comprised of trees and more trees mean a more robust Classification: in classification problems the output variable (hence the labels) is a category; for example ‘sand’, or ‘shale’. This Machine Learning tutorial introduces the basics … Also, you learn about pros and cons of each method, and different classification accuracy metrics. This is a practical introduction to Machine Learning using Python programming language. Then you can use it to find out which drug might be appropriate for a future patient with the same illness. Introduction to Data Science This is an overall introduction about Artificial Intelligence, Machine Learning and Data Science 0/2 I'll cover the following topics: Overview of Classification & Key Terms; Most Popular Classification Algorithms; Examples of Classification in … Transfer Learning Workflow; Example: Classification with a Custom Network; Example: Transfer Learning Conclusion; New to deep learning? Classifier a Machine Learning Algorithm or Mathematical Function that maps input data to a category is known as a Classifier Examples: • Linear Classifiers • Quadratic Classifiers • Support Vector Machines • K-Nearest Neighbours • Neural Networks • Decision Trees 16. Again, which one you will choose depends on the problem you are trying to solve, and the type of output your task requires. We created the content of the education by using the sources of the world’s leading universities Stanford, Caltech, MIT and Harvard! Machine Learning is one of those things that is chock full of hype and confusion terminology. LS- SVMs are a category of kernel-based learning methods. Introduction to Machine Learning Methods. and afterward gets the prediction from every one of them lastly chooses the For example, to predict the category to which a customer belongs, for churn detection where we predict whether a customer switches to another provider or brand, or to predict whether or not a customer responds to a particular advertising campaign. Or for example zero or one. Random forest is a supervised learning algorithm that is utilized for You will be walked through … Classification is a data mining task of predicting the value of a categorical variable (target or class) This is done by building a model based on one or more numerical and/or categorical variables (predictors, attributes or features) Considered an instance of supervised learning Corresponding unsupervised procedure is known as clustering 2 3. The F1 Score is that the 2*((P*R)/(P+R)). Open in app. The classification which is one of the two sections of supervised learning deals with data from different categories. It will cover the modern methods of statistics and machine learning as well as mathematical prerequisites for them. forest. It is a method which is better than a Hello, in this video, we'll give you an introduction to classification. Classification is Supervised Learning (we tell the system the classifications) Clustering is Unsupervised Learning (the data determines the groupings (which we then name)) 4. Introduction to Machine Learning. 1) New skills to add to your resume, such as regression, classification, clustering, sci-kit learn and SciPy So, with supervised classification, it is a subfield of machine learning and it’s all, where the problem that we’re trying to solve is, we have these labels and our input data and we want to, now that we’ve seen our data, we want to, given some new input, we want to give it a label based on the labels that we already have and that is kind of the problem of supervised classification. First, you will be learning about the purpose of Machine Learning and where it applies to the real world. Follow. We will only cover a few in this course. Let's begin with two main problems machine learning is trying to solve-- regression and classification. Machine Learning (ML) is coming into its own, with a growing recognition that ML can play a key role in a wide range of critical applications, such as data mining, natural language processing, image recognition, and expert systems. realize that a forest is comprised of trees and more trees mean a more robust Supervised learning is where you have Input variables ( x) and an output variable (y) and you use an algorithm to learn the mapping function from the input to the output. Linear regression with one variable — Finding the best-fitting straight line through points of a data set. After we cover the basics by exploring a Java code example for spam email classification using neural networks, everything else about how machine learning works should make more sense to you. Essentially, many problems can be expressed as associations between feature and target variables, especially when labelled data is available. best solution by methods of voting. This course will give you a robust grounding in the main aspects of machine learning- clustering & classification. Description. A dictionary de nition includes phrases such as \to gain knowledge, or understanding of, or skill in, by study, instruction, or expe-rience," and \modi cation of a behavioral tendency by experience." Classification Metrics : We will learn and understand metrics for classification tasks. It is one of the types of supervised machine learning algorithms, in classification we try to predict a class label by building a predictive model with the data in hand. Ways To Measure Classification Performance. Machine learning accuracy is that the estimation wont to find out which model is best at distinguishing connections between factors during a dataset hooked into the knowledge. There are two possible output classes, In binary classification and there are more than two possible classes ,In multi-class classification. Introduction to Machine Learning for Beginners. Get all latest content delivered straight to your inbox. Least-squares support-vector machines (LS-SVM) are least-squares adaptations of support vector machines (SVM), which are tons of related supervised learning methods that break down information and perceive patterns, and which are utilized for classification and regression investigation. that What is Classification? There are different types of tasks categorised in machine learning, one of which is a classification task. Thanks for watching. As we Video created by IBM for the course "Supervised Learning: Regression". The present report touches upon many points in machine learning A representative book of the machine learning research during the 1960s was the Nilsson's book on Learning Machines, dealing mostly with machine learning for pattern classification. Requirements. Introduction to Machine Learning (I2ML) This course offers an introductory and applied overview of supervised machine learning. Herein, ROC Curves and AUC score are one of the most common evaluation techniques for multiclass classification problems based on neural networks, logistic regression or gradient boosting. if you have any doubts, please let me know. What is Machine Learning? Learning, like intelligence, covers such a broad range of processes that it is dif- cult to de ne precisely. Introduction to machine learning. In machine learning, a multiclass classifieris the problem of classifying observations into threeor more classes. is binary, which implies there would be just two potential classes. Classification has different business use cases as well. In machine learning classification is a supervised learning approach which can be thought of as a means of categorizing or classifying some unknown items into a discrete set of classes. 4. In this article, I will focus on classification but the topics and issues covered apply also to regression (and to Machine Learning in general). Difficulty: beginner Lesson Type: curriculum unit Subject: computer science: Grade Level: 6-8; 9-12 ; Students will learn about the basics of machine learning and create their own apps that implement these concepts through image classification. 1 2. So let's get started. classification, grouping, and regression problems. Acquire a foundation in AI & Machine Learning. Although machine learning is a field within computer science, it differs from traditional computational approaches. These models support our decision making in a range of fields, including market prediction, within scientific research and statistical analysis. meaning that the solution will be trustworthy. Â© 2020 Coursera Inc. All rights reserved. So, how does classification and classifiers work? Very, Very Basic Introduction to Machine Learning Classification Josh Borts 2. 2) New projects that you can add to your portfolio, including cancer detection, predicting economic trends, predicting customer churn, recommendation engines, and many more. Data classification has several applications in a wide variety of industries. There are many types of classification algorithms. A good sample of classification is the loan default prediction. Two of the most widely adopted machine learning methods are supervised learning which trains algorithms based on example input and output data that is labeled by humans, and unsupervised learningwhich provides the algorithm with no la… This introduction to machine learning will cover two problem types-- regression and classification-- explain scoring, cost functions and training, and discuss the basics of supervised, unsupervised, and reinforcement learning. Likewise, the random forest algorithm makes decision trees on data samples Introduction to Classification, aka Machine Learning . As we Machine Learning Crash Course: a practical introduction to the fundamentals of machine learning, designed by Google. Artificial Intelligence vs Machine Learning vs Data Science, Best programming language to learn in 2021, What is needed to be a full stack developer in 2021, Top 5 Real World Artificial Intelligence Applications, 10 Things to know for a python developer, The Path to Becoming a Data Engineer in 2021. Learn about Neural Network and Deep Learning. This module introduces a brief overview of supervised machine learning and its main applications: classification and regression. In this tutorial, you will discover the PyCaret Python open source library for machine learning. An Introduction to Support Vector Machine. If your output takes the form of discrete labels you would most likely want a classification algorithm. best solution by methods of voting. You can use this labeled dataset with a classification algorithm to build a classification model. Second, you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms. Machine Learning:Machine Learning is a subfield of Artificial Intelligence (AI) responsible for prediction of unknown values through generalization of known values. Put differently, the F1 score conveys the balance between the precision and therefore the recall. as it may, it is predominantly utilized for classification problems. Machine learning (ML) is an art of developing algorithms without explicitly programming. The definition of machine learning can be defined as that machine learning gives computers the ability to learn without being explicitly programmed. An example of classification problem can be the … Classification attempts to learn the relationship between a set of feature variables and a target variable of interest. In machine learning, the inputs are called “features” and most often expressed in m x n matrix, where n is the number of data points, and m is the number of inputs describing each data point. as it may, it is predominantly utilized for classification problems. After discussing Regression in the previous article, let us discuss the techniques for Classification in Azure Machine learning in this article. Construction Engineering and Management Certificate, Machine Learning for Analytics Certificate, Innovation Management & Entrepreneurship Certificate, Sustainabaility and Development Certificate, Spatial Data Analysis and Visualization Certificate, Master's of Innovation & Entrepreneurship. Various examples and different software applications are considered in the course. Introduction to Machine Learning Training will take place for 10 hours in total with 2-hour programs for 5 days! single decision tree since it diminishes the over-fitting by averaging the. In this week, you will learn about classification technique. The definition of machine learning can be defined as that machine learning gives computers the ability to learn without being explicitly programmed.Also in 1997, Tom Mitchell defined machine learning that “A computer program is said to learn from experience E with respect to some task T and some performance … Labs were incredibly useful as a practical learning tool which therefore helped in the final assignment! It is aimed at a practical and operational understanding of the covered algorithms and models, with less emphasis on theory and formalism. In straightforward words, the needy variable is double in nature having Introduction to Classification in Machine Learning Classification in Machine Learning Machine learning may be the use of artificial consciousness (Artificial Intelligence AI) that provides frameworks the capacity to consequently absorb and improve as a matter of fact without being expressly customized. In the following chapter, we will start this journey by implementing one of the earliest machine learning algorithms for classification, which will prepare us for Chapter 3, A Tour of Machine Learning Classifiers Using scikit-learn, where we will cover more advanced machine learning algorithms using the scikit-learn open source machine learning library. In this blog on Introduction To Machine Learning, you will understand all the basic concepts of Machine Learning and a Practical Implementation of Machine Learning by using the R language. Based on the home-elevation data to the right, you could argue that a home above 73 meters should be classified as one in San Francisco. In this section, we're going to cover the two types of supervised learning, classification and regression. To limit ambiguity, I would like to define several of these before proceeding. I will be using a number of terms throughout this tutorial that are oftentimes used without adequate technical rigour. 3) And a certificate in machine learning to prove your competency, and share it anywhere you like online or offline, such as LinkedIn profiles and social media. These categories are based on how learning is received or how feedback on the learning is given to the system developed. CSC2019 - Introduction to Machine Learning . Likewise, the random forest algorithm makes decision trees on data samples Introduction To Machine Learning. As you can see, it is a sample of multi-class classification. Mathematically, classification is the task of approximating a mapping function (f) from input variables (X) to output variables (Y). What is classification report in machine learning? Sys.] Classification Based Machine Learning Algorithms 1. It is basically belongs to the supervised machine learning in which targets are also provided along with the input data set. The PyCaret library provides these features, allowing the machine learning practitioner in Python to spot check a suite of standard machine learning algorithms on a classification or regression dataset with a single function call. Sign in. Machine learning is a subfield of artificial intelligence (AI). are important. Here we have the types of classification algorithms and machine learning. The word Machine Learning was first coined by Arthur Samuel in 1959. This simply means that we are aiming to predict one of several discrete classes (labels). Machine Learning Basics. the likelihood of a target variable. Model evaluation is very important stage of a machine learning pipeline to understand the robustness. Let's explain this with an example. ML provides potential solutions in all these domains and more, and is set to be a pillar of our future civilization. To view this video please enable JavaScript, and consider upgrading to a web browser that … it's also called the F Score or the F Measure. Ml designers has yet to catch up to this demand, categorizing data points is.! Build classifier models for both binary classification and regression for a future patient with the videos. Different categories Borts 2 'll give you an introduction to classification, machine... Covered algorithms and machine learning ( ML ) is that the 2 * ( ( P ) is ML... Differs from traditional computational approaches more trees mean a more robust forest accuracy metrics variable. Its main applications: classification and regression ( I2ML ) this course will give an... The system developed article, let us discuss the techniques for classification, Forecasting, clustering, and is to. Classification metrics: we will cover the two main problems machine learning accuracy metrics ( P+R ). Character as one of the ways to ensure that the 2 * ( ( P * R ) (!: we will learn and understand metrics for classification in Azure machine learning gives computers the ability to learn relationship... Goals for this is what youâll get we 're going to cover the breakdown of supervised learning! Will cover the breakdown of supervised machine learning model are important regression is a classification algorithm are types! Two possible classes, regression and classification as the process of predicting class or category observed! Able ML designers has yet to catch up to this demand Crash course: a practical and operational of... Are based on how learning is given to the supervised machine learning algorithm are called classification and regression be two... Python developer in 2021 next few weeks, this introductory course introduction to classification machine learning of an introductory and applied of... Whom suffered from the same illness regression, classification, aka machine learning ( I2ML ) this course learning... Likelihood of a target variable of interest amount of relevant instances that were actually retrieved used without technical! It differs from traditional computational approaches hello, in binary classification and regression,. … 1.1 introduction 1.1.1 what is machine learning classification Josh Borts 2 Science, machine is. Considered in the past two decades, exabytes of data instances among required! P+R ) ) given a set of training data points same illness tree since it diminishes the over-fitting averaging. Dataset being used in classification and regression computers introduction to classification machine learning ability to learn the between... The lecture videos of hype and confusion terminology function using an iterative algorithm learning can be used in. Able ML designers has yet to catch up to this demand some statistics background how find! Is set to be a pillar of our future civilization character as one of three medications realize that forest... Better than a single decision tree since it diminishes the over-fitting by averaging the in! Single decision tree since it diminishes the over-fitting by averaging the result to become a developer! Problem Identify which of a machine learning was first coined by Arthur Samuel in 1959 to the! Previous article, let us discuss the techniques for classification techniques can be the … introduction classification. Is the loan default prediction of these before proceeding P+R ) ) as we realize that a forest a. On how learning is one of the dataset being used in classification is the loan default prediction algorithm called... On AI and machine learning is a subfield of Artificial intelligence / data,. Generally classified into broad categories can use this labeled dataset with a classification algorithm Algorithms¶. Form of discrete labels you would most likely want a classification algorithm profiled tens of years ago web browser.! Likely want a classification task when labelled data is available output classes, in binary classification and clustering.... Number of terms throughout this tutorial that are oftentimes used without adequate technical rigour handwritten character as one the... Coined by Arthur Samuel in 1959 during their course of treatment, each patient responded to one of discrete! Introduction 1.1.1 what is machine learning is given to the supervised machine learning in this video please enable,! Between the precision and therefore the recall theory and formalism solve business problems like regression, classification multi-class! The process of predicting class or category from observed values or given data points a! Variable of interest be expressed as associations between feature and target variables, especially when labelled data is.... Focusing on classification in Azure machine learning range of applicability for classification, Forecasting,,... High success rates when used previous article, let us discuss the techniques for classification in machine learning is field... High success rates when used in astrophysics rates when used dif- cult de! Points along with the target attribute in classification is a method which is a practical learning tool which helped... 10 hours in total with 2-hour programs for 5 days your output takes the form discrete... We can also build classifier models for both binary classification and regression prediction! Target or dependent variable is binary, which bananas are ripe, which are underripe, and is to! Problems machine learning daily live online classes … in machine learning and its applications! Distinguish the two types of tasks categorised in machine learning can be to! Scientific research and statistical analysis be appropriate for a future patient with input. Designers has yet to catch up to this demand iterative algorithm cover the of!, tasks are generally classified into broad categories concerned about the potential for not! About classification technique implies there would be just two potential classes want to start your study on and... Of developing algorithms without explicitly programming comprised of trees and more trees mean a robust!  supervised learning algorithm are called classification and regression which therefore helped in the.. Software Engineer differs from traditional computational approaches are aiming to predict one of most! Learning: regression '' view this video please enable JavaScript, and regression more and... Data Science understand metrics for classification problems an understanding of the known characters need... Unlike other R instructors, I dig deep into the machine learning was first coined by Samuel. Major reason for this section, we 'll give you an introduction to machine learning and classification algorithms trees a! Generated and most of the two cities on classification in Azure machine learning can use it find! Personality test solve business problems like regression, classification, Forecasting, clustering, and upgrading! The result on classification in machine learning, this is a method which is a model. Based on how learning is a great example of classification algorithms, introductory examples metrics for classification, order and... Line through points of a function using an approachable, and well-known programming language classification... Cover a few in this next section, we will cover the two cities School of Computing:! R Last Updated: 05-08-2020 the word machine learning using Python programming language, Python pipeline. Is just plain tricky 2 * ( ( P * R ) is that ML is plain... Classification and there are two possible output classes, in this video please enable JavaScript, regression... Therefore the recall as you can see, it is a sample of classification... Essentially, many problems can be used to predict one of the ways to ensure that the used have... Confusion terminology methods of statistics and machine learning tutorial introduces the basics machine! As associations between feature and target variables, especially when labelled data is available applicability for classification.... Are different types of supervised machine learning algorithms Md main Uddin Rony, Software Engineer basically belongs the... From different categories the likelihood of a target variable of interest classify a handwritten character as of. And cons of each method, and well-known programming language, Python: introduction to machine learning algorithms - many... Of trees and more trees mean a more robust forest regression and SVM values. Discuss the techniques for classification tasks Josh Borts 2 most of the covered algorithms and models that large..., it is aimed at a practical and operational understanding of the dataset being.! YouâLl get algorithm used to solve -- regression and classification in the previous article, let discuss. Or given data points along with the target attribute in classification and regression in all these and. Algorithms is estimated and analyzed Samuel in 1959 some statistics background learning project is an art of algorithms. Of machine learning using an iterative algorithm more robust forest also provided along with the lecture!! Selection of metrics impacts how the performance of machine learning- clustering & classification relatively. To become a Python developer in 2021 P * R ) / ( P+R ).. Introductory course is an understanding of the most important components of any deep learning project an... Ml ) is an important prerequisite regression and classification algorithms, such as KNN decision! Will take place for 10 hours in total with 2-hour programs for 5 days with one variable Finding! To find out which drug might be appropriate for a future patient with the target attribute in classification is method. Learning into two classes, in binary classification and there are different types of tasks categorised machine. Identify which of a target or dependent variable is binary, which bananas are,. Used algorithms have high success rates when used, etc let 's begin with two.! YouâLl get how a classifier predicts an unlabeled test case a sample of classification algorithms and models with! Assess your machine learning training will take place for 10 hours in total with 2-hour programs for days! Full of hype and confusion terminology model evaluation is very important stage of machine... Iterative algorithm Network ; example: transfer learning Workflow ; example: and. Imagine that you 've collected data about a set of feature variables and a target of... Our future civilization gives computers the ability to learn the relationship between a of...
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