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Classification in Machine Learning | Machine Learning Tutorial | Python Training | Edureka

description
** Machine Learning Certification Training: https://www.edureka.co/machine-learning-certification-training ** This Edureka video on 'Classification In Machine Learning ' covers the concept of classification in machine learning with various classification algorithms and a Use case to implement digit classification on MNIST data. Following are the topics discussed in this Machine Learning Tutorial: What Is Classification in Machine learning Classification Terminologies in Machine Learning Classification Algorithms Classifier Evaluation Algorithm Selection Use Case - MNIST Digit Classification Python Tutorial Playlist: https://goo.gl/WsBpKe Blog Series: http://bit.ly/2sqmP4s #Edureka #edurekaclassificationinmachinelearning #EdurekaMachinelearning #PythonTraining #PythonEdureka Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: https://goo.gl/6ohpTV SlideShare: https://www.slideshare.net/EdurekaIN Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka ----------------------------------------------------------------------------------------------------------------------------------- How it Works? 1. This is a 5 Week Instructor-led Online Course,40 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training, you will be working on a real-time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course Edureka’s Machine Learning Course using Python is designed to make you grab the concepts of Machine Learning. The Machine Learning training will provide deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in python programming language. Furthermore, you will be taught of Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to automate real-life scenarios using Machine Learning Algorithms. Towards the end of the course, we will be discussing various practical use cases of Machine Learning in the python programming language to enhance your learning experience. Gain insight into the 'Roles' played by a Machine Learning Engineer Automate data analysis using python Describe Machine Learning Work with real-time data Learn tools and techniques for predictive modeling Discuss Machine Learning algorithms and their implementation Validate Machine Learning algorithms Explain Time Series and it’s related concepts Gain expertise to handle business in future, living the present - - - - - - - - - - - - - - - - - - - Why learn Machine Learning Using Python? Data Science is a set of techniques that enables the computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning ----------------------------------- Who should go for this Machine Learning Certification Training using Python? Edureka’s Python Machine Learning Certification Course is a good fit for the below professionals: Developers aspiring to be a ‘Machine Learning Engineer' Analytics Managers who are leading a team of analysts Business Analysts who want to understand Machine Learning (ML) Techniques Information Architects who want to gain expertise in Predictive Analytics 'Python' professionals who want to design automatic predictive models For more information, Please write back to us at sales@edureka.in or call us at IND: 9606058406 / US: 18338555775 (toll free)

detail
{'title': 'Classification in Machine Learning | Machine Learning Tutorial | Python Training | Edureka', 'heatmap': [{'end': 163.687, 'start': 140.95, 'weight': 1}], 'summary': 'Tutorial on machine learning covers classification algorithms in python, including logistic regression, naive bias, stochastic gradient descent, and more, with a focus on model efficiency and classifier evaluation. it also demonstrates building a logistic regression classifier achieving 97% accuracy in predicting digit classification using the mnist dataset.', 'chapters': [{'end': 44.7, 'segs': [{'end': 44.7, 'src': 'embed', 'start': 11.735, 'weight': 0, 'content': [{'end': 12.275, 'text': 'Hello everyone.', 'start': 11.735, 'duration': 0.54}, {'end': 19.417, 'text': 'This is Vasim from Edureka and I welcome you all to this session in which I am going to talk about classification in machine learning using Python.', 'start': 12.295, 'duration': 7.122}, {'end': 23.338, 'text': 'So before moving on to the session, let us take a look at the agenda.', 'start': 20.097, 'duration': 3.241}, {'end': 28.68, 'text': 'So first of all, I will give you a brief introduction to classification in machine learning moving further.', 'start': 23.798, 'duration': 4.882}, {'end': 37.442, 'text': 'I will start with a few classification algorithms and after this I will tell you about classifier evaluation and then I will tell you about the algorithm selection as well.', 'start': 28.8, 'duration': 8.642}, {'end': 39.698, 'text': 'And finally to sum up this session.', 'start': 38.197, 'duration': 1.501}, {'end': 42.419, 'text': 'I will use the MNIST data for digit classification.', 'start': 39.878, 'duration': 2.541}, {'end': 44.7, 'text': 'I hope you guys are clear with the agenda.', 'start': 43.219, 'duration': 1.481}], 'summary': 'Vasim from edureka discusses classification in machine learning using python and plans to cover algorithms, evaluation, selection, and use of mnist data for digit classification.', 'duration': 32.965, 'max_score': 11.735, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXdum128xww/pics/pXdum128xww11735.jpg'}], 'start': 11.735, 'title': 'Classification in machine learning with python', 'summary': 'Provides a brief introduction to classification in machine learning, covers various classification algorithms, discusses classifier evaluation and algorithm selection, and demonstrates the use of mnist data for digit classification.', 'chapters': [{'end': 44.7, 'start': 11.735, 'title': 'Classification in machine learning with python', 'summary': 'Covers a brief introduction to classification in machine learning, various classification algorithms, classifier evaluation, algorithm selection, and the use of mnist data for digit classification.', 'duration': 32.965, 'highlights': ['The chapter covers a brief introduction to classification in machine learning, various classification algorithms, classifier evaluation, algorithm selection, and the use of MNIST data for digit classification.', 'The session will cover a brief introduction to classification, moving on to various classification algorithms, and then discussing classifier evaluation and algorithm selection.', 'The chapter will conclude with the use of MNIST data for digit classification.', 'The session will cover a brief introduction to classification, various classification algorithms, classifier evaluation, algorithm selection, and the use of MNIST data for digit classification.']}], 'duration': 32.965, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXdum128xww/pics/pXdum128xww11735.jpg', 'highlights': ['Covers various classification algorithms, classifier evaluation, and algorithm selection', 'Provides a brief introduction to classification in machine learning', 'Demonstrates the use of MNIST data for digit classification']}, {'end': 565.988, 'segs': [{'end': 91.116, 'src': 'embed', 'start': 63.607, 'weight': 0, 'content': [{'end': 70.602, 'text': 'So what exactly is classification in machine learning? Classification is a process of categorizing a given set of data into classes.', 'start': 63.607, 'duration': 6.995}, {'end': 74.484, 'text': 'It can be performed on both structured or unstructured data.', 'start': 71.542, 'duration': 2.942}, {'end': 81.849, 'text': 'The process starts with predicting the class of given data points and the classes are often referred to as target label or categories.', 'start': 74.925, 'duration': 6.924}, {'end': 91.116, 'text': 'The classification predictive modeling is the task of approximating the mapping function from input variables to discrete output variables,', 'start': 82.89, 'duration': 8.226}], 'summary': 'Classification in machine learning categorizes data into classes, can be done on structured or unstructured data.', 'duration': 27.509, 'max_score': 63.607, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXdum128xww/pics/pXdum128xww63607.jpg'}, {'end': 140.25, 'src': 'embed', 'start': 103.569, 'weight': 1, 'content': [{'end': 110.895, 'text': 'This is a binary classification, since there can only be two classes, that is, has a heart disease or does not have a heart disease.', 'start': 103.569, 'duration': 7.326}, {'end': 118.262, 'text': 'the classifier in this case needs training data to understand how the given input variables are related to the class,', 'start': 110.895, 'duration': 7.367}, {'end': 124.607, 'text': 'and once the classifier is trained accurately, it can be used to detect whether heart disease is there or not for a particular patient.', 'start': 118.262, 'duration': 6.345}, {'end': 131.045, 'text': 'Since classification is a type of supervised learning even the targets are also provided with the input data.', 'start': 125.683, 'duration': 5.362}, {'end': 135.628, 'text': 'So let us get familiar with the classification in machine learning terminologies as well.', 'start': 131.766, 'duration': 3.862}, {'end': 140.25, 'text': "So I'll get you through a few terminologies that are used in classification in machine learning.", 'start': 136.168, 'duration': 4.082}], 'summary': 'Binary classification in supervised learning predicts heart disease presence based on input variables.', 'duration': 36.681, 'max_score': 103.569, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXdum128xww/pics/pXdum128xww103569.jpg'}, {'end': 165.388, 'src': 'heatmap', 'start': 140.95, 'weight': 1, 'content': [{'end': 148.054, 'text': 'So first of all, we have a classifier a classifier is nothing but an algorithm that is used to map the input data to a specific category.', 'start': 140.95, 'duration': 7.104}, {'end': 150.721, 'text': 'Talking about a classification model.', 'start': 148.8, 'duration': 1.921}, {'end': 157.064, 'text': 'the model predicts or draws a conclusion to the input data given for training and it will predict the class or category,', 'start': 150.721, 'duration': 6.343}, {'end': 163.687, 'text': 'for the data feature is nothing but an individual, measurable property of the phenomenon being observed.', 'start': 157.064, 'duration': 6.623}, {'end': 165.388, 'text': 'now talking about binary classification.', 'start': 163.687, 'duration': 1.701}], 'summary': 'A classifier maps input data to categories; binary classification is discussed.', 'duration': 24.438, 'max_score': 140.95, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXdum128xww/pics/pXdum128xww140950.jpg'}, {'end': 204.255, 'src': 'embed', 'start': 176.496, 'weight': 2, 'content': [{'end': 181.758, 'text': 'the classification with more than two classes is known as multi-class classification,', 'start': 176.496, 'duration': 5.262}, {'end': 189.78, 'text': 'and in multi-class classification each sample is assigned to one and only one label or Target, not talking about multi-label classification.', 'start': 181.758, 'duration': 8.022}, {'end': 195.702, 'text': 'This is a type of classification where each sample is assigned to a set of labels or targets.', 'start': 190.06, 'duration': 5.642}, {'end': 204.255, 'text': 'then we have initialize, which is used to assign the classifier to be used for the classification, and then we have trained the classifier which is.', 'start': 195.702, 'duration': 8.553}], 'summary': 'Multi-class classification assigns each sample to one label. initialization assigns classifier for training.', 'duration': 27.759, 'max_score': 176.496, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXdum128xww/pics/pXdum128xww176496.jpg'}, {'end': 242.871, 'src': 'embed', 'start': 214.579, 'weight': 4, 'content': [{'end': 217.72, 'text': "Let's say, the predict method returns the predicted label.", 'start': 214.579, 'duration': 3.141}, {'end': 224.403, 'text': 'Y. last but not the least, we have evaluate, which basically means the evaluation of the model, that is a classification report,', 'start': 217.72, 'duration': 6.683}, {'end': 226.243, 'text': 'or it can be cross validation, Etc.', 'start': 224.403, 'duration': 1.84}, {'end': 229.305, 'text': 'Now coming on to the types of learners in classification.', 'start': 226.924, 'duration': 2.381}, {'end': 230.705, 'text': 'We have two types of learners.', 'start': 229.405, 'duration': 1.3}, {'end': 232.906, 'text': 'We have lazy learners and eager learners.', 'start': 230.945, 'duration': 1.961}, {'end': 238.647, 'text': 'So lazy learner simply store the training data and wait until a testing data appears.', 'start': 233.522, 'duration': 5.125}, {'end': 242.871, 'text': 'the classification is done using the most related data in the stored training data.', 'start': 238.647, 'duration': 4.224}], 'summary': 'The transcript discusses the predict method, evaluation, and types of learners in classification, including lazy and eager learners.', 'duration': 28.292, 'max_score': 214.579, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXdum128xww/pics/pXdum128xww214579.jpg'}, {'end': 357.852, 'src': 'embed', 'start': 330.106, 'weight': 5, 'content': [{'end': 335.207, 'text': 'and the outcome is measured with a dichotomous variable, meaning it will have only two possible outcomes.', 'start': 330.106, 'duration': 5.101}, {'end': 339.568, 'text': 'that is true or false, or, for example, we can have one or zero.', 'start': 335.207, 'duration': 4.361}, {'end': 346.089, 'text': 'the goal of logistic regression is to find a best fitting relationship between the dependent variable and a set of independent variables.', 'start': 339.568, 'duration': 6.521}, {'end': 354.411, 'text': 'It is better than other binary classification algorithms like nearest neighbor since it quantitatively explains the factors leading to classification.', 'start': 346.87, 'duration': 7.541}, {'end': 357.852, 'text': 'So let me talk about a few advantages with logistic regression.', 'start': 355.071, 'duration': 2.781}], 'summary': 'Logistic regression aims to find best fitting relationship between dependent and independent variables, offering better quantitative explanation than other binary classification algorithms.', 'duration': 27.746, 'max_score': 330.106, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXdum128xww/pics/pXdum128xww330106.jpg'}, {'end': 432.834, 'src': 'embed', 'start': 407.29, 'weight': 6, 'content': [{'end': 411.834, 'text': 'which gives an assumption of independence among predictors in simple terms.', 'start': 407.29, 'duration': 4.544}, {'end': 420.242, 'text': 'This classifier assumes that the presence of a particular feature in any class is unrelated to the presence of any other feature.', 'start': 412.395, 'duration': 7.847}, {'end': 426.508, 'text': 'Even if the features depend on each other all of these properties contribute to the probability independently.', 'start': 421.163, 'duration': 5.345}, {'end': 432.834, 'text': 'So this model is easy to make and is particularly useful for comparatively large datasets.', 'start': 427.329, 'duration': 5.505}], 'summary': 'Naive bayes classifier assumes independence among predictors, making it easy to use and particularly useful for large datasets.', 'duration': 25.544, 'max_score': 407.29, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXdum128xww/pics/pXdum128xww407290.jpg'}, {'end': 482.907, 'src': 'embed', 'start': 460.336, 'weight': 7, 'content': [{'end': 468, 'text': 'The use cases include disease predictions and then we can use it for document classifications spam filters or sentiment analysis as well.', 'start': 460.336, 'duration': 7.664}, {'end': 469.941, 'text': 'So this is all about naive eyes.', 'start': 468.54, 'duration': 1.401}, {'end': 472.322, 'text': "Let's take a look at stochastic gradient descent.", 'start': 470.141, 'duration': 2.181}, {'end': 476.984, 'text': 'So it is a very effective and simple approach to fit linear models.', 'start': 472.962, 'duration': 4.022}, {'end': 482.907, 'text': 'stochastic gradient descent is particularly useful when the sample data is in a large number.', 'start': 476.984, 'duration': 5.923}], 'summary': 'Naive bayes and stochastic gradient descent are used for disease predictions, document classifications, spam filters, and sentiment analysis, especially when dealing with large sample data.', 'duration': 22.571, 'max_score': 460.336, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXdum128xww/pics/pXdum128xww460336.jpg'}, {'end': 552.224, 'src': 'embed', 'start': 522.332, 'weight': 8, 'content': [{'end': 529.099, 'text': 'It is a lazy learning algorithm that stores all instances corresponding to training data in n-dimensional space.', 'start': 522.332, 'duration': 6.767}, {'end': 536.205, 'text': 'It is a lazy learning algorithm as it does not focus on constructing a general internal model instead.', 'start': 529.9, 'duration': 6.305}, {'end': 539.128, 'text': 'It works on storing instances of training data.', 'start': 536.685, 'duration': 2.443}, {'end': 545.002, 'text': 'Classification is computed from a simple majority vote of the k nearest neighbors of each point.', 'start': 539.861, 'duration': 5.141}, {'end': 552.224, 'text': 'It is supervised and takes a bunch of label points and uses them to label other points to label a new point.', 'start': 545.723, 'duration': 6.501}], 'summary': 'Lazy learning algorithm stores training data instances in n-dimensional space, computes classification via majority vote of k nearest neighbors.', 'duration': 29.892, 'max_score': 522.332, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXdum128xww/pics/pXdum128xww522332.jpg'}], 'start': 45.32, 'title': 'Ml classification and algorithms', 'summary': 'Covers classification in ml, including binary and multi-class classification, and discusses various algorithms such as logistic regression, naive bias, stochastic gradient descent, and k nearest neighbor, along with their characteristics and use cases.', 'chapters': [{'end': 195.702, 'start': 45.32, 'title': 'Understanding classification in ml', 'summary': 'Explains classification in machine learning, which involves categorizing data into classes, with examples including binary and multi-class classification, and terminologies such as classifier, feature, and multi-label classification being discussed.', 'duration': 150.382, 'highlights': ['Classification is a process of categorizing a given set of data into classes, and it can be performed on both structured or unstructured data. Classification in machine learning involves categorizing data into classes, and it can be performed on both structured or unstructured data.', "Binary classification involves two classes, such as 'has a heart disease' or 'does not have a heart disease', and the classifier needs training data to understand the relationship between input variables and the class. Binary classification involves two classes, and in the example of heart disease detection, the classifier needs training data to understand the relationship between input variables and the class.", 'Multi-class classification involves classifying data into more than two classes, with each sample being assigned to one label, while multi-label classification assigns each sample to a set of labels or targets. Multi-class classification involves classifying data into more than two classes, with each sample being assigned to one label, while multi-label classification assigns each sample to a set of labels or targets.', 'Terminologies such as classifier, model, feature, binary classification, multi-class classification, and multi-label classification are explained to familiarize with classification in machine learning. The chapter explains various terminologies such as classifier, model, feature, binary classification, multi-class classification, and multi-label classification to familiarize with classification in machine learning.']}, {'end': 565.988, 'start': 195.702, 'title': 'Classification algorithms overview', 'summary': 'Covers the types of learners in classification, including lazy learners and eager learners, and then discusses various classification algorithms such as logistic regression, naive bias classifier, stochastic gradient descent, and k nearest neighbor algorithm, along with their advantages, disadvantages, and use cases.', 'duration': 370.286, 'highlights': ['The chapter discusses the types of learners in classification, including lazy learners and eager learners, and then covers various classification algorithms such as logistic regression, naive bias classifier, stochastic gradient descent, and K nearest neighbor algorithm. It provides an overview of the different types of learners in classification and introduces several classification algorithms, serving as the fundamental content of the chapter.', 'Logistic regression is a classification algorithm that uses one or more independent variables to determine an outcome with two possible outcomes, and it quantitatively explains the factors leading to classification. Logistic regression is a binary classification algorithm that aims to find the best fitting relationship between the dependent variable and a set of independent variables, providing a quantitative explanation of classification factors.', 'Naive bias classifier is based on Bayes theorem and assumes the independence among predictors, and it is known to outperform most of the classification methods in machine learning. Naive bias classifier, based on Bayes theorem, assumes independence among predictors and is recognized for its performance in comparison to other classification methods.', 'Stochastic gradient descent is a simple approach to fit linear models, particularly useful for large sample data, and it supports different loss functions and penalties for classification. Stochastic gradient descent is an effective method for fitting linear models, especially suitable for large sample data, and it offers support for various loss functions and penalties for classification.', 'K nearest neighbor algorithm is a lazy learning algorithm that stores all instances corresponding to training data in n-dimensional space and computes classification through a majority vote of the neighbors of each point. K nearest neighbor algorithm is a lazy learning approach that stores training data instances and performs classification through a majority vote of the neighbors of each point, representing an essential concept in classification algorithms.']}], 'duration': 520.668, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXdum128xww/pics/pXdum128xww45320.jpg', 'highlights': ['Classification in ml involves categorizing data into classes, can be performed on structured or unstructured data.', "Binary classification involves two classes, e.g., 'has a heart disease' or 'does not have a heart disease'.", 'Multi-class classification involves classifying data into more than two classes, each sample being assigned to one label.', 'Terminologies such as classifier, model, feature, binary classification, multi-class classification, and multi-label classification are explained.', 'The chapter discusses types of learners in classification, including lazy learners and eager learners.', 'Logistic regression uses one or more independent variables to determine an outcome with two possible outcomes.', 'Naive bias classifier is based on Bayes theorem and assumes independence among predictors.', 'Stochastic gradient descent is a simple approach to fit linear models, particularly useful for large sample data.', 'K nearest neighbor algorithm stores all instances corresponding to training data in n-dimensional space and computes classification through a majority vote.']}, {'end': 1044.413, 'segs': [{'end': 625.379, 'src': 'embed', 'start': 566.789, 'weight': 1, 'content': [{'end': 569.03, 'text': 'Talking about the advantages and disadvantages.', 'start': 566.789, 'duration': 2.241}, {'end': 574.694, 'text': 'This algorithm is quite simple in its implementation and is very robust noisy training data.', 'start': 569.511, 'duration': 5.183}, {'end': 578.176, 'text': 'Even if the training data is large, it is quite efficient.', 'start': 575.234, 'duration': 2.942}, {'end': 584.22, 'text': 'The only disadvantage with KNN algorithm is that there is no need to determine the value of K,', 'start': 578.656, 'duration': 5.564}, {'end': 587.362, 'text': 'and computation cost is pretty high compared to other algorithms.', 'start': 584.22, 'duration': 3.142}, {'end': 593.157, 'text': 'Use cases include industrial applications to look for similar tasks in comparison to others.', 'start': 588.234, 'duration': 4.923}, {'end': 595.759, 'text': 'Then we have handwriting detection applications.', 'start': 593.637, 'duration': 2.122}, {'end': 599.141, 'text': 'We can use it for image recognition video recognition, etc.', 'start': 596.119, 'duration': 3.022}, {'end': 601.622, 'text': 'Or we can also use it for stock analysis.', 'start': 599.741, 'duration': 1.881}, {'end': 604.204, 'text': 'That is all about KNN algorithm guys.', 'start': 602.222, 'duration': 1.982}, {'end': 606.325, 'text': "Let's take a look at the decision tree algorithm.", 'start': 604.304, 'duration': 2.021}, {'end': 611.188, 'text': 'So that decision tree algorithm builds the classifier model in the form of a tree structure.', 'start': 606.785, 'duration': 4.403}, {'end': 615.751, 'text': 'It uses the if-then rules, which are equally exhaustive and mutually exclusive.', 'start': 611.728, 'duration': 4.023}, {'end': 617.112, 'text': 'in classification,', 'start': 615.751, 'duration': 1.361}, {'end': 625.379, 'text': 'the process goes on with breaking down the data into similar structures or smaller structures and eventually associating it with an incremental decision tree.', 'start': 617.112, 'duration': 8.267}], 'summary': 'Knn algorithm is simple, efficient, but computationally expensive. use cases include industrial, handwriting, image and video recognition, and stock analysis. decision tree builds classifier model using if-then rules in the form of a tree structure.', 'duration': 58.59, 'max_score': 566.789, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXdum128xww/pics/pXdum128xww566789.jpg'}, {'end': 729.538, 'src': 'embed', 'start': 688.134, 'weight': 0, 'content': [{'end': 692.037, 'text': 'Talking about the use cases it can be used for data exploration pattern recognition.', 'start': 688.134, 'duration': 3.903}, {'end': 697.821, 'text': 'We can use it for option pricing and finances and we can also use it for identifying disease and risk threats.', 'start': 692.097, 'duration': 5.724}, {'end': 699.802, 'text': 'So that is all about decision tree.', 'start': 698.421, 'duration': 1.381}, {'end': 701.783, 'text': "Let's take a look at random forest algorithm.", 'start': 699.842, 'duration': 1.941}, {'end': 708.368, 'text': 'So the random decision trees or random forest are an ensemble learning method for classification regression, etc.', 'start': 702.604, 'duration': 5.764}, {'end': 716.934, 'text': 'It operates by constructing a multitude of decision trees at training time and outputs the class, that is, the mode of the classes,', 'start': 709.352, 'duration': 7.582}, {'end': 720.415, 'text': 'or the classification or mean prediction of the individual trees.', 'start': 716.934, 'duration': 3.481}, {'end': 729.538, 'text': 'a random Forest is a meta estimator that fits a number of trees on various sub samples of data and then uses an average to improve the accuracy.', 'start': 720.415, 'duration': 9.123}], 'summary': 'Decision tree and random forest are used for data exploration, option pricing, disease identification, and risk threats. random forest operates by constructing multiple decision trees to improve accuracy.', 'duration': 41.404, 'max_score': 688.134, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXdum128xww/pics/pXdum128xww688134.jpg'}, {'end': 834.319, 'src': 'embed', 'start': 806.685, 'weight': 5, 'content': [{'end': 810.246, 'text': 'wings are applied to the signals passing from one layer to the other,', 'start': 806.685, 'duration': 3.561}, {'end': 815.867, 'text': 'and these are the wings that are tuned in the training phase to adapt a neural network for any problem statement.', 'start': 810.246, 'duration': 5.621}, {'end': 817.926, 'text': 'Now talking about the advantages.', 'start': 816.365, 'duration': 1.561}, {'end': 827.994, 'text': 'it has a high tolerance to noisy data and it is able to classify untrained patterns, and it performs better with continuous valued inputs and outputs.', 'start': 817.926, 'duration': 10.068}, {'end': 834.319, 'text': 'the disadvantage with the artificial neural networks is that it has poor interpretation compared to other models.', 'start': 827.994, 'duration': 6.325}], 'summary': 'Neural networks have high noise tolerance and classification capabilities, but poor interpretability.', 'duration': 27.634, 'max_score': 806.685, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXdum128xww/pics/pXdum128xww806685.jpg'}, {'end': 876.715, 'src': 'embed', 'start': 850.407, 'weight': 6, 'content': [{'end': 860.03, 'text': 'So the support vector machine is a classifier that represents the training data as points in space which are separated into categories by a gap as wide as possible.', 'start': 850.407, 'duration': 9.623}, {'end': 866.252, 'text': 'new points are then added to the space by predicting which category they fall into and which space they will belong to.', 'start': 860.03, 'duration': 6.222}, {'end': 876.715, 'text': 'the advantages include it uses a subset of training points in the decision function which makes it memory efficient and it is very highly effective in high dimensional spaces.', 'start': 866.252, 'duration': 10.463}], 'summary': 'Support vector machine classifies data efficiently in high dimensions.', 'duration': 26.308, 'max_score': 850.407, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXdum128xww/pics/pXdum128xww850407.jpg'}, {'end': 931.712, 'src': 'embed', 'start': 905.097, 'weight': 7, 'content': [{'end': 911.481, 'text': 'So the most important part after the completion of any classifier is the evaluation to check its accuracy and efficiency.', 'start': 905.097, 'duration': 6.384}, {'end': 914.862, 'text': 'So there are a lot of ways in which we can evaluate a classifier.', 'start': 911.881, 'duration': 2.981}, {'end': 920.446, 'text': "So I'm going to take a look at these methods list down below which are holdout method then we have cross validation.", 'start': 914.882, 'duration': 5.564}, {'end': 924.948, 'text': 'There is classification report and then we have ROC curve as well.', 'start': 920.526, 'duration': 4.422}, {'end': 927.349, 'text': "So let's talk about the holdout method guys.", 'start': 925.448, 'duration': 1.901}, {'end': 931.712, 'text': 'The holdout method is the most common method to evaluate a classifier.', 'start': 928.19, 'duration': 3.522}], 'summary': 'Evaluation is crucial after classifier completion. methods include holdout, cross validation, classification report, and roc curve.', 'duration': 26.615, 'max_score': 905.097, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXdum128xww/pics/pXdum128xww905097.jpg'}], 'start': 566.789, 'title': 'Classification algorithms in machine learning', 'summary': 'Covers decision tree, random forest, artificial neural networks and support vector machine algorithms for classification in machine learning, highlighting their advantages, use cases, and classifier evaluation methods, including holdout method, cross validation, classification report, and roc curve.', 'chapters': [{'end': 606.325, 'start': 566.789, 'title': 'Knn algorithm overview', 'summary': 'Discusses the advantages and disadvantages of the knn algorithm, highlighting its simplicity, robustness with noisy training data, and efficiency with large datasets, while noting the need to determine the value of k and its high computation cost. use cases for knn include industrial applications, handwriting and image detection, video recognition, and stock analysis.', 'duration': 39.536, 'highlights': ['The KNN algorithm is simple in its implementation, robust with noisy training data, and efficient with large datasets, but requires determining the value of K and has a high computation cost.', 'Use cases for KNN algorithm include industrial applications, handwriting detection, image recognition, video recognition, and stock analysis.']}, {'end': 1044.413, 'start': 606.785, 'title': 'Classification algorithms in machine learning', 'summary': 'Discusses decision tree, random forest, artificial neural networks and support vector machine algorithms for classification in machine learning, highlighting their advantages, disadvantages, use cases, and classifier evaluation methods including holdout method, cross validation, classification report, and roc curve.', 'duration': 437.628, 'highlights': ['Classification Algorithms Overview The chapter covers decision tree, random forest, artificial neural networks, and support vector machine algorithms, discussing their advantages, disadvantages, and use cases.', 'Decision Tree Algorithm The decision tree algorithm builds a classifier model using if-then rules, can handle both categorical and numerical data, and has advantages of simplicity and little data preparation, with the disadvantage of potentially creating complex and unstable trees.', 'Random Forest Algorithm The random forest algorithm is an ensemble learning method that constructs multiple decision trees, providing higher accuracy than decision trees due to reduction in overfitting, but is complex in implementation and slow in real-time prediction.', 'Artificial Neural Networks Artificial neural networks consist of neurons arranged in layers, providing high tolerance to noisy data, ability to classify untrained patterns, and better performance with continuous valued inputs and outputs, but with the disadvantage of poor interpretation compared to other models.', 'Support Vector Machine The support vector machine classifier represents training data as points in space, is memory efficient and highly effective in high dimensional spaces, but does not directly provide probability estimates.', "Classifier Evaluation Methods The chapter explains holdout method, cross validation, classification report, and ROC curve as methods for evaluating classifiers, with detailed explanations of each method's process and purpose."]}], 'duration': 477.624, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXdum128xww/pics/pXdum128xww566789.jpg', 'highlights': ['The chapter covers decision tree, random forest, artificial neural networks, and support vector machine algorithms, discussing their advantages, disadvantages, and use cases.', 'The KNN algorithm is simple in its implementation, robust with noisy training data, and efficient with large datasets, but requires determining the value of K and has a high computation cost.', 'Use cases for KNN algorithm include industrial applications, handwriting detection, image recognition, video recognition, and stock analysis.', 'The decision tree algorithm builds a classifier model using if-then rules, can handle both categorical and numerical data, and has advantages of simplicity and little data preparation, with the disadvantage of potentially creating complex and unstable trees.', 'The random forest algorithm is an ensemble learning method that constructs multiple decision trees, providing higher accuracy than decision trees due to reduction in overfitting, but is complex in implementation and slow in real-time prediction.', 'Artificial neural networks consist of neurons arranged in layers, providing high tolerance to noisy data, ability to classify untrained patterns, and better performance with continuous valued inputs and outputs, but with the disadvantage of poor interpretation compared to other models.', 'The support vector machine classifier represents training data as points in space, is memory efficient and highly effective in high dimensional spaces, but does not directly provide probability estimates.', "The chapter explains holdout method, cross validation, classification report, and ROC curve as methods for evaluating classifiers, with detailed explanations of each method's process and purpose."]}, {'end': 1623.788, 'segs': [{'end': 1079.419, 'src': 'embed', 'start': 1044.453, 'weight': 1, 'content': [{'end': 1048.954, 'text': "Let's talk about algorithm selection, how we can choose algorithm for our given data set,", 'start': 1044.453, 'duration': 4.501}, {'end': 1052.255, 'text': 'so we can follow the following steps to use the best algorithm for the model', 'start': 1048.954, 'duration': 3.301}, {'end': 1054.835, 'text': 'So first of all, we have to read the data after that.', 'start': 1052.375, 'duration': 2.46}, {'end': 1059.797, 'text': 'We have to create dependent and independent data sets based on our dependent and independent features.', 'start': 1054.855, 'duration': 4.942}, {'end': 1065.918, 'text': 'Then we are going to split the data into training and testing sets after that we train the model using the different algorithms.', 'start': 1060.257, 'duration': 5.661}, {'end': 1067.639, 'text': 'We can do the trial and error over here.', 'start': 1065.979, 'duration': 1.66}, {'end': 1071.751, 'text': 'I can use KNN decision tree SVM logistic regression, etc.', 'start': 1068.188, 'duration': 3.563}, {'end': 1073.854, 'text': 'After we are done training the model.', 'start': 1072.352, 'duration': 1.502}, {'end': 1076.997, 'text': 'We are going to evaluate the classifier after the evaluation.', 'start': 1073.914, 'duration': 3.083}, {'end': 1079.419, 'text': 'We will choose the classifier with the most accuracy.', 'start': 1077.117, 'duration': 2.302}], 'summary': 'Select best algorithm by evaluating classifiers for maximum accuracy', 'duration': 34.966, 'max_score': 1044.453, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXdum128xww/pics/pXdum128xww1044453.jpg'}, {'end': 1127.778, 'src': 'embed', 'start': 1099.035, 'weight': 0, 'content': [{'end': 1101.116, 'text': 'So first of all, let me tell you what is MNIST.', 'start': 1099.035, 'duration': 2.081}, {'end': 1107.457, 'text': 'It is a set of 70, 000 small handwritten images labeled with the respective digit that they represent.', 'start': 1101.676, 'duration': 5.781}, {'end': 1115.16, 'text': 'So each image has almost 784 features a feature simply represents the pixels density and each image is 28 by 28 pixels.', 'start': 1107.858, 'duration': 7.302}, {'end': 1121.836, 'text': "So what we'll do is we will make a digit predictor using the MNIST data set with the help of different classifiers.", 'start': 1116.815, 'duration': 5.021}, {'end': 1127.778, 'text': "So I'm going to use logistic regression and I will use the SVM classifier as well for this problem statement.", 'start': 1121.876, 'duration': 5.902}], 'summary': 'Mnist dataset has 70,000 handwritten images with 784 features, aiming to create a digit predictor using logistic regression and svm.', 'duration': 28.743, 'max_score': 1099.035, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXdum128xww/pics/pXdum128xww1099035.jpg'}, {'end': 1479.919, 'src': 'embed', 'start': 1432.275, 'weight': 5, 'content': [{'end': 1440.921, 'text': "So I'm going to take the first 600, or let's say 6, 000 data points because it is a very last data set and it's going to take a lot of time.", 'start': 1432.275, 'duration': 8.646}, {'end': 1444.304, 'text': 'fight. include all the data sets over there for X test.', 'start': 1440.921, 'duration': 3.383}, {'end': 1447.385, 'text': 'I am going to take after 6, 000.', 'start': 1444.424, 'duration': 2.961}, {'end': 1448.987, 'text': "I'll take thousand more samples.", 'start': 1447.386, 'duration': 1.601}, {'end': 1453.811, 'text': "So let's just say this and similarly for y train and y test as well.", 'start': 1449.708, 'duration': 4.103}, {'end': 1465.696, 'text': 'Why 6000 why 6000 and 7000 so we have done the splitting of the data as well after this.', 'start': 1455.313, 'duration': 10.383}, {'end': 1471.257, 'text': "I'm going to do the shuffling of the data shuffling is just to you know, make it a more efficient model.", 'start': 1465.876, 'duration': 5.381}, {'end': 1475.958, 'text': "There's nothing involved in the shuffling but for that you will have to import the numpy library as well.", 'start': 1471.457, 'duration': 4.501}, {'end': 1479.919, 'text': 'So make sure you have installed the numpy library along with sklown and matplotlib.', 'start': 1476.418, 'duration': 3.501}], 'summary': 'Using 6000 data points and shuffling for efficient model training.', 'duration': 47.644, 'max_score': 1432.275, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXdum128xww/pics/pXdum128xww1432275.jpg'}], 'start': 1044.453, 'title': 'Algorithm selection for model efficiency', 'summary': 'Discusses the process of algorithm selection for model efficiency, emphasizing the steps of reading data, creating dependent and independent data sets, splitting the data into training and testing sets, training the model using different algorithms, evaluating the classifier, and choosing the classifier with the highest accuracy. it also highlights the use of mnist dataset, consisting of 70,000 small handwritten images with 784 features, to create a digit predictor using different classifiers.', 'chapters': [{'end': 1121.836, 'start': 1044.453, 'title': 'Algorithm selection for model efficiency', 'summary': 'Discusses the process of algorithm selection for model efficiency, emphasizing the steps of reading data, creating dependent and independent data sets, splitting the data into training and testing sets, training the model using different algorithms, evaluating the classifier, and choosing the classifier with the highest accuracy. it also highlights the use of mnist dataset, consisting of 70,000 small handwritten images with 784 features, to create a digit predictor using different classifiers.', 'duration': 77.383, 'highlights': ["The MNIST dataset consists of 70,000 small handwritten images labeled with the respective digit they represent, with each image having almost 784 features representing pixels density and being 28 by 28 pixels in size. The MNIST dataset contains 70,000 labeled images with 784 features, representing the pixels' density, and each image being 28x28 pixels in size.", 'The chapter emphasizes the process of algorithm selection for model efficiency, including reading data, creating dependent and independent data sets, splitting the data into training and testing sets, training the model using different algorithms, evaluating the classifier, and choosing the classifier with the highest accuracy. The chapter focuses on algorithm selection for model efficiency, covering steps such as reading data, creating dependent and independent data sets, splitting the data, training the model, evaluating the classifier, and choosing the most accurate classifier.', 'The model efficiency is determined by choosing the classifier with the highest accuracy after evaluating the trained models. Model efficiency is determined by selecting the classifier with the highest accuracy after evaluating the trained models.']}, {'end': 1623.788, 'start': 1121.876, 'title': 'Classification in machine learning', 'summary': 'Covers the process of loading, exploring, and visualizing the mnist dataset for classification, along with the steps of data segregation, reshaping, splitting, shuffling, and creating a digit predictor using logistic regression and svm classifier.', 'duration': 501.912, 'highlights': ['The process involves loading, exploring, and visualizing the MNIST dataset for classification, along with data segregation, reshaping, splitting, shuffling, and creating a digit predictor using logistic regression and SVM classifier. This encompasses the core processes and techniques involved in working with the MNIST dataset for classification, including loading, exploring, visualizing data, segregating data into variables, reshaping images, splitting data into training and testing sets, shuffling data, and creating a digit predictor using logistic regression and SVM classifier.', 'The dataset contains 70,000 small handwritten images, and the chapter demonstrates the process of visualizing and exploring these images. The dataset comprises 70,000 small handwritten images, and the chapter illustrates the process of visualizing and exploring these images, demonstrating the practical aspects of working with the dataset.', 'The splitting of the data into training and testing sets involves selecting the first 6,000 data points for training and the subsequent 1,000 data points for testing. The process of splitting the data involves selecting the first 6,000 data points for training and the subsequent 1,000 data points for testing, demonstrating the specific approach used in dividing the dataset for training and testing purposes.', "The shuffling of the data is performed to make the model more efficient, and the chapter explains the process of shuffling using numpy library functions. The chapter explains the purpose of shuffling the data to enhance the efficiency of the model, and demonstrates the process using numpy library functions, providing insights into improving the model's effectiveness."]}], 'duration': 579.335, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXdum128xww/pics/pXdum128xww1044453.jpg', 'highlights': ['The MNIST dataset consists of 70,000 small handwritten images labeled with the respective digit they represent, with each image having almost 784 features representing pixels density and being 28 by 28 pixels in size.', 'The chapter emphasizes the process of algorithm selection for model efficiency, including reading data, creating dependent and independent data sets, splitting the data into training and testing sets, training the model using different algorithms, evaluating the classifier, and choosing the classifier with the highest accuracy.', 'The model efficiency is determined by choosing the classifier with the highest accuracy after evaluating the trained models.', 'The process involves loading, exploring, and visualizing the MNIST dataset for classification, along with data segregation, reshaping, splitting, shuffling, and creating a digit predictor using logistic regression and SVM classifier.', 'The dataset contains 70,000 small handwritten images, and the chapter demonstrates the process of visualizing and exploring these images.', 'The splitting of the data into training and testing sets involves selecting the first 6,000 data points for training and the subsequent 1,000 data points for testing.', 'The shuffling of the data is performed to make the model more efficient, and the chapter explains the process of shuffling using numpy library functions.']}, {'end': 1991.973, 'segs': [{'end': 1674.348, 'src': 'embed', 'start': 1645.616, 'weight': 2, 'content': [{'end': 1649.157, 'text': 'So for that we have to import.', 'start': 1645.616, 'duration': 3.541}, {'end': 1655.252, 'text': 'Linear model we are going to import logistic regression.', 'start': 1650.738, 'duration': 4.514}, {'end': 1659.977, 'text': 'Yes, and after this we are going to make a classifier.', 'start': 1656.453, 'duration': 3.524}, {'end': 1671.087, 'text': "Let's just say CLF and inside this we're going to use logistic regression and we're going to take the tolerance is equal to let's say 0.1.", 'start': 1660.017, 'duration': 11.07}, {'end': 1674.348, 'text': 'So we have successfully generated the model that is the logistic regression.', 'start': 1671.087, 'duration': 3.261}], 'summary': 'Imported linear model, created logistic regression classifier with tolerance 0.1.', 'duration': 28.732, 'max_score': 1645.616, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXdum128xww/pics/pXdum128xww1645616.jpg'}, {'end': 1826.972, 'src': 'embed', 'start': 1798.776, 'weight': 1, 'content': [{'end': 1804.998, 'text': 'with the accuracy of 0.97 for the logistic regression classifier that we have just made for this digit classifier.', 'start': 1798.776, 'duration': 6.222}, {'end': 1811.74, 'text': "But let's say instead of this, okay, not just one because the classifier might mistake it for 9 as well.", 'start': 1805.738, 'duration': 6.002}, {'end': 1813.06, 'text': 'Wait a minute.', 'start': 1812.54, 'duration': 0.52}, {'end': 1814.76, 'text': 'Not even 0.', 'start': 1813.8, 'duration': 0.96}, {'end': 1818.028, 'text': "Okay, I'll just make it at 4 5 6.", 'start': 1814.76, 'duration': 3.268}, {'end': 1819.189, 'text': 'So they made a six right now.', 'start': 1818.029, 'duration': 1.16}, {'end': 1826.972, 'text': "So instead of the model that we have logistic regression, I'm going to use that support vector machine or SVM.", 'start': 1820.01, 'duration': 6.962}], 'summary': 'Logistic regression achieved 97% accuracy, considering potential misclassification with 9. transitioning to support vector machine (svm).', 'duration': 28.196, 'max_score': 1798.776, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXdum128xww/pics/pXdum128xww1798776.jpg'}, {'end': 1954.618, 'src': 'embed', 'start': 1917.61, 'weight': 0, 'content': [{'end': 1923.814, 'text': 'but the cross validation shows much better accuracy with the logistic regression classifier instead of support vector machine classifier.', 'start': 1917.61, 'duration': 6.204}, {'end': 1926.836, 'text': 'It is only because we have a lot of values over here.', 'start': 1924.775, 'duration': 2.061}, {'end': 1934.413, 'text': 'and the number of values that it turns true is a lot lesser than the number of values of Falls.', 'start': 1927.851, 'duration': 6.562}, {'end': 1941.755, 'text': "but naturally it's going to make it much more efficient with the case of logistic regression, because it takes only, you know, the binomial,", 'start': 1934.413, 'duration': 7.342}, {'end': 1947.956, 'text': 'or it works fine when the outcome is either true or false, you know, or 1 and 0 like categorical output.', 'start': 1941.755, 'duration': 6.201}, {'end': 1954.618, 'text': 'So in that case that is only the reason why logistic regression is performing better or has a much more accuracy than the SVM model over here.', 'start': 1947.976, 'duration': 6.642}], 'summary': 'Cross validation shows logistic regression has better accuracy than svm due to categorical output.', 'duration': 37.008, 'max_score': 1917.61, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXdum128xww/pics/pXdum128xww1917610.jpg'}], 'start': 1624.386, 'title': 'Logistic regression efficiency', 'summary': 'Discusses building a logistic regression classifier to predict if an image represents the number 2 or not, achieving a 97% accuracy through cross-validation. it also compares the performance of logistic regression and support vector machine classifiers on the mnist dataset, with logistic regression achieving a 97% accuracy and demonstrating its efficiency on binomial outcomes.', 'chapters': [{'end': 1798.776, 'start': 1624.386, 'title': 'Logistic regression image predictor', 'summary': 'Discusses building a logistic regression classifier to predict if an image represents the number 2 or not, achieving a 97% accuracy through cross-validation.', 'duration': 174.39, 'highlights': ['The logistic regression classifier achieved a 97% accuracy through cross-validation, demonstrating its effectiveness in predicting whether an image represents the number 2 or not.', 'The model was successfully generated using logistic regression with a tolerance of 0.1, showcasing the process of building the predictor.', 'The process involved fitting the training data and then using the prediction method to determine if a random digit represented the number 2, showcasing the practical application of the classifier.']}, {'end': 1991.973, 'start': 1798.776, 'title': 'Logistic regression vs. support vector machine', 'summary': 'Compares the performance of logistic regression and support vector machine classifiers on the mnist dataset, with logistic regression achieving a 97% accuracy and svm achieving a 90% accuracy through cross-validation, demonstrating the superiority of logistic regression due to better efficiency on binomial outcomes.', 'duration': 193.197, 'highlights': ["Logistic regression achieved 97% accuracy, outperforming the support vector machine classifier's 90% accuracy through cross-validation.", "The difference in accuracy is attributed to logistic regression's efficiency in handling binomial outcomes, leading to better performance on the given structured dataset.", "The session concludes with a reminder to subscribe to Edureka for more tutorials and updates, emphasizing the importance of the structured dataset in determining the classifier's performance."]}], 'duration': 367.587, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXdum128xww/pics/pXdum128xww1624386.jpg', 'highlights': ["Logistic regression achieved 97% accuracy, outperforming the support vector machine classifier's 90% accuracy through cross-validation.", 'The logistic regression classifier achieved a 97% accuracy through cross-validation, demonstrating its effectiveness in predicting whether an image represents the number 2 or not.', 'The model was successfully generated using logistic regression with a tolerance of 0.1, showcasing the process of building the predictor.', "The difference in accuracy is attributed to logistic regression's efficiency in handling binomial outcomes, leading to better performance on the given structured dataset."]}], 'highlights': ['Covers various classification algorithms, classifier evaluation, and algorithm selection', 'Demonstrates the use of MNIST data for digit classification', 'The logistic regression classifier achieved a 97% accuracy through cross-validation, demonstrating its effectiveness in predicting whether an image represents the number 2 or not', "Logistic regression achieved 97% accuracy, outperforming the support vector machine classifier's 90% accuracy through cross-validation", 'The process involves loading, exploring, and visualizing the MNIST dataset for classification, along with data segregation, reshaping, splitting, shuffling, and creating a digit predictor using logistic regression and SVM classifier', 'The chapter covers decision tree, random forest, artificial neural networks, and support vector machine algorithms, discussing their advantages, disadvantages, and use cases', 'The KNN algorithm is simple in its implementation, robust with noisy training data, and efficient with large datasets, but requires determining the value of K and has a high computation cost', "The chapter explains holdout method, cross validation, classification report, and ROC curve as methods for evaluating classifiers, with detailed explanations of each method's process and purpose", 'The MNIST dataset consists of 70,000 small handwritten images labeled with the respective digit they represent, with each image having almost 784 features representing pixels density and being 28 by 28 pixels in size', 'The splitting of the data into training and testing sets involves selecting the first 6,000 data points for training and the subsequent 1,000 data points for testing']}