title
Scikit Learn Tutorial | Machine Learning with Python | Python for Data Science Training | Edureka

description
Python Certification Training for Data Science : https://www.edureka.co/python-programming-certification-training This Edureka video on "Scikit-learn Tutorial" introduces you to machine learning in Python. It will also takes you through regression and clustering techniques along with a demo on SVM classification on the famous iris dataset. This video helps you to learn the below topics: 1. Machine learning Overview 2. Introduction to Scikit-learn 3. Installation of Scikit-learn 4. Regression and Classification 5. Demo Subscribe to our channel to get video updates. Hit the subscribe button above. Check out our Python Training Playlist: https://goo.gl/Na1p9G PG in Artificial Intelligence and Machine Learning with NIT Warangal : https://www.edureka.co/post-graduate/machine-learning-and-ai Post Graduate Certification in Data Science with IIT Guwahati - https://www.edureka.co/post-graduate/data-science-program (450+ Hrs || 9 Months || 20+ Projects & 100+ Case studies) #Python #PythonForDataScience #PythonTutorial #PythonForBeginners #PythonOnlineTraining 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 Course on Python helps you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes and Q-Learning. Throughout the Python Certification Course, you’ll be solving real life case studies on Media, Healthcare, Social Media, Aviation, HR. During our Python Certification Training, our instructors will help you to: 1. Master the basic and advanced concepts of Python 2. Gain insight into the 'Roles' played by a Machine Learning Engineer 3. Automate data analysis using python 4. Gain expertise in machine learning using Python and build a Real Life Machine Learning application 5. Understand the supervised and unsupervised learning and concepts of Scikit-Learn 6. Explain Time Series and it’s related concepts 7. Perform Text Mining and Sentimental analysis 8. Gain expertise to handle business in future, living the present 9. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience - - - - - - - - - - - - - - - - - - - Why learn Python? Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license. Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain. For more information, Please write back to us at sales@edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll free). 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

detail
{'title': 'Scikit Learn Tutorial | Machine Learning with Python | Python for Data Science Training | Edureka', 'heatmap': [{'end': 1075.722, 'start': 1044.237, 'weight': 0.752}, {'end': 1181.749, 'start': 1112.918, 'weight': 0.743}, {'end': 1299.49, 'start': 1244.363, 'weight': 0.857}], 'summary': 'Tutorial covers scikit-learn, machine learning applications, svm classifier, jupyter notebook basics, implementing svm and knn classifiers, and machine learning implementation in python, achieving 96% accuracy in classifying iris flower data.', 'chapters': [{'end': 91.626, 'segs': [{'end': 44.714, 'src': 'embed', 'start': 19.471, 'weight': 0, 'content': [{'end': 26.255, 'text': "then we'll directly move on to the hands-on part wherein I'll help you guys with the installation of scikit-learn and then we'll implement various algorithms.", 'start': 19.471, 'duration': 6.784}, {'end': 31.598, 'text': 'So for that will be understanding regression and classification techniques followed by a practical demo.', 'start': 26.795, 'duration': 4.803}, {'end': 37.529, 'text': "So over here, I'll be implementing SVM classifier as well as logistic regression and care nearest neighbors.", 'start': 32.045, 'duration': 5.484}, {'end': 40.031, 'text': 'So I hope you guys are clear with this agenda.', 'start': 38.109, 'duration': 1.922}, {'end': 44.714, 'text': 'So kindly give me a quick confirmation or you can just type in your chat box so that we can proceed.', 'start': 40.571, 'duration': 4.143}], 'summary': 'Hands-on session includes installation of scikit-learn, implementing regression and classification techniques, and practical demo of svm classifier, logistic regression, and k-nearest neighbors.', 'duration': 25.243, 'max_score': 19.471, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/bwZ3Qiuj3i8/pics/bwZ3Qiuj3i819471.jpg'}, {'end': 91.626, 'src': 'embed', 'start': 59.707, 'weight': 1, 'content': [{'end': 67.692, 'text': 'So machine learning is a type of artificial intelligence that allows software applications to learn from the data without any human intervention,', 'start': 59.707, 'duration': 7.985}, {'end': 70.034, 'text': 'and this also helps you to predict the outcomes as well.', 'start': 67.692, 'duration': 2.342}, {'end': 73.176, 'text': 'So let me take a very basic example to explain this concept.', 'start': 70.374, 'duration': 2.802}, {'end': 75.338, 'text': 'So have you guys ever shopped online?', 'start': 73.817, 'duration': 1.521}, {'end': 80.001, 'text': "So, while checking for a product, did you notice it recommends products similar to what you're looking for?", 'start': 75.638, 'duration': 4.363}, {'end': 82.783, 'text': 'or did you see the person bought this product also bought this.', 'start': 80.001, 'duration': 2.782}, {'end': 87.286, 'text': 'that is, the combination of products and, for every user, these different set of recommendations.', 'start': 82.783, 'duration': 4.503}, {'end': 91.626, 'text': 'Now, have you ever wonder how are they doing this recommendation or who does this recommendations?', 'start': 87.745, 'duration': 3.881}], 'summary': 'Machine learning enables software to predict outcomes and provide personalized recommendations based on user behavior.', 'duration': 31.919, 'max_score': 59.707, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/bwZ3Qiuj3i8/pics/bwZ3Qiuj3i859707.jpg'}], 'start': 4.963, 'title': 'Scikit-learn tutorial', 'summary': 'Introduces a tutorial on scikit-learn, covering machine learning in python, installation, regression, classification techniques, and practical demos, with confirmations from the audience, and an explanation of machine learning and its applications in predicting outcomes and making recommendations in online shopping.', 'chapters': [{'end': 91.626, 'start': 4.963, 'title': 'Scikit-learn tutorial', 'summary': 'Introduces a tutorial on scikit-learn, covering machine learning, implementation in python, installation, regression, classification techniques, and practical demos, with confirmations from the audience, and an explanation of machine learning and its applications in predicting outcomes and making recommendations in online shopping.', 'duration': 86.663, 'highlights': ['The chapter covers machine learning, implementation in Python, installation, regression, classification techniques, and practical demos, with confirmations from the audience, and an explanation of machine learning and its applications in predicting outcomes and making recommendations in online shopping.', 'The tutorial includes implementations of SVM classifier, logistic regression, and care nearest neighbors.', 'Machine learning is a type of artificial intelligence that allows software applications to learn from the data without any human intervention, and this also helps you to predict the outcomes as well.', 'Online shopping recommendations are based on the data of user interactions and purchases, with the system making recommendations based on similar products and combinations of products purchased by different users.']}], 'duration': 86.663, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/bwZ3Qiuj3i8/pics/bwZ3Qiuj3i84963.jpg', 'highlights': ['The tutorial includes implementations of SVM classifier, logistic regression, and care nearest neighbors.', 'Online shopping recommendations are based on the data of user interactions and purchases, with the system making recommendations based on similar products and combinations of products purchased by different users.', 'The chapter covers machine learning, implementation in Python, installation, regression, classification techniques, and practical demos, with confirmations from the audience, and an explanation of machine learning and its applications in predicting outcomes and making recommendations in online shopping.', 'Machine learning is a type of artificial intelligence that allows software applications to learn from the data without any human intervention, and this also helps you to predict the outcomes as well.']}, {'end': 341.248, 'segs': [{'end': 141.543, 'src': 'embed', 'start': 112.873, 'weight': 0, 'content': [{'end': 117.514, 'text': 'Now coming to the programming world you exactly say what will be the input and what should be the output.', 'start': 112.873, 'duration': 4.641}, {'end': 125.395, 'text': "That's where it stays but in machine learning as in when the data comes system adjust itself to the reality and then it behaves accordingly.", 'start': 118.052, 'duration': 7.343}, {'end': 132.039, 'text': 'So this is the dynamic nature of machine learning, or you can say, artificial intelligence, that is, it learns from its own meaning.', 'start': 125.956, 'duration': 6.083}, {'end': 138.081, 'text': 'you have programmed it once, but every time it encounters a problem, it should not be programmed again, which is the main motive of it.', 'start': 132.039, 'duration': 6.042}, {'end': 141.543, 'text': 'So what it does it changes it code to the new scenarios it discovers.', 'start': 138.382, 'duration': 3.161}], 'summary': 'Machine learning dynamically adjusts to data, learning from its own meaning and adapting to new scenarios without reprogramming.', 'duration': 28.67, 'max_score': 112.873, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/bwZ3Qiuj3i8/pics/bwZ3Qiuj3i8112873.jpg'}, {'end': 250.77, 'src': 'embed', 'start': 164.201, 'weight': 1, 'content': [{'end': 168.443, 'text': 'enhance the accuracy of the model so as to predict the action based on a new data set.', 'start': 164.201, 'duration': 4.242}, {'end': 170.725, 'text': 'Now, if you look at the image over here.', 'start': 168.824, 'duration': 1.901}, {'end': 172.692, 'text': 'So in machine learning, what you do?', 'start': 171.351, 'duration': 1.341}, {'end': 177.875, 'text': 'first you train your model and based on any machine learning algorithm which is built on your data set, you build a model.', 'start': 172.692, 'duration': 5.183}, {'end': 184.919, 'text': 'So by training data I mean the initial stage, that is, every data is considered like a training data, and there, if you see,', 'start': 178.295, 'duration': 6.624}, {'end': 190.242, 'text': 'there is this feedback loop going on, which is performed again and again until we achieve a good amount of accuracy.', 'start': 184.919, 'duration': 5.323}, {'end': 194.024, 'text': 'So this is the whole idea behind machine learning and how machine learning works.', 'start': 190.602, 'duration': 3.422}, {'end': 201.486, 'text': 'Now machine learning can also be used in various other domains such as in analytics it can be used then can be used in weather forecasting.', 'start': 194.585, 'duration': 6.901}, {'end': 207.748, 'text': 'It can also be used to predict what will be the stock price next day, and all of this can be done using machine learning,', 'start': 201.806, 'duration': 5.942}, {'end': 214.249, 'text': 'where the system is learning by its own or detecting patterns so that it can take actions whenever it is exposed to a new data set.', 'start': 207.748, 'duration': 6.501}, {'end': 221.27, 'text': 'Next machine learning is classified into three types that is supervised learning, unsupervised learning and reinforcement learning.', 'start': 215.109, 'duration': 6.161}, {'end': 224.451, 'text': 'So we have already discussed all these three types of machine learning.', 'start': 221.75, 'duration': 2.701}, {'end': 225.824, 'text': 'So you can go back to the video.', 'start': 224.684, 'duration': 1.14}, {'end': 229.685, 'text': 'What is machine learning for now? Let me just recap all these three algorithms.', 'start': 225.844, 'duration': 3.841}, {'end': 231.626, 'text': 'So first is my supervised learning.', 'start': 230.205, 'duration': 1.421}, {'end': 236.467, 'text': 'So supervised learning is a process of an algorithm which is learning from the training data set,', 'start': 232.086, 'duration': 4.381}, {'end': 241.428, 'text': 'or you can think it off as a teacher supervising the learning process where you know the correct answer.', 'start': 236.467, 'duration': 4.961}, {'end': 245.969, 'text': 'but the algorithm iteratively keeps predicting the training data and is corrected by the teacher.', 'start': 241.428, 'duration': 4.541}, {'end': 250.77, 'text': 'So this learning stops whenever the algorithm achieves an acceptable level of performance.', 'start': 246.589, 'duration': 4.181}], 'summary': 'Machine learning aims to enhance model accuracy for action prediction using feedback loop. it is applied in various domains like analytics and weather forecasting, and categorized into supervised, unsupervised, and reinforcement learning.', 'duration': 86.569, 'max_score': 164.201, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/bwZ3Qiuj3i8/pics/bwZ3Qiuj3i8164201.jpg'}], 'start': 92.266, 'title': 'Machine learning', 'summary': "Provides an understanding of machine learning's dynamic nature, its applications in various domains, and its classification into supervised learning, unsupervised learning, and reinforcement learning. it emphasizes creating models, enhancing accuracy, and popular algorithms.", 'chapters': [{'end': 194.024, 'start': 92.266, 'title': 'Understanding machine learning', 'summary': 'Explains the dynamic nature of machine learning and its ability to adjust to new scenarios, highlighting the process of creating models over a data set and enhancing their accuracy to predict actions based on new data, emphasizing the feedback loop for achieving accuracy.', 'duration': 101.758, 'highlights': ['Machine learning adjusts itself to the reality and behaves accordingly, learning from its own meaning.', 'The process involves providing scenarios and past experiences to create models that detect patterns in a data set and can be adjusted to enhance accuracy for predicting actions based on new data.', 'Training data is considered at the initial stage, and a feedback loop is performed again and again until a good amount of accuracy is achieved.']}, {'end': 341.248, 'start': 194.585, 'title': 'Machine learning overview', 'summary': 'Explains the applications of machine learning in various domains, such as analytics and weather forecasting, and classifies machine learning into three types: supervised learning, unsupervised learning, and reinforcement learning, with an emphasis on their processes and popular algorithms.', 'duration': 146.663, 'highlights': ['Machine learning applications in analytics and weather forecasting Machine learning can be used in analytics and weather forecasting to predict stock prices, demonstrating its wide applications.', 'Classification of machine learning into supervised, unsupervised, and reinforcement learning The chapter discusses the classification of machine learning into three types: supervised learning, unsupervised learning, and reinforcement learning, providing an overview of each type.', 'Supervised learning process and popular algorithms including linear regression and logistic regression The supervised learning process involves learning from a training data set with a teacher supervising the process, and popular algorithms such as linear regression and logistic regression are used for predictive modeling.']}], 'duration': 248.982, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/bwZ3Qiuj3i8/pics/bwZ3Qiuj3i892266.jpg', 'highlights': ['Machine learning adjusts itself to the reality and learns from its own meaning.', 'Machine learning involves creating models that detect patterns in a data set and can be adjusted to enhance accuracy for predicting actions based on new data.', 'Training data is considered at the initial stage, and a feedback loop is performed until a good amount of accuracy is achieved.', 'Machine learning applications include analytics and weather forecasting, demonstrating its wide applications.', 'Machine learning is classified into supervised learning, unsupervised learning, and reinforcement learning, providing an overview of each type.', 'Supervised learning process involves learning from a training data set with popular algorithms such as linear regression and logistic regression used for predictive modeling.']}, {'end': 659.744, 'segs': [{'end': 383.694, 'src': 'embed', 'start': 341.808, 'weight': 0, 'content': [{'end': 348.234, 'text': "Next let's come to the main topic of a discussion that is scikit-learn or what exactly is scikit-learn or how does it helps in machine learning.", 'start': 341.808, 'duration': 6.426}, {'end': 352.898, 'text': 'So we know that scikit-learn is a library which is used to perform machine learning in Python.', 'start': 348.654, 'duration': 4.244}, {'end': 358.823, 'text': 'Now it is an open source library which is licensed under BSD and it is reusable in various contexts,', 'start': 353.358, 'duration': 5.465}, {'end': 361.165, 'text': 'encouraging academic as well as your commercial use.', 'start': 358.823, 'duration': 2.342}, {'end': 366.209, 'text': 'Next it is also built on popular libraries such as numpy scipy and natplotlib.', 'start': 361.707, 'duration': 4.502}, {'end': 373.631, 'text': 'Also the best part about scikit-learn is that it has many tuning parameters along with a wonderful documentation and a support community.', 'start': 366.629, 'duration': 7.002}, {'end': 375.372, 'text': 'So let me show you that as well.', 'start': 374.051, 'duration': 1.321}, {'end': 379.413, 'text': 'So I just open my Google and I just type in scikit-learn.', 'start': 376.352, 'duration': 3.061}, {'end': 383.694, 'text': 'So over here the first link is my official documentation of scikit-learn.', 'start': 380.193, 'duration': 3.501}], 'summary': 'Scikit-learn is an open source library for machine learning in python, built on popular libraries like numpy and scipy, with a support community and comprehensive documentation.', 'duration': 41.886, 'max_score': 341.808, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/bwZ3Qiuj3i8/pics/bwZ3Qiuj3i8341808.jpg'}, {'end': 422.837, 'src': 'embed', 'start': 398.925, 'weight': 1, 'content': [{'end': 405.149, 'text': 'We have regression clustering dimensional reduction model selection and pre-processing which is again a part of machine learning.', 'start': 398.925, 'duration': 6.224}, {'end': 409.092, 'text': 'So over here we have some applications and some algorithms to solve that.', 'start': 405.59, 'duration': 3.502}, {'end': 411.554, 'text': "So here let's say in classification.", 'start': 409.653, 'duration': 1.901}, {'end': 417.355, 'text': 'We have applications such as spam detection or image recognition in which the algorithms use an estim classifier.', 'start': 411.594, 'duration': 5.761}, {'end': 420.916, 'text': 'Then we have nearest neighbors random forest and similarly for all of them.', 'start': 417.415, 'duration': 3.501}, {'end': 422.837, 'text': 'Now, let me go back to my presentation.', 'start': 421.376, 'duration': 1.461}], 'summary': 'Machine learning involves regression, clustering, and model selection for applications like spam detection and image recognition.', 'duration': 23.912, 'max_score': 398.925, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/bwZ3Qiuj3i8/pics/bwZ3Qiuj3i8398925.jpg'}, {'end': 460.822, 'src': 'embed', 'start': 433.92, 'weight': 3, 'content': [{'end': 440.217, 'text': "you just need to go to your command line and just type in pip, install scikit-learn or, if you're using anaconda distribution,", 'start': 433.92, 'duration': 6.297}, {'end': 442.618, 'text': 'you can simply type in conda installs like it.', 'start': 440.217, 'duration': 2.401}, {'end': 446.659, 'text': 'learn now, as I mentioned, that it already contains a lot of algorithms.', 'start': 442.618, 'duration': 4.041}, {'end': 453.701, 'text': 'It has a library such as numpy scipy which makes your work easy with arrays and machine learning techniques much much easier.', 'start': 447.279, 'duration': 6.422}, {'end': 460.822, 'text': 'So now, if you go back to my scikit-learn documentation here, you will see that first we have the home page,', 'start': 454.041, 'duration': 6.781}], 'summary': 'Install scikit-learn via pip or conda for easy access to algorithms and libraries like numpy and scipy.', 'duration': 26.902, 'max_score': 433.92, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/bwZ3Qiuj3i8/pics/bwZ3Qiuj3i8433920.jpg'}, {'end': 521.923, 'src': 'embed', 'start': 494.526, 'weight': 4, 'content': [{'end': 500.371, 'text': "So to take a specific example, let's say I want to perform a linear regression which is the first machine learning algorithm that we have learned.", 'start': 494.526, 'duration': 5.845}, {'end': 506.677, 'text': "So we'll just go ahead and type in from sklearn.linear model import linear regression.", 'start': 500.832, 'duration': 5.845}, {'end': 514.784, 'text': "So here my linear underscore model is the family and the linear regression that's the model itself and then you just need to instantiate that model.", 'start': 507.197, 'duration': 7.587}, {'end': 518.267, 'text': 'So this is how you can implement any algorithm using scikit-learn.', 'start': 515.323, 'duration': 2.944}, {'end': 521.923, 'text': 'Moving ahead, let us see the concept of regression and clustering.', 'start': 518.902, 'duration': 3.021}], 'summary': 'Introduction to implementing linear regression using scikit-learn and exploring regression and clustering concepts.', 'duration': 27.397, 'max_score': 494.526, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/bwZ3Qiuj3i8/pics/bwZ3Qiuj3i8494526.jpg'}, {'end': 558.648, 'src': 'embed', 'start': 532.147, 'weight': 5, 'content': [{'end': 537.63, 'text': 'So here regression is the prediction of a numerical values that often takes input as a continuous value.', 'start': 532.147, 'duration': 5.483}, {'end': 541.691, 'text': 'So as you can see here in the graph as well, we have continuous value of data points.', 'start': 538.09, 'duration': 3.601}, {'end': 543.532, 'text': 'Then we have classification.', 'start': 542.191, 'duration': 1.341}, {'end': 548.841, 'text': 'So classification is the problem identifying to which set of categories a new observation belongs.', 'start': 543.817, 'duration': 5.024}, {'end': 555.986, 'text': 'So let us understand this with an example, where you have a given set of mail and you have to classify these mails into two categories,', 'start': 549.381, 'duration': 6.605}, {'end': 558.648, 'text': 'based on the fact that whether the mail is a spam or not.', 'start': 555.986, 'duration': 2.662}], 'summary': 'Regression predicts numerical values, while classification categorizes data into sets.', 'duration': 26.501, 'max_score': 532.147, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/bwZ3Qiuj3i8/pics/bwZ3Qiuj3i8532147.jpg'}, {'end': 648.179, 'src': 'embed', 'start': 620.043, 'weight': 6, 'content': [{'end': 623.444, 'text': 'Here we have the most popular data that is the iris data set.', 'start': 620.043, 'duration': 3.401}, {'end': 629.964, 'text': 'Now why iris data set because it is one of the oldest data sets and carries out easy supervised learning task.', 'start': 623.962, 'duration': 6.002}, {'end': 637.966, 'text': 'Now, this data set contains three classes of 50 instances each, where each class refers to a type of iris plant, that is, iris setosa,', 'start': 630.444, 'duration': 7.522}, {'end': 640.007, 'text': 'iris virginica and iris versicolor.', 'start': 637.966, 'duration': 2.041}, {'end': 644.408, 'text': 'So it has a strong measurements among all these species, that is the sepal length.', 'start': 640.327, 'duration': 4.081}, {'end': 648.179, 'text': 'Then we have simple width, we have better length and we have better width as well.', 'start': 644.778, 'duration': 3.401}], 'summary': 'The iris dataset contains three classes of 50 instances each, representing different types of iris plants, with strong measurements for sepal length, simple width, petal length, and petal width.', 'duration': 28.136, 'max_score': 620.043, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/bwZ3Qiuj3i8/pics/bwZ3Qiuj3i8620043.jpg'}], 'start': 341.808, 'title': 'Scikit-learn for machine learning', 'summary': 'Discusses the role of scikit-learn, an open source library for machine learning in python, highlighting its features such as integration with popular libraries like numpy, scipy, and matplotlib, reusable licensing, and support community.', 'chapters': [{'end': 417.355, 'start': 341.808, 'title': 'Scikit-learn for machine learning', 'summary': 'Discusses the role of scikit-learn, an open source library for machine learning in python, highlighting its features such as integration with popular libraries like numpy, scipy, and matplotlib, reusable licensing, and support community.', 'duration': 75.547, 'highlights': ['Scikit-learn is an open source library used for machine learning in Python, built on popular libraries such as numpy, scipy, and matplotlib, and licensed under BSD, encouraging academic as well as commercial use.', 'It offers numerous tuning parameters, excellent documentation, and a strong support community, making it a valuable tool for machine learning applications.', 'The official documentation of scikit-learn provides an introduction to its features, including classification, regression, clustering, dimensional reduction, model selection, and pre-processing, with applications like spam detection and image recognition.']}, {'end': 659.744, 'start': 417.415, 'title': 'Implementing machine learning with scikit-learn', 'summary': 'Discusses the process of installing scikit-learn package, importing models, implementing algorithms such as linear regression, and exploring regression, classification, and clustering techniques with examples using scikit-learn, focusing on the iris dataset and various algorithms for classification and regression.', 'duration': 242.329, 'highlights': ['Installing Scikit-Learn Package The process of installing scikit-learn is explained, including using pip or conda for installation, and the mention of scikit-learn containing a library such as numpy and scipy, making work with arrays and machine learning techniques easier.', 'Importing and Implementing Algorithms The general form of importing models from scikit-learn is discussed, including an example of importing linear regression, instantiating the model, and implementing any algorithm using scikit-learn.', 'Exploring Regression and Clustering The concepts of regression and clustering are introduced, with explanations of regression as the prediction of numerical values and classification as identifying to which set of categories a new observation belongs, along with examples and use cases in various domains.', 'Understanding the Iris Dataset for Classification The iris dataset is introduced as a popular dataset for supervised learning tasks, containing three classes of 50 instances each, and four features measured from each sample, focusing on the length and width of sepals and petals in centimeters.']}], 'duration': 317.936, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/bwZ3Qiuj3i8/pics/bwZ3Qiuj3i8341808.jpg', 'highlights': ['Scikit-learn is an open source library for machine learning in Python, built on popular libraries such as numpy, scipy, and matplotlib, and licensed under BSD, encouraging academic as well as commercial use.', 'The official documentation of scikit-learn provides an introduction to its features, including classification, regression, clustering, dimensional reduction, model selection, and pre-processing, with applications like spam detection and image recognition.', 'It offers numerous tuning parameters, excellent documentation, and a strong support community, making it a valuable tool for machine learning applications.', 'Installing Scikit-Learn Package The process of installing scikit-learn is explained, including using pip or conda for installation, and the mention of scikit-learn containing a library such as numpy and scipy, making work with arrays and machine learning techniques easier.', 'Importing and Implementing Algorithms The general form of importing models from scikit-learn is discussed, including an example of importing linear regression, instantiating the model, and implementing any algorithm using scikit-learn.', 'Exploring Regression and Clustering The concepts of regression and clustering are introduced, with explanations of regression as the prediction of numerical values and classification as identifying to which set of categories a new observation belongs, along with examples and use cases in various domains.', 'Understanding the Iris Dataset for Classification The iris dataset is introduced as a popular dataset for supervised learning tasks, containing three classes of 50 instances each, and four features measured from each sample, focusing on the length and width of sepals and petals in centimeters.']}, {'end': 838.302, 'segs': [{'end': 705.137, 'src': 'embed', 'start': 677.057, 'weight': 0, 'content': [{'end': 684.579, 'text': 'So SDM is a supervised machine learning algorithm which can be used for both classification or regression challenges, but in general,', 'start': 677.057, 'duration': 7.522}, {'end': 686.439, 'text': 'SDM is used for classification problem.', 'start': 684.579, 'duration': 1.86}, {'end': 687.78, 'text': 'So what it does?', 'start': 687.079, 'duration': 0.701}, {'end': 691.901, 'text': 'it tries to define a hyperplane which can split the data in the most optimal way,', 'start': 687.78, 'duration': 4.121}, {'end': 695.782, 'text': 'such that there is a wide margin among the hyperplane and the observation.', 'start': 691.901, 'duration': 3.881}, {'end': 698.535, 'text': 'So this is your data points plotted in this very graph.', 'start': 696.314, 'duration': 2.221}, {'end': 705.137, 'text': 'So what the SVM is doing it is defining a hyperplane which is able to segregate data into different different categories.', 'start': 698.935, 'duration': 6.202}], 'summary': 'Sdm is a supervised ml algorithm used for classification, defining a hyperplane to split data optimally.', 'duration': 28.08, 'max_score': 677.057, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/bwZ3Qiuj3i8/pics/bwZ3Qiuj3i8677057.jpg'}, {'end': 794.741, 'src': 'embed', 'start': 765.621, 'weight': 1, 'content': [{'end': 766.802, 'text': 'So these are my data points.', 'start': 765.621, 'duration': 1.181}, {'end': 773.103, 'text': 'Now I want to draw or you can say I want to create a hyperplane which will best segregate these data points.', 'start': 767.502, 'duration': 5.601}, {'end': 775.704, 'text': 'So over here my hyperplane would be a line.', 'start': 773.663, 'duration': 2.041}, {'end': 780.125, 'text': 'So this is my line which will basically segregate your data points into two halves.', 'start': 775.904, 'duration': 4.221}, {'end': 785.517, 'text': 'So, basically, a hyperplane allows you to categorize the data points into different classes,', 'start': 780.575, 'duration': 4.942}, {'end': 789.059, 'text': 'or you can simply say it segregates the data points into different different categories.', 'start': 785.517, 'duration': 3.542}, {'end': 791.3, 'text': 'So I hope I answered your question be big.', 'start': 789.519, 'duration': 1.781}, {'end': 794.741, 'text': 'Okay, the big is saying yes, certainly great baby.', 'start': 792.32, 'duration': 2.421}], 'summary': 'Creating a hyperplane to segregate data points into two categories.', 'duration': 29.12, 'max_score': 765.621, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/bwZ3Qiuj3i8/pics/bwZ3Qiuj3i8765621.jpg'}, {'end': 844.691, 'src': 'embed', 'start': 819.095, 'weight': 2, 'content': [{'end': 825.257, 'text': 'So we have features such as sepal length, sepal width, petal length and we have petal width as well and we have to classify the flowers.', 'start': 819.095, 'duration': 6.162}, {'end': 830.959, 'text': 'So here we know that we have different species of flowers that is iris satosa, virginica and versicolor.', 'start': 825.697, 'duration': 5.262}, {'end': 833.46, 'text': 'So now let us go ahead and build our model for that.', 'start': 831.459, 'duration': 2.001}, {'end': 835.581, 'text': 'So I just go to my Jupyter notebook.', 'start': 833.96, 'duration': 1.621}, {'end': 838.302, 'text': 'So this is my Jupyter notebook.', 'start': 836.961, 'duration': 1.341}, {'end': 841.97, 'text': "So I just go to Python 3 and I'll give a name to it.", 'start': 838.789, 'duration': 3.181}, {'end': 844.691, 'text': "Let's say I want to name it as sidekit learn tutorial.", 'start': 842.19, 'duration': 2.501}], 'summary': 'Building a model to classify iris flowers based on features like sepal length and width, and petal length and width.', 'duration': 25.596, 'max_score': 819.095, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/bwZ3Qiuj3i8/pics/bwZ3Qiuj3i8819095.jpg'}], 'start': 659.804, 'title': 'Support vector machine classifier', 'summary': 'Explains the concept of support vector machine (svm) classifier, a supervised machine learning algorithm used for classification problems, defining a hyperplane to segregate data into different categories. svm is considered one of the most efficient algorithms for classification.', 'chapters': [{'end': 838.302, 'start': 659.804, 'title': 'Support vector machine classifier', 'summary': 'Explains the concept of support vector machine (svm) classifier, a supervised machine learning algorithm used for classification problems, which defines a hyperplane to segregate data into different categories. it is considered one of the most efficient algorithms in machine learning for performing classification.', 'duration': 178.498, 'highlights': ['SVM is a supervised machine learning algorithm used for classification problems, aiming to define a hyperplane to split the data in the most optimal way, creating a wide margin between the hyperplane and the observations.', 'A hyperplane is a generalization of a plane used to categorize or segregate data points into different classes, allowing for the segregation of data points into different categories based on their placement relative to the hyperplane.', 'The chapter also includes a use case where an SVM classification is performed on the iris data set, which involves using the SVM classifier to classify flowers based on features such as sepal length, sepal width, petal length, and petal width into species such as iris setosa, virginica, and versicolor.']}], 'duration': 178.498, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/bwZ3Qiuj3i8/pics/bwZ3Qiuj3i8659804.jpg', 'highlights': ['SVM is a supervised machine learning algorithm used for classification problems, aiming to define a hyperplane to split the data in the most optimal way, creating a wide margin between the hyperplane and the observations.', 'A hyperplane is a generalization of a plane used to categorize or segregate data points into different classes, allowing for the segregation of data points into different categories based on their placement relative to the hyperplane.', 'The chapter also includes a use case where an SVM classification is performed on the iris data set, which involves using the SVM classifier to classify flowers based on features such as sepal length, sepal width, petal length, and petal width into species such as iris setosa, virginica, and versicolor.']}, {'end': 1675.915, 'segs': [{'end': 893.995, 'src': 'embed', 'start': 860.318, 'weight': 1, 'content': [{'end': 866.16, 'text': "let's say, you want to type in heading one and you can go to the markdown and you can just run this.", 'start': 860.318, 'duration': 5.842}, {'end': 874.724, 'text': 'So shortcut to run this is shift and enter, or you can just directly go over here and click this cell then, if you want it, in this header 2,', 'start': 867.079, 'duration': 7.645}, {'end': 877.925, 'text': 'so you can just type in 2 hash and write in any header.', 'start': 874.724, 'duration': 3.201}, {'end': 884.289, 'text': "So let's say header 2 has run this already have to change it to markdown and we just run this.", 'start': 877.945, 'duration': 6.344}, {'end': 893.995, 'text': 'So this is my header 2 similarly you can do it for header 3 so you can you just need to type in 3 hashes and you can say header 3.', 'start': 884.329, 'duration': 9.666}], 'summary': 'Demonstrating markdown formatting, using shortcuts, and creating headers 1, 2, and 3.', 'duration': 33.677, 'max_score': 860.318, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/bwZ3Qiuj3i8/pics/bwZ3Qiuj3i8860318.jpg'}, {'end': 937.012, 'src': 'embed', 'start': 909.774, 'weight': 3, 'content': [{'end': 914.695, 'text': 'So this is my bold text now similarly if you want some bullet points in it.', 'start': 909.774, 'duration': 4.921}, {'end': 917.456, 'text': 'So now first I just set it to markdown.', 'start': 915.555, 'duration': 1.901}, {'end': 918.836, 'text': 'So I say markdown.', 'start': 917.996, 'duration': 0.84}, {'end': 922.437, 'text': "So over here and say let's say point one.", 'start': 919.756, 'duration': 2.681}, {'end': 925.004, 'text': 'and then I want point two.', 'start': 923.723, 'duration': 1.281}, {'end': 927.686, 'text': 'So this will basically show me the bullet point.', 'start': 925.764, 'duration': 1.922}, {'end': 928.726, 'text': 'So let me just run this.', 'start': 927.706, 'duration': 1.02}, {'end': 931.448, 'text': 'So as you can see I have bullet points over there.', 'start': 929.207, 'duration': 2.241}, {'end': 937.012, 'text': 'So this is just the basics of Jupiter notebook and also for the shortcuts you have everything over here.', 'start': 931.848, 'duration': 5.164}], 'summary': 'Introduction to using markdown for bullet points in jupyter notebook.', 'duration': 27.238, 'max_score': 909.774, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/bwZ3Qiuj3i8/pics/bwZ3Qiuj3i8909774.jpg'}, {'end': 1075.722, 'src': 'heatmap', 'start': 1044.237, 'weight': 0.752, 'content': [{'end': 1049.64, 'text': 'So this will basically print me all the features or you can see all the specifications of your iris data set.', 'start': 1044.237, 'duration': 5.403}, {'end': 1055.023, 'text': 'Now as you can see here, I have four columns and I have some 150 rows.', 'start': 1050.16, 'duration': 4.863}, {'end': 1057.184, 'text': 'Now but these are the measurements.', 'start': 1055.723, 'duration': 1.461}, {'end': 1062.107, 'text': "So I've already told you that iris data set contains some specifications of the flower.", 'start': 1057.344, 'duration': 4.763}, {'end': 1063.7, 'text': 'So we have simple length.', 'start': 1062.52, 'duration': 1.18}, {'end': 1064.58, 'text': 'We have simple width.', 'start': 1063.72, 'duration': 0.86}, {'end': 1066.761, 'text': 'We have Peter length and we have little bit as well.', 'start': 1064.6, 'duration': 2.161}, {'end': 1068.861, 'text': 'So now you want to know the feature name.', 'start': 1067.281, 'duration': 1.58}, {'end': 1075.722, 'text': 'So you can just type in Iris dot feature name feature underscore names and just run this.', 'start': 1068.901, 'duration': 6.821}], 'summary': 'The iris dataset contains 4 columns and 150 rows, representing flower measurements and specifications.', 'duration': 31.485, 'max_score': 1044.237, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/bwZ3Qiuj3i8/pics/bwZ3Qiuj3i81044237.jpg'}, {'end': 1181.749, 'src': 'heatmap', 'start': 1112.918, 'weight': 0.743, 'content': [{'end': 1118.142, 'text': 'So as you can see here, I have 0 for setosa then 1 for versicolor and 2 for virginica.', 'start': 1112.918, 'duration': 5.224}, {'end': 1120.304, 'text': 'So this is how you can explore any data set.', 'start': 1118.482, 'duration': 1.822}, {'end': 1123.648, 'text': 'So now let me just run our SVM classification.', 'start': 1120.967, 'duration': 2.681}, {'end': 1128.231, 'text': 'So till now we have just imported our SVM and we have imported the data set.', 'start': 1124.149, 'duration': 4.082}, {'end': 1134.394, 'text': 'So now what we need to do we need to create different areas for storing a dependent variable at independent variable.', 'start': 1128.571, 'duration': 5.823}, {'end': 1140.537, 'text': "So for independent variable, let's take it as X and we'll store the value that is the features of it.", 'start': 1134.914, 'duration': 5.623}, {'end': 1145.54, 'text': "So I say iris dot data and we'll say colon comma two.", 'start': 1140.877, 'duration': 4.663}, {'end': 1150.021, 'text': 'So this is similar to I log which is used for row selection or column selection.', 'start': 1146.178, 'duration': 3.843}, {'end': 1153.343, 'text': 'Now for columns will be considering the first two features of it.', 'start': 1150.421, 'duration': 2.922}, {'end': 1156.946, 'text': 'So now let me just go ahead and create my dependent variable as well.', 'start': 1154.204, 'duration': 2.742}, {'end': 1158.547, 'text': "So I'll take it as Y.", 'start': 1157.006, 'duration': 1.541}, {'end': 1165.391, 'text': "So I'll just say Iris dot target which is the value I need to predict or you can say which is the species that the flower belongs to.", 'start': 1158.547, 'duration': 6.844}, {'end': 1170.355, 'text': 'So here the class of each observation or you can say the data point is stored in dot target.', 'start': 1165.812, 'duration': 4.543}, {'end': 1172.543, 'text': 'that is the attribute of the data set.', 'start': 1170.782, 'duration': 1.761}, {'end': 1179.028, 'text': 'So we have assigned this to a date dependent variable that is why and we have already understand what exactly is my target.', 'start': 1172.803, 'duration': 6.225}, {'end': 1181.749, 'text': 'So we need to specify whether it is a setosa.', 'start': 1179.488, 'duration': 2.261}], 'summary': 'Using svm classification to analyze iris data set with 0 setosa, 1 versicolor, and 2 virginica.', 'duration': 68.831, 'max_score': 1112.918, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/bwZ3Qiuj3i8/pics/bwZ3Qiuj3i81112918.jpg'}, {'end': 1299.49, 'src': 'heatmap', 'start': 1244.363, 'weight': 0.857, 'content': [{'end': 1252.968, 'text': "So I'll be passing independent variable that is X then the dependent variable that is Y and I specify a size to it and says I want to split the data.", 'start': 1244.363, 'duration': 8.605}, {'end': 1256.15, 'text': "Let's say 0.2 and then what I'll be doing.", 'start': 1253.228, 'duration': 2.922}, {'end': 1258.091, 'text': "I'll be giving a random state to it.", 'start': 1256.31, 'duration': 1.781}, {'end': 1264.675, 'text': 'So here random state is nothing, but it just ensure that each time we run this code will be getting the same sampling,', 'start': 1258.553, 'duration': 6.122}, {'end': 1266.835, 'text': 'so you can specify any number to it in our case.', 'start': 1264.675, 'duration': 2.16}, {'end': 1267.695, 'text': "Let's take it as code.", 'start': 1266.855, 'duration': 0.84}, {'end': 1270.616, 'text': 'So here I have for an underscore.', 'start': 1268.235, 'duration': 2.381}, {'end': 1273.217, 'text': 'All right, so this runs fine now.', 'start': 1271.296, 'duration': 1.921}, {'end': 1274.777, 'text': 'So now what you need to do?', 'start': 1273.797, 'duration': 0.98}, {'end': 1282.039, 'text': 'you need to use STM to build a classifier which helps to classify whenever we are providing a new data or any data regarding a flower.', 'start': 1274.777, 'duration': 7.262}, {'end': 1289.404, 'text': "So you can conclude to which species belongs to or you can say which plant belongs Then what I'll be doing and creating a model for that.", 'start': 1282.339, 'duration': 7.065}, {'end': 1299.49, 'text': "So I'll say model equals to SVM dot SVC, and inside that I'll be passing Colonel, which is equal to linear, and then finally,", 'start': 1289.705, 'duration': 9.785}], 'summary': 'Using svm to build a classifier for flower species classification with a 80:20 data split.', 'duration': 55.127, 'max_score': 1244.363, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/bwZ3Qiuj3i8/pics/bwZ3Qiuj3i81244363.jpg'}, {'end': 1616.105, 'src': 'embed', 'start': 1576.877, 'weight': 0, 'content': [{'end': 1583.16, 'text': 'and find it was in we have calculated the accuracy using this function accuracy score and then we have passed the variable of their subset.', 'start': 1576.877, 'duration': 6.283}, {'end': 1587.381, 'text': 'So now we have calculated the accuracy of 96% which is quite good.', 'start': 1583.64, 'duration': 3.741}, {'end': 1589.942, 'text': 'So let me just recap all of this once again.', 'start': 1587.881, 'duration': 2.061}, {'end': 1597.385, 'text': "So here I'll explain you the basics of Jupiter notebook how you can type in heading 1 header 2 header 3 then a bullet points to it.", 'start': 1590.462, 'duration': 6.923}, {'end': 1602.027, 'text': 'Then we have learned about SVM classifier where we are first imported SVM and data sets.', 'start': 1597.725, 'duration': 4.302}, {'end': 1609.601, 'text': "Then we have just load my iris data set We have printed the type of it which is bunch then I've just printed one of the attribute that is data.", 'start': 1602.367, 'duration': 7.234}, {'end': 1613.404, 'text': 'So this will basically tell me all the specifications for my iris flower.', 'start': 1609.981, 'duration': 3.423}, {'end': 1616.105, 'text': 'Then I just printed the feature name of it.', 'start': 1613.844, 'duration': 2.261}], 'summary': 'Accuracy calculated at 96% using svm classifier in jupyter notebook.', 'duration': 39.228, 'max_score': 1576.877, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/bwZ3Qiuj3i8/pics/bwZ3Qiuj3i81576877.jpg'}], 'start': 838.789, 'title': 'Basics of jupyter notebook and svm classifier', 'summary': 'Covers how to create headings and format text in a jupyter notebook using markdown, including creating header levels, bold text, and bullet points. it also discusses the implementation of a svm classifier to classify iris flower data, achieving an accuracy of 96%.', 'chapters': [{'end': 931.448, 'start': 838.789, 'title': 'Jupyter notebook basics', 'summary': 'Covers how to create headings and format text in a jupyter notebook using markdown, including creating header levels, bold text, and bullet points.', 'duration': 92.659, 'highlights': ['Explaining how to create different levels of headings using Markdown in Jupyter notebook. Creating heading levels 1, 2, and 3 using hash symbols, and demonstrating the usage of Markdown.', 'Demonstrating the process of formatting text as bold using Markdown in a Jupyter notebook. Showing the use of asterisks to create bold text and then running the Markdown to display it.', 'Showing how to create bullet points using Markdown in a Jupyter notebook. Setting the cell to Markdown and then creating bullet points using asterisks, followed by running the cell to display the bullet points.']}, {'end': 1675.915, 'start': 931.848, 'title': 'Basics of jupiter notebook and svm classifier', 'summary': 'Covers the basics of jupiter notebook, including how to type headings and bullet points, as well as the implementation of a svm classifier to classify iris flower data, achieving an accuracy of 96%.', 'duration': 744.067, 'highlights': ['Implemented SVM classifier to classify iris flower data, achieving an accuracy of 96%.', 'Explained the basics of Jupiter notebook, including heading and bullet point usage.', 'Imported and loaded iris dataset using scikit-learn library and printed its type and attributes.', 'Printed feature names and target values for the iris dataset.', 'Split the data into training and testing subsets using cross-validation.']}], 'duration': 837.126, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/bwZ3Qiuj3i8/pics/bwZ3Qiuj3i8838789.jpg', 'highlights': ['Implemented SVM classifier to classify iris flower data, achieving an accuracy of 96%.', 'Explaining how to create different levels of headings using Markdown in Jupyter notebook. Creating heading levels 1, 2, and 3 using hash symbols, and demonstrating the usage of Markdown.', 'Explained the basics of Jupiter notebook, including heading and bullet point usage.', 'Showing how to create bullet points using Markdown in a Jupyter notebook. Setting the cell to Markdown and then creating bullet points using asterisks, followed by running the cell to display the bullet points.', 'Imported and loaded iris dataset using scikit-learn library and printed its type and attributes.']}, {'end': 1948.432, 'segs': [{'end': 1714.204, 'src': 'embed', 'start': 1675.975, 'weight': 0, 'content': [{'end': 1680.198, 'text': "So what I've done, I've just imported all these arrays into my one dimension.", 'start': 1675.975, 'duration': 4.223}, {'end': 1688.804, 'text': "So I've used the function the T shape and then I simply use a model that is SVM dot SVC and I've used the functionality of kernel to linear model.", 'start': 1680.498, 'duration': 8.306}, {'end': 1693.327, 'text': 'Then I just fit my model and pass on my independent variable and dependent variable.', 'start': 1689.204, 'duration': 4.123}, {'end': 1696.801, 'text': 'Then I just calculated my accuracy over here.', 'start': 1693.8, 'duration': 3.001}, {'end': 1700.501, 'text': 'I have first imported my accuracy score that is the predefined function.', 'start': 1696.921, 'duration': 3.58}, {'end': 1704.122, 'text': 'So using this predefined function, I have just calculated my accuracy.', 'start': 1700.822, 'duration': 3.3}, {'end': 1706.583, 'text': 'So this was all about my SVM classifier.', 'start': 1704.522, 'duration': 2.061}, {'end': 1709.243, 'text': 'Next let us implement k nearest neighbors.', 'start': 1707.383, 'duration': 1.86}, {'end': 1714.204, 'text': 'So here first of all, you need to select the value of k which will basically define your nearest neighbors.', 'start': 1709.703, 'duration': 4.501}], 'summary': 'Imported arrays, used svm dot svc model with linear kernel, calculated accuracy. next, implemented k nearest neighbors.', 'duration': 38.229, 'max_score': 1675.975, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/bwZ3Qiuj3i8/pics/bwZ3Qiuj3i81675975.jpg'}, {'end': 1840.094, 'src': 'embed', 'start': 1765.455, 'weight': 2, 'content': [{'end': 1770.596, 'text': 'So sklearn.datasets is my module and load iris is the function from the sk module.', 'start': 1765.455, 'duration': 5.141}, {'end': 1773.436, 'text': 'So here I have just loaded my iris data set.', 'start': 1771.096, 'duration': 2.34}, {'end': 1784.93, 'text': "So now what I'll be doing is I'm having a variable called as X which has iris dot data and I have Y which is equal to iris dot target.", 'start': 1774.356, 'duration': 10.574}, {'end': 1786.931, 'text': 'So we have already discussed this above.', 'start': 1785.23, 'duration': 1.701}, {'end': 1788.772, 'text': 'Let me just print the shape of this.', 'start': 1787.551, 'duration': 1.221}, {'end': 1793.895, 'text': "So I'll just type in print or you can simply type in X dot shape.", 'start': 1789.152, 'duration': 4.743}, {'end': 1801.56, 'text': 'So as you can see here, I got a 2D array with 150 rows and four columns.', 'start': 1797.058, 'duration': 4.502}, {'end': 1803.762, 'text': 'Similarly, you can do it for Y as well.', 'start': 1802.081, 'duration': 1.681}, {'end': 1811.322, 'text': 'So you can say Y dot shape So here we have one day array of length 150 or you can say we need just one response value.', 'start': 1803.842, 'duration': 7.48}, {'end': 1815.783, 'text': 'So now as we have discussed to create a model, we first need to collect the data.', 'start': 1811.862, 'duration': 3.921}, {'end': 1821.105, 'text': 'We need to clean that data or that is something which is not required in my iris data set.', 'start': 1816.163, 'duration': 4.942}, {'end': 1823.286, 'text': 'Then we need to train and test the data.', 'start': 1821.545, 'duration': 1.741}, {'end': 1828.007, 'text': 'Then what we need to do, we need to split the data that is into training subset and testing subset.', 'start': 1823.486, 'duration': 4.521}, {'end': 1830.088, 'text': 'And finally we can calculate the accuracy.', 'start': 1828.288, 'duration': 1.8}, {'end': 1831.749, 'text': 'So let me just implement this.', 'start': 1830.648, 'duration': 1.101}, {'end': 1840.094, 'text': "So I'll say from sklearn.neighbors Import K neighbors classifier.", 'start': 1832.109, 'duration': 7.985}], 'summary': 'Loaded iris dataset with 150 rows and 4 columns, preparing to create a model using k neighbors classifier.', 'duration': 74.639, 'max_score': 1765.455, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/bwZ3Qiuj3i8/pics/bwZ3Qiuj3i81765455.jpg'}, {'end': 1958.036, 'src': 'embed', 'start': 1927.523, 'weight': 5, 'content': [{'end': 1932.065, 'text': 'So, as you can see here, we are just getting the same error that we got in a previous example, that is,', 'start': 1927.523, 'duration': 4.542}, {'end': 1935.206, 'text': 'reshaping your data using the array dot reshape function.', 'start': 1932.065, 'duration': 3.141}, {'end': 1940.588, 'text': 'So this error we have already encountered in the last example when we are discussing about SVM classifier.', 'start': 1935.966, 'duration': 4.622}, {'end': 1945.43, 'text': 'So over here what we can do we can simply convert this into a NumPy array.', 'start': 1941.289, 'duration': 4.141}, {'end': 1948.432, 'text': 'So for that let me just import my NumPy.', 'start': 1945.79, 'duration': 2.642}, {'end': 1958.036, 'text': "So I say import NumPy as NP then I'll be taking a variable let's say A and I'll say NP dot array and I'll pass some values to it.", 'start': 1948.632, 'duration': 9.404}], 'summary': 'Encountered error in reshaping data, converting to numpy array resolved the issue.', 'duration': 30.513, 'max_score': 1927.523, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/bwZ3Qiuj3i8/pics/bwZ3Qiuj3i81927523.jpg'}], 'start': 1675.975, 'title': 'Implementing svm and knn classifiers', 'summary': 'Covers the implementation of svm classifier using a linear model and calculating accuracy, followed by the introduction and implementation of knn classifier with a focus on selecting the value of k for defining nearest neighbors. it also covers loading the iris dataset in python using scikit-learn, creating variable x and y, printing their shapes, and discussing their dimensions and structure, in order to prepare for data modeling. additionally, it covers the process of cleaning the data, splitting it into training and testing subsets, and implementing the knn classifier with the nearest neighbor value of one to predict new observations, encountering a reshaping error that can be resolved by converting the data into a numpy array.', 'chapters': [{'end': 1734.993, 'start': 1675.975, 'title': 'Implementing svm and knn classifiers', 'summary': 'Covers the implementation of svm classifier using a linear model and calculating accuracy, followed by the introduction and implementation of knn classifier with a focus on selecting the value of k for defining nearest neighbors.', 'duration': 59.018, 'highlights': ['Using SVM classifier with linear model to fit and calculate accuracy.', 'Implementing KNN classifier and emphasizing the selection of k for defining nearest neighbors.']}, {'end': 1815.783, 'start': 1740.154, 'title': 'Loading and exploring iris dataset', 'summary': 'Covers loading the iris dataset in python using scikit-learn, creating variable x and y, printing their shapes, and discussing their dimensions and structure, in order to prepare for data modeling.', 'duration': 75.629, 'highlights': ['Loading the iris dataset using scikit-learn and creating variables X and Y for data modeling', 'Printing the shape of variable X and Y to understand the dimensions and structure of the dataset', 'Importing load_iris function from sklearn.datasets and discussing its usage']}, {'end': 1948.432, 'start': 1816.163, 'title': 'Implementing knn classifier', 'summary': 'Covers the process of cleaning the data, splitting it into training and testing subsets, and implementing the knn classifier with the nearest neighbor value of one to predict new observations, encountering a reshaping error that can be resolved by converting the data into a numpy array.', 'duration': 132.269, 'highlights': ['The process involves cleaning the data, splitting it into training and testing subsets, and implementing the KNN classifier with the nearest neighbor value of one.', 'Encountering a reshaping error when predicting new observations, which can be resolved by converting the data into a NumPy array.', 'Training the model to learn the relationship between the features and the response.']}], 'duration': 272.457, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/bwZ3Qiuj3i8/pics/bwZ3Qiuj3i81675975.jpg', 'highlights': ['Implementing KNN classifier and emphasizing the selection of k for defining nearest neighbors.', 'Using SVM classifier with linear model to fit and calculate accuracy.', 'Loading the iris dataset using scikit-learn and creating variables X and Y for data modeling.', 'Printing the shape of variable X and Y to understand the dimensions and structure of the dataset.', 'The process involves cleaning the data, splitting it into training and testing subsets, and implementing the KNN classifier with the nearest neighbor value of one.', 'Encountering a reshaping error when predicting new observations, which can be resolved by converting the data into a NumPy array.']}, {'end': 2227.226, 'segs': [{'end': 2079.467, 'src': 'embed', 'start': 2049.71, 'weight': 0, 'content': [{'end': 2053.331, 'text': 'So all your doubts regarding this question will be covered in my next session.', 'start': 2049.71, 'duration': 3.621}, {'end': 2056.413, 'text': "next, any other doubt, guys, you have regarding what I've explained till now.", 'start': 2053.331, 'duration': 3.082}, {'end': 2060.074, 'text': "All right, since you guys don't have any questions.", 'start': 2058.033, 'duration': 2.041}, {'end': 2061.833, 'text': 'Let me take one more example.', 'start': 2060.574, 'duration': 1.259}, {'end': 2065.344, 'text': 'So here we have taken an example of K nearest neighbors.', 'start': 2062.402, 'duration': 2.942}, {'end': 2070.205, 'text': 'So let us implement logistic regression on the same model or you can say on the same data set.', 'start': 2065.684, 'duration': 4.521}, {'end': 2074.185, 'text': 'So for that what I can do I can I have to first import my model.', 'start': 2070.645, 'duration': 3.54}, {'end': 2079.467, 'text': 'So I have to type in from sklearn.linear model import logistic regression.', 'start': 2074.205, 'duration': 5.262}], 'summary': 'Upcoming session will cover doubts on k nearest neighbors and logistic regression.', 'duration': 29.757, 'max_score': 2049.71, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/bwZ3Qiuj3i8/pics/bwZ3Qiuj3i82049710.jpg'}, {'end': 2181.744, 'src': 'embed', 'start': 2146.662, 'weight': 1, 'content': [{'end': 2147.743, 'text': 'Jenny is also saying no.', 'start': 2146.662, 'duration': 1.081}, {'end': 2150.855, 'text': "Okay, so I don't see any questions right now.", 'start': 2148.994, 'duration': 1.861}, {'end': 2157.756, 'text': 'No worry, guys, even if you have questions later, you can just come up with those in my next session or you can just contact a support team,', 'start': 2151.395, 'duration': 6.361}, {'end': 2159.177, 'text': 'which 24-7 available for you.', 'start': 2157.756, 'duration': 1.421}, {'end': 2162.678, 'text': "All right, so let me just recap the things that we have covered in today's training.", 'start': 2159.677, 'duration': 3.001}, {'end': 2166.659, 'text': 'So, first of all we understood what is machine learning and then we got into scikit-learn,', 'start': 2163.018, 'duration': 3.641}, {'end': 2169.68, 'text': 'which is a Python package for implementing machine learning.', 'start': 2166.659, 'duration': 3.021}, {'end': 2174.781, 'text': 'then we took a deep dive into it and we understood the installation and the various steps involved in machine learning.', 'start': 2169.68, 'duration': 5.101}, {'end': 2181.744, 'text': 'such as loading the data, analyzing it, splitting the data into train and test subset and finally finding out the accuracy of it.', 'start': 2175.222, 'duration': 6.522}], 'summary': 'Training covers machine learning using scikit-learn with 24-7 support available.', 'duration': 35.082, 'max_score': 2146.662, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/bwZ3Qiuj3i8/pics/bwZ3Qiuj3i82146662.jpg'}], 'start': 1948.632, 'title': 'Implementing machine learning in python', 'summary': 'Covers implementing machine learning in python using scikit-learn, focusing on k-nearest neighbors and logistic regression, and includes a recap of regression and classification techniques.', 'chapters': [{'end': 2227.226, 'start': 1948.632, 'title': 'Implementing machine learning in python', 'summary': 'Covers implementing machine learning in python using scikit-learn, with a focus on k-nearest neighbors and logistic regression, and ends with a recap of the topics covered including regression and classification techniques.', 'duration': 278.594, 'highlights': ['Focus on K-nearest neighbors and logistic regression The session focuses on implementing K-nearest neighbors and logistic regression using the iris dataset, showcasing the predict function and observing outputs for different values, providing practical demonstrations of machine learning concepts.', 'Recap of topics covered The session ends with a recap of the topics covered, including understanding machine learning, using scikit-learn, installation steps, data analysis, regression, classification techniques, and finding accuracy, offering a comprehensive overview of the learning material.', 'Future topics and support The instructor mentions covering clustering with larger datasets in the next session and assures coverage of doubts, while also encouraging questions and providing support from a 24/7 support team, demonstrating commitment to student learning and support.']}], 'duration': 278.594, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/bwZ3Qiuj3i8/pics/bwZ3Qiuj3i81948632.jpg', 'highlights': ['The session focuses on implementing K-nearest neighbors and logistic regression using the iris dataset, showcasing the predict function and observing outputs for different values, providing practical demonstrations of machine learning concepts.', 'The session ends with a recap of the topics covered, including understanding machine learning, using scikit-learn, installation steps, data analysis, regression, classification techniques, and finding accuracy, offering a comprehensive overview of the learning material.', 'The instructor mentions covering clustering with larger datasets in the next session and assures coverage of doubts, while also encouraging questions and providing support from a 24/7 support team, demonstrating commitment to student learning and support.']}], 'highlights': ['The tutorial includes implementations of SVM classifier, logistic regression, and care nearest neighbors.', 'Machine learning is a type of artificial intelligence that allows software applications to learn from the data without any human intervention, and this also helps you to predict the outcomes as well.', 'Machine learning adjusts itself to the reality and learns from its own meaning.', 'Machine learning involves creating models that detect patterns in a data set and can be adjusted to enhance accuracy for predicting actions based on new data.', 'Scikit-learn is an open source library for machine learning in Python, built on popular libraries such as numpy, scipy, and matplotlib, and licensed under BSD, encouraging academic as well as commercial use.', 'SVM is a supervised machine learning algorithm used for classification problems, aiming to define a hyperplane to split the data in the most optimal way, creating a wide margin between the hyperplane and the observations.', 'Implemented SVM classifier to classify iris flower data, achieving an accuracy of 96%.', 'Implementing KNN classifier and emphasizing the selection of k for defining nearest neighbors.', 'The session focuses on implementing K-nearest neighbors and logistic regression using the iris dataset, showcasing the predict function and observing outputs for different values, providing practical demonstrations of machine learning concepts.']}