title
Machine Learning with R | Machine Learning Algorithms | Data Science Training | Edureka

description
( Data Science Training: https://www.edureka.co/data-science-r-programming-certification-course ) This "Machine Learning with R" video by Edureka will help you to understand the core concepts of Machine Learning followed by a very interesting case study on Pokemon Dataset in R. This tutorial will comprise of these topics: 1. Understanding Machine Learning 2. Applications of Machine Learning 3. Types of Machine Learning Algorithms 4. Case Study on the "Pokemon Dataset" to implement Machine Learning Algorithms Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #LogisticRegression #Datasciencetutorial #Datasciencecourse #datascience How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 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. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies For more information, Please write back to us at sales@edureka.in 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 Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "

detail
{'title': 'Machine Learning with R | Machine Learning Algorithms | Data Science Training | Edureka', 'heatmap': [{'end': 321.806, 'start': 265.681, 'weight': 0.908}, {'end': 589.178, 'start': 341.102, 'weight': 0.81}, {'end': 1049.772, 'start': 1015.66, 'weight': 0.719}], 'summary': 'Covers an introduction to machine learning with r, building machine learning algorithms, analyzing data models with practical examples achieving 85% accuracy, and implementing linear regression on datasets. it also involves pokemon type selection and building linear regression models to determine the influence of defense, speed, and health points on attack.', 'chapters': [{'end': 62.045, 'segs': [{'end': 39.98, 'src': 'embed', 'start': 4.4, 'weight': 0, 'content': [{'end': 10.325, 'text': 'Hey guys, this is Bharani from Edureka and this session is all about machine learning with R.', 'start': 4.4, 'duration': 5.925}, {'end': 17.83, 'text': "We'll start off by understanding what exactly is machine learning and then we'll head on to look at some applications of machine learning,", 'start': 10.325, 'duration': 7.505}, {'end': 25.016, 'text': "following which we'll look at the different languages to implement machine learning algorithms and understand what makes R so popular.", 'start': 17.83, 'duration': 7.186}, {'end': 29.41, 'text': "Further going ahead, we'll understand the different types of machine learning algorithms.", 'start': 25.766, 'duration': 3.644}, {'end': 34.655, 'text': "And finally, we'll be working on a very interesting case study to implement all that we've learned.", 'start': 29.77, 'duration': 4.885}, {'end': 36.076, 'text': "So let's get started.", 'start': 35.175, 'duration': 0.901}, {'end': 39.98, 'text': "Let's understand the concept of machine learning with this example over here.", 'start': 36.437, 'duration': 3.543}], 'summary': 'Bharani from edureka covers machine learning with r, including applications, languages, algorithms, and a case study.', 'duration': 35.58, 'max_score': 4.4, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SeyghJ5cdm4/pics/SeyghJ5cdm44400.jpg'}], 'start': 4.4, 'title': 'Introduction to machine learning with r', 'summary': 'Provides an introduction to machine learning with r, covering its applications, popular languages for implementation, and various machine learning algorithms, followed by a practical case study demonstrating the concept of identifying fish.', 'chapters': [{'end': 62.045, 'start': 4.4, 'title': 'Machine learning with r', 'summary': 'Covers an introduction to machine learning with r, including its applications, popular languages for implementation, and different types of machine learning algorithms, culminating in a practical case study, illustrated with the concept of identifying fish as a case study example.', 'duration': 57.645, 'highlights': ['The session covers an introduction to machine learning with R, its applications, and popular languages for implementation.', 'The chapter delves into the different types of machine learning algorithms.', 'The practical case study involves implementing the learned concepts, using the example of identifying fish to illustrate the concept of machine learning.']}], 'duration': 57.645, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SeyghJ5cdm4/pics/SeyghJ5cdm44400.jpg', 'highlights': ['The practical case study involves implementing the learned concepts, using the example of identifying fish to illustrate the concept of machine learning.', 'The chapter delves into the different types of machine learning algorithms.', 'The session covers an introduction to machine learning with R, its applications, and popular languages for implementation.']}, {'end': 353.169, 'segs': [{'end': 219.175, 'src': 'embed', 'start': 112.066, 'weight': 0, 'content': [{'end': 118.073, 'text': "Now that we've understood what exactly is machine learning, let's go ahead and look at some applications of machine learning.", 'start': 112.066, 'duration': 6.007}, {'end': 126.742, 'text': 'The aviation industry uses machine learning for tasks such as finding optimal air routes, predicting flight delay and dynamic pricing,', 'start': 118.533, 'duration': 8.209}, {'end': 131.427, 'text': 'and all of these are quite serious optimization problems which can be solved by machine learning.', 'start': 126.742, 'duration': 4.685}, {'end': 134.449, 'text': 'Marketing organizations are in love with machine learning.', 'start': 131.907, 'duration': 2.542}, {'end': 137.051, 'text': 'According to Twitter,', 'start': 134.889, 'duration': 2.162}, {'end': 144.736, 'text': '75% of marketing enterprises use machine learning to enhance customer satisfaction and also to improve sales of new products and services.', 'start': 137.051, 'duration': 7.685}, {'end': 149.86, 'text': 'Healthcare industry uses machine learning for drug discovery and disease prediction.', 'start': 145.296, 'duration': 4.564}, {'end': 153.122, 'text': 'Robotic surgery is also an application of machine learning.', 'start': 150.3, 'duration': 2.822}, {'end': 158.784, 'text': 'The Da Vinci robot is a device which helps doctors to perform surgeries with fine detail.', 'start': 153.642, 'duration': 5.142}, {'end': 167.066, 'text': 'Machine learning has wide applications in financial industry as well, such as building econometric models and finding out fraudulent transactions.', 'start': 159.164, 'duration': 7.902}, {'end': 174.148, 'text': "Google's much-hyped self-driving car and the automated drones used in military are also applications of machine learning.", 'start': 167.666, 'duration': 6.482}, {'end': 176.549, 'text': 'So those were the applications of machine learning.', 'start': 174.648, 'duration': 1.901}, {'end': 182.791, 'text': "Now, let's move ahead and look at the different languages which can be used to implement machine learning algorithms.", 'start': 176.949, 'duration': 5.842}, {'end': 187.855, 'text': 'R and Python are the most widely used languages to implement machine learning,', 'start': 183.531, 'duration': 4.324}, {'end': 194.7, 'text': 'and that is because they provide wide variety of packages for the purpose of data science, machine learning and visualization.', 'start': 187.855, 'duration': 6.845}, {'end': 200.325, 'text': 'MATLAB is another language which is used for image recognition and machine learning.', 'start': 195.261, 'duration': 5.064}, {'end': 206.149, 'text': 'Our good old Java also provides machine learning libraries such as mallet and deep learning 4G.', 'start': 200.725, 'duration': 5.424}, {'end': 209.052, 'text': "Let's understand why R for machine learning.", 'start': 206.59, 'duration': 2.462}, {'end': 212.013, 'text': 'R is a Turing complete language that us.', 'start': 209.652, 'duration': 2.361}, {'end': 219.175, 'text': 'it can perform any computation which a Turing machine can, and thus we can perform tasks such as statistical analysis,', 'start': 212.013, 'duration': 7.162}], 'summary': 'Machine learning is widely used in aviation, marketing, healthcare, finance, and robotics, with 75% of marketing enterprises leveraging it for customer satisfaction and sales. r and python are the most popular languages for implementing machine learning algorithms.', 'duration': 107.109, 'max_score': 112.066, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SeyghJ5cdm4/pics/SeyghJ5cdm4112066.jpg'}, {'end': 265.221, 'src': 'embed', 'start': 239.962, 'weight': 5, 'content': [{'end': 245.386, 'text': "We'll start off by ingesting the data from various sources and once the ingestion is done,", 'start': 239.962, 'duration': 5.424}, {'end': 249.709, 'text': 'the data needs to be cleaned so that simple insights can be found from it.', 'start': 245.386, 'duration': 4.323}, {'end': 251.21, 'text': 'after cleaning and understanding,', 'start': 249.709, 'duration': 1.501}, {'end': 258.935, 'text': 'the structure of the data will divide the entire data set into train and test sets and build the algorithm on top of the train set.', 'start': 251.21, 'duration': 7.725}, {'end': 265.221, 'text': "Once the algorithm learns all the features of the train set, we'll check for the model's accuracy with the test set.", 'start': 259.475, 'duration': 5.746}], 'summary': 'Ingest, clean, and split data for algorithm training and testing.', 'duration': 25.259, 'max_score': 239.962, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SeyghJ5cdm4/pics/SeyghJ5cdm4239962.jpg'}, {'end': 331.936, 'src': 'heatmap', 'start': 265.681, 'weight': 6, 'content': [{'end': 272.408, 'text': "And finally, we'll evaluate the result by using various parameters such as confusion matrix and root mean square error.", 'start': 265.681, 'duration': 6.727}, {'end': 277.553, 'text': "We'll be implementing both the confusion matrix and root mean square error in a case study later on.", 'start': 272.728, 'duration': 4.825}, {'end': 281.316, 'text': 'So those were the steps involved in building a machine learning algorithm.', 'start': 278.194, 'duration': 3.122}, {'end': 286.801, 'text': "Now we'll have a look at the different types of machine learning algorithms broadly speaking.", 'start': 281.777, 'duration': 5.024}, {'end': 291.144, 'text': 'We have supervised unsupervised and reinforcement machine learning algorithms.', 'start': 286.921, 'duration': 4.223}, {'end': 292.805, 'text': "So let's start with the first one.", 'start': 291.564, 'duration': 1.241}, {'end': 296.028, 'text': "Let's understand what exactly is supervised learning.", 'start': 293.246, 'duration': 2.782}, {'end': 301.531, 'text': 'A supervised learning algorithm learns from a known data set with labels, that is,', 'start': 296.488, 'duration': 5.043}, {'end': 309.536, 'text': 'it needs a training data set to learn all the features and once it learns these features, it is given the test set to check for its accuracy.', 'start': 301.531, 'duration': 8.005}, {'end': 311.157, 'text': "Let's take this example.", 'start': 310.097, 'duration': 1.06}, {'end': 313.679, 'text': 'There is a student who is about to appear for a test.', 'start': 311.417, 'duration': 2.262}, {'end': 319.044, 'text': 'And before she appears for the test, she needs to train herself well so that she can perform well in the test.', 'start': 314.119, 'duration': 4.925}, {'end': 321.806, 'text': 'And this is the concept behind supervised learning.', 'start': 319.444, 'duration': 2.362}, {'end': 325.95, 'text': 'Some examples of supervised learning are classification and regression.', 'start': 322.247, 'duration': 3.703}, {'end': 327.592, 'text': "So let's start with classification.", 'start': 326.25, 'duration': 1.342}, {'end': 331.936, 'text': 'Classification determines to which sort of categories does a new observation belong.', 'start': 328.092, 'duration': 3.844}], 'summary': 'Overview of building a machine learning algorithm, including steps and types of algorithms: supervised, unsupervised, and reinforcement learning.', 'duration': 35.448, 'max_score': 265.681, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SeyghJ5cdm4/pics/SeyghJ5cdm4265681.jpg'}], 'start': 62.045, 'title': 'Machine learning', 'summary': 'Explains the concept of machine learning, its applications in various industries, the significance of r and python in its implementation, and outlines the process of building a machine learning algorithm, including data ingestion, cleaning, model training, testing, and evaluation, introducing the concept of supervised learning with examples of classification and regression.', 'chapters': [{'end': 239.522, 'start': 62.045, 'title': 'Understanding machine learning', 'summary': 'Explains the concept of machine learning, its applications in various industries, and the languages commonly used for its implementation, highlighting its impact on areas such as aviation, marketing, healthcare, and finance, as well as the significance of r and python in implementing machine learning algorithms.', 'duration': 177.477, 'highlights': ['The aviation industry uses machine learning for tasks such as finding optimal air routes, predicting flight delay and dynamic pricing, and 75% of marketing enterprises use machine learning to enhance customer satisfaction and improve sales of new products and services, according to Twitter.', 'Healthcare industry uses machine learning for drug discovery and disease prediction, and robotic surgery is also an application of machine learning.', 'The financial industry utilizes machine learning for building econometric models and detecting fraudulent transactions, and R and Python are the most widely used languages for implementing machine learning due to their wide variety of packages for data science, machine learning, and visualization.', 'R is a Turing complete language that can perform statistical analysis, predictive modeling, and implement machine learning algorithms, and it provides both object-oriented and functional programming paradigms, and the best thing about R is it is a free open-source software with no licensing restrictions.']}, {'end': 353.169, 'start': 239.962, 'title': 'Building machine learning algorithm', 'summary': 'Outlines the process of building a machine learning algorithm, including data ingestion, cleaning, model training, testing, and evaluation, and introduces the concept of supervised learning with examples of classification and regression.', 'duration': 113.207, 'highlights': ['The chapter outlines the process of building a machine learning algorithm, including data ingestion, cleaning, model training, testing, and evaluation, and introduces the concept of supervised learning with examples of classification and regression.', 'Supervised learning algorithm learns from a known data set with labels and requires a training data set to learn all the features, followed by testing for accuracy using a test set.', 'Examples of supervised learning include classification, which determines the category to which a new observation belongs, and regression.', 'The concept of supervised learning is illustrated with the analogy of a student preparing for a test to perform well, mirroring the process of the algorithm learning from a training data set before testing its accuracy with a test set.']}], 'duration': 291.124, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SeyghJ5cdm4/pics/SeyghJ5cdm462045.jpg', 'highlights': ['75% of marketing enterprises use machine learning to enhance customer satisfaction and improve sales of new products and services, according to Twitter.', 'The aviation industry uses machine learning for tasks such as finding optimal air routes, predicting flight delay and dynamic pricing.', 'The financial industry utilizes machine learning for building econometric models and detecting fraudulent transactions.', 'Healthcare industry uses machine learning for drug discovery and disease prediction, and robotic surgery is also an application of machine learning.', 'R and Python are the most widely used languages for implementing machine learning due to their wide variety of packages for data science, machine learning, and visualization.', 'The chapter outlines the process of building a machine learning algorithm, including data ingestion, cleaning, model training, testing, and evaluation.', 'Supervised learning algorithm learns from a known data set with labels and requires a training data set to learn all the features, followed by testing for accuracy using a test set.', 'Examples of supervised learning include classification, which determines the category to which a new observation belongs, and regression.', 'R is a Turing complete language that can perform statistical analysis, predictive modeling, and implement machine learning algorithms, and it provides both object-oriented and functional programming paradigms.']}, {'end': 1073.371, 'segs': [{'end': 625.549, 'src': 'embed', 'start': 598.625, 'weight': 0, 'content': [{'end': 603.048, 'text': 'and to find out, the accuracy will divide the left diagonal with all of the observations.', 'start': 598.625, 'duration': 4.423}, {'end': 616.523, 'text': "So that will be 80 plus 39 divided by 80 plus 39 plus 11 plus 10 and that's the accuracy of this model is 85%.", 'start': 603.408, 'duration': 13.115}, {'end': 625.549, 'text': "So what we've done is we build the classification model on top of the car purchase data set and we found out the accuracy of the model is 85%.", 'start': 616.523, 'duration': 9.026}], 'summary': 'Accuracy of the classification model is 85%.', 'duration': 26.924, 'max_score': 598.625, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SeyghJ5cdm4/pics/SeyghJ5cdm4598625.jpg'}, {'end': 927.456, 'src': 'embed', 'start': 902.95, 'weight': 1, 'content': [{'end': 910.852, 'text': "So we've implemented the linear regression model and we found out that the root mean square error of this built model is 1020.", 'start': 902.95, 'duration': 7.902}, {'end': 914.453, 'text': "Now, let's head back and understand what exactly is unsupervised learning.", 'start': 910.852, 'duration': 3.601}, {'end': 920.173, 'text': 'An unsupervised learning algorithm draws inferences from data which does not have labels.', 'start': 915.33, 'duration': 4.843}, {'end': 922.534, 'text': "So let's take this example over here.", 'start': 920.613, 'duration': 1.921}, {'end': 927.456, 'text': 'There are students who do not require any external training and they can learn by themselves.', 'start': 922.794, 'duration': 4.662}], 'summary': "Linear regression model's rmse is 1020. exploring unsupervised learning.", 'duration': 24.506, 'max_score': 902.95, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SeyghJ5cdm4/pics/SeyghJ5cdm4902950.jpg'}, {'end': 971.668, 'src': 'embed', 'start': 942.426, 'weight': 2, 'content': [{'end': 946.931, 'text': 'There is high intra cluster similarity and low inter cluster similarity.', 'start': 942.426, 'duration': 4.505}, {'end': 952.257, 'text': "Now we'll go ahead and implement the k-means clustering algorithm on top of the iris data set.", 'start': 947.372, 'duration': 4.885}, {'end': 954.34, 'text': "Let's have a glance at the iris data set.", 'start': 952.718, 'duration': 1.622}, {'end': 955.915, 'text': 'View of iris.', 'start': 955.074, 'duration': 0.841}, {'end': 965.103, 'text': 'So this is our iris data set which comprises of these five columns now before we build the key means clustering algorithm on top of this.', 'start': 956.635, 'duration': 8.468}, {'end': 971.668, 'text': 'We would have to remove the fifth column because the key means can be applied only on top of the numerical values.', 'start': 965.183, 'duration': 6.485}], 'summary': 'High intra-cluster similarity, low inter-cluster similarity; implementing k-means clustering on iris dataset.', 'duration': 29.242, 'max_score': 942.426, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SeyghJ5cdm4/pics/SeyghJ5cdm4942426.jpg'}, {'end': 1049.772, 'src': 'heatmap', 'start': 1015.66, 'weight': 0.719, 'content': [{'end': 1024.365, 'text': 'It is divided into so we are taking this data set and dividing this entire data set into three clusters will store the result in iris cluster.', 'start': 1015.66, 'duration': 8.705}, {'end': 1026.506, 'text': "Let's have a look at this iris cluster object.", 'start': 1024.865, 'duration': 1.641}, {'end': 1034.111, 'text': 'So see that out of these 150 observations all of these have been divided into three clusters.', 'start': 1027.906, 'duration': 6.205}, {'end': 1041.827, 'text': "So now we'll bind the clustering vector with the original data set to have a better analysis.", 'start': 1035.183, 'duration': 6.644}, {'end': 1045.049, 'text': "Let's have a glance at this clustered data object.", 'start': 1042.928, 'duration': 2.121}, {'end': 1049.772, 'text': 'So we have binded the clustering vector object with the original data set.', 'start': 1046.47, 'duration': 3.302}], 'summary': 'Data set divided into 3 clusters, 150 observations grouped, clustering vector binded with original data set.', 'duration': 34.112, 'max_score': 1015.66, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SeyghJ5cdm4/pics/SeyghJ5cdm41015660.jpg'}], 'start': 353.169, 'title': 'Building and analyzing data models', 'summary': 'Discusses building a classification model on car purchase data using r, achieving 85% accuracy, and implementing linear regression on a diamonds dataset with a root mean square error of 1020. it also covers the application of k-means clustering on an iris dataset, successfully dividing observations into three clusters.', 'chapters': [{'end': 625.549, 'start': 353.169, 'title': 'Building classification model on car purchase data', 'summary': 'Discusses building a classification model on the car purchase dataset using the r programming language, including loading the dataset, data manipulation, model building, prediction, and evaluating the model accuracy, achieving an 85% accuracy.', 'duration': 272.38, 'highlights': ['The model accuracy is 85% on the car purchase dataset.', 'Predicting the values on the test set using the built model.', 'Splitting the dataset into training and testing sets with a 65-35 ratio.', 'Implementing the classification algorithm on the car purchase dataset.', 'Loading the car purchase dataset and removing unnecessary columns.']}, {'end': 1073.371, 'start': 625.549, 'title': 'Regression and clustering in data analysis', 'summary': 'Explains the implementation of linear regression model on a diamonds dataset, achieving a root mean square error of 1020, and then demonstrates the application of k-means clustering algorithm on an iris dataset, successfully dividing the observations into three clusters based on their attributes.', 'duration': 447.822, 'highlights': ['The linear regression model achieved a root mean square error of 1020.', 'The k-means clustering algorithm divided the iris dataset into three clusters.']}], 'duration': 720.202, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SeyghJ5cdm4/pics/SeyghJ5cdm4353169.jpg', 'highlights': ['Achieved 85% model accuracy on car purchase data', 'Implemented linear regression with 1020 RMSE', 'Applied k-means clustering on iris dataset']}, {'end': 1395.406, 'segs': [{'end': 1118.217, 'src': 'embed', 'start': 1096.326, 'weight': 0, 'content': [{'end': 1105.811, 'text': 'Reinforcement learning is a type of machine learning algorithm where the machine or agent in an environment learns ideal behavior in order to maximize its performance.', 'start': 1096.326, 'duration': 9.485}, {'end': 1109.953, 'text': 'And simple reward feedback is required for the agent to learn its behavior.', 'start': 1106.231, 'duration': 3.722}, {'end': 1112.574, 'text': 'And this is known as reinforcement signal.', 'start': 1110.433, 'duration': 2.141}, {'end': 1114.435, 'text': "Let's take Pac-Man for example.", 'start': 1112.954, 'duration': 1.481}, {'end': 1118.217, 'text': 'As long as Pac-Man keeps eating food, it earns points.', 'start': 1114.995, 'duration': 3.222}], 'summary': 'Reinforcement learning maximizes performance through reward feedback.', 'duration': 21.891, 'max_score': 1096.326, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SeyghJ5cdm4/pics/SeyghJ5cdm41096326.jpg'}, {'end': 1395.406, 'src': 'embed', 'start': 1351.958, 'weight': 1, 'content': [{'end': 1362.185, 'text': 'So we see that this is a data frame which consists of 721 observations of 20 variables or in other words, there are 721 Pokemons in total.', 'start': 1351.958, 'duration': 10.227}, {'end': 1370.031, 'text': 'Now there are some variables which are of logical type, such as is legendary is a column which is of class logical.', 'start': 1362.886, 'duration': 7.145}, {'end': 1373.113, 'text': 'similarly has gender, is a column which is of logical.', 'start': 1370.031, 'duration': 3.082}, {'end': 1377.016, 'text': 'and again has Miguel evolution is a column which is of logical type.', 'start': 1373.113, 'duration': 3.903}, {'end': 1381.979, 'text': "Now we'll go ahead and change the class of these three columns to factor,", 'start': 1377.456, 'duration': 4.523}, {'end': 1386.301, 'text': 'because when we have to build the machine learning algorithms on top of these,', 'start': 1381.979, 'duration': 4.322}, {'end': 1391.084, 'text': 'you would require the class of this to be a factor and not logical type.', 'start': 1386.301, 'duration': 4.783}, {'end': 1395.406, 'text': 'and we can change the class of this to be factor using the as dot factor function,', 'start': 1391.084, 'duration': 4.322}], 'summary': 'Data frame with 721 pokemons, 20 variables. changing logical type to factor for machine learning.', 'duration': 43.448, 'max_score': 1351.958, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SeyghJ5cdm4/pics/SeyghJ5cdm41351958.jpg'}], 'start': 1073.731, 'title': 'Machine learning and data set transformation', 'summary': 'Discusses implementing k-means algorithm on the iris-k dataset, reinforcement learning, and working on a pokemon dataset for a case study. it also covers transformation of a pokemon data set, including 721 pokemons and the need to change logical columns to factor for machine learning algorithms.', 'chapters': [{'end': 1264.655, 'start': 1073.731, 'title': 'K-means clustering and reinforcement learning in machine learning', 'summary': 'Discusses implementing the k-means algorithm on the iris-k dataset, understanding reinforcement learning in machine learning, and working on a pokemon dataset for a case study, which includes selecting grass, fire, and water-type pokemons for the johto league.', 'duration': 190.924, 'highlights': ['The chapter discusses implementing the k-means algorithm on the iris-k dataset, where the setosa species are grouped into the third cluster and the rest of the observations into cluster one and two.', 'Reinforcement learning is explained as a type of machine learning algorithm where the machine learns ideal behavior to maximize its performance, with examples including Google self-driving car, manufacturing robots, and autonomous flying helicopters.', 'The chapter introduces a case study using a Pokemon dataset, where the task involves selecting one grass, fire, and water-type Pokemon for the Johto League.']}, {'end': 1395.406, 'start': 1265.416, 'title': 'Data set transformation and structure analysis', 'summary': 'Covers the transformation of a pokemon data set by removing the first column, renaming columns, and understanding the structure, including 721 pokemons and the need to change logical columns to factor for machine learning algorithms.', 'duration': 129.99, 'highlights': ['The data set consists of 721 observations of 20 variables, indicating the total number of Pokemons in the dataset and the dimensionality of the data.', "Logical columns like 'is legendary', 'has gender', and 'has Miguel evolution' are identified and their need for conversion to factor type is highlighted for machine learning purposes.", "The process of removing the first column and renaming columns, such as 'primary type', 'secondary type', 'health points', 'special attack', and 'special defense', is described as part of data set transformation."]}], 'duration': 321.675, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SeyghJ5cdm4/pics/SeyghJ5cdm41073731.jpg', 'highlights': ['Reinforcement learning maximizes machine performance, e.g., Google self-driving car.', 'Pokemon dataset has 721 observations and 20 variables.', "Logical columns like 'is legendary' need conversion to factor type for ML."]}, {'end': 1858.6, 'segs': [{'end': 1425.792, 'src': 'embed', 'start': 1395.406, 'weight': 4, 'content': [{'end': 1401.71, 'text': 'will say as dot factor of Pokemon dollar is legendary and will change the class of this column to factor.', 'start': 1395.406, 'duration': 6.304}, {'end': 1407.035, 'text': "Similarly, we'll change the class of hasMegaEvolution to a factor.", 'start': 1402.671, 'duration': 4.364}, {'end': 1411.379, 'text': "Again, we'll change the class of hasGenderColumn to a factor.", 'start': 1407.535, 'duration': 3.844}, {'end': 1415.823, 'text': "Now let's go ahead and look at the different primary types of the Pokemon.", 'start': 1412.56, 'duration': 3.263}, {'end': 1418.265, 'text': "We'll be using the table function to do that.", 'start': 1416.463, 'duration': 1.802}, {'end': 1425.792, 'text': 'So there are in total 18 Pokemon types which could be Bug, Dark, Dragon, Electric and so on.', 'start': 1419.306, 'duration': 6.486}], 'summary': 'Changing data classes to factor and analyzing 18 pokemon types.', 'duration': 30.386, 'max_score': 1395.406, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SeyghJ5cdm4/pics/SeyghJ5cdm41395406.jpg'}, {'end': 1492.903, 'src': 'embed', 'start': 1468.194, 'weight': 3, 'content': [{'end': 1473.782, 'text': 'and we see that there are 66 grass Pokemons in total, and out of the 66 grass Pokemons,', 'start': 1468.194, 'duration': 5.588}, {'end': 1477.868, 'text': 'we need to select all of those grass Pokemons whose secondary type is poison.', 'start': 1473.782, 'duration': 4.086}, {'end': 1483.316, 'text': "So let's go ahead and select all of those grass Pokemons which are also poisonous.", 'start': 1478.349, 'duration': 4.967}, {'end': 1492.903, 'text': "So from this grass Pokemon data set I am filtering out those Pokemons which are also poisonous and I'll store the result in grass poison Pokemon.", 'start': 1483.977, 'duration': 8.926}], 'summary': 'Out of 66 grass pokemons, filtering those with poison as secondary type.', 'duration': 24.709, 'max_score': 1468.194, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SeyghJ5cdm4/pics/SeyghJ5cdm41468194.jpg'}, {'end': 1575.701, 'src': 'embed', 'start': 1540.981, 'weight': 0, 'content': [{'end': 1543.481, 'text': "and I'm storing it in my grass Pokemon.", 'start': 1540.981, 'duration': 2.5}, {'end': 1546.942, 'text': 'object view of my grass Pokemon.', 'start': 1543.481, 'duration': 3.461}, {'end': 1553.483, 'text': 'So this tells me that Roserade is my final grass Pokemon which has the speed value of 90.', 'start': 1547.502, 'duration': 5.981}, {'end': 1555.483, 'text': "So I've successfully selected my grass Pokemon.", 'start': 1553.483, 'duration': 2}, {'end': 1558.184, 'text': "Now it's time to select the water Pokemon.", 'start': 1555.863, 'duration': 2.321}, {'end': 1559.424, 'text': "So we'll do the same thing.", 'start': 1558.564, 'duration': 0.86}, {'end': 1568.176, 'text': 'From the Pokemon data set, I am filtering out all those Pokemons whose primary type is water and storing it into the water Pokemon object.', 'start': 1560.111, 'duration': 8.065}, {'end': 1575.701, 'text': 'View of water Pokemon and I see that there are 105 water Pokemons in total.', 'start': 1569.017, 'duration': 6.684}], 'summary': 'Selected roserade as final grass pokemon with speed value 90. chose from 105 water pokemons.', 'duration': 34.72, 'max_score': 1540.981, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SeyghJ5cdm4/pics/SeyghJ5cdm41540981.jpg'}, {'end': 1654.995, 'src': 'embed', 'start': 1628.561, 'weight': 2, 'content': [{'end': 1633.483, 'text': 'Now, let me go ahead and select this one Pokemon which has the maximum defense value.', 'start': 1628.561, 'duration': 4.922}, {'end': 1641.307, 'text': 'So, from the water psychic Pokemon data set, I am filtering out that one particular Pokemon which has a defense value of 110,', 'start': 1633.943, 'duration': 7.364}, {'end': 1644.588, 'text': "and I'm storing it in my water Pokemon object.", 'start': 1641.307, 'duration': 3.281}, {'end': 1653.414, 'text': 'View of my water Pokemon and I see that Slowbro is my final water Pokemon with a defense value of 110.', 'start': 1645.149, 'duration': 8.265}, {'end': 1654.995, 'text': "So I've selected my grass Pokemon.", 'start': 1653.414, 'duration': 1.581}], 'summary': 'Selected slowbro as the water pokemon with a defense value of 110.', 'duration': 26.434, 'max_score': 1628.561, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SeyghJ5cdm4/pics/SeyghJ5cdm41628561.jpg'}, {'end': 1788.522, 'src': 'embed', 'start': 1739.9, 'weight': 1, 'content': [{'end': 1760.254, 'text': "So, from the firefighting Pokemon data set I am filtering out that one particular Pokemon which has the attack value of 123 and I'll store it in my fire Pokemon object view of my fire Pokemon and Emboar will be my final fire Pokemon with the attack value of 123..", 'start': 1739.9, 'duration': 20.354}, {'end': 1766.316, 'text': "So I've finally selected the grass, water and the fire Pokemons.", 'start': 1760.254, 'duration': 6.062}, {'end': 1773.878, 'text': 'Now let me bind all of these three Pokemons into a single data set by using the rbind function.', 'start': 1767.036, 'duration': 6.842}, {'end': 1781.72, 'text': "So I'll use the rbind function and give these three parameters, which are my fire Pokemon, my grass Pokemon and my water Pokemon,", 'start': 1774.218, 'duration': 7.502}, {'end': 1783.48, 'text': 'and store the result in my Pokemons.', 'start': 1781.72, 'duration': 1.76}, {'end': 1788.522, 'text': 'So these are my three Pokemons for the fight in the Johto League.', 'start': 1784.938, 'duration': 3.584}], 'summary': 'Filtered out emboar as the final fire pokemon with an attack value of 123 and selected grass, water, and fire pokemons for the johto league.', 'duration': 48.622, 'max_score': 1739.9, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SeyghJ5cdm4/pics/SeyghJ5cdm41739900.jpg'}, {'end': 1834.244, 'src': 'embed', 'start': 1808.723, 'weight': 6, 'content': [{'end': 1818.231, 'text': "So let me implement the linear regression algorithm on top of the Pokemon data set and understand what are the factors influencing the Pokemon's attack.", 'start': 1808.723, 'duration': 9.508}, {'end': 1823.295, 'text': 'So to do that, I would have to split the entire data set into training and testing sets.', 'start': 1819.051, 'duration': 4.244}, {'end': 1834.244, 'text': "So I'll use this sample.split function to divide the entire data set into train and test sets and this time the attack column from the Pokemon data set.", 'start': 1824.301, 'duration': 9.943}], 'summary': 'Implement linear regression on pokemon data to analyze factors influencing attack.', 'duration': 25.521, 'max_score': 1808.723, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SeyghJ5cdm4/pics/SeyghJ5cdm41808723.jpg'}], 'start': 1395.406, 'title': 'Pokemon type selection', 'summary': "Involves selecting grass and water pokemons based on type and speed, resulting in the selection of roserade with a speed value of 90. it also discusses selecting water, psychic, and fire types based on attributes like defense and attack values, and implementing linear regression to understand factors influencing a pokemon's attack.", 'chapters': [{'end': 1559.424, 'start': 1395.406, 'title': 'Selecting grass and water pokemon', 'summary': 'Involves changing the class of certain columns, filtering and selecting grass and water pokemons based on type and speed, resulting in the selection of roserade as the final grass pokemon with a speed value of 90.', 'duration': 164.018, 'highlights': ['Using the table function, there are 18 total Pokemon types including Bug, Dark, Dragon, Electric, etc.', 'Filtering out grass Pokemons results in a total of 66 grass Pokemons.', 'Out of 66 grass Pokemons, 14 have a secondary type of poison.', 'One particular Pokemon has the maximum speed value of 90, resulting in the selection of Roserade as the final grass Pokemon.']}, {'end': 1858.6, 'start': 1560.111, 'title': 'Pokemon types selection', 'summary': "Discusses the process of selecting specific types of pokemons, such as water, psychic, and fire, based on their attributes like defense and attack values, ultimately assembling a team for the johto league, and then delving into the implementation of linear regression to understand the factors influencing a pokemon's attack.", 'duration': 298.489, 'highlights': ['Selected Emboar as the fire Pokemon with the maximum attack value of 123.', 'Filtered out Slowbro as the water psychic Pokemon with the maximum defense value of 110.', 'Assembled a team of grass, water, and fire Pokemons for the Johto League.', 'Implemented linear regression on the Pokemon dataset to understand factors influencing attack.']}], 'duration': 463.194, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SeyghJ5cdm4/pics/SeyghJ5cdm41395406.jpg', 'highlights': ['Roserade selected as final grass Pokemon with max speed of 90', 'Emboar chosen as fire Pokemon with max attack of 123', 'Slowbro filtered as water psychic Pokemon with max defense of 110', '66 grass Pokemons identified after filtering', '18 total Pokemon types revealed using table function', '14 grass Pokemons have secondary type of poison', 'Linear regression implemented to understand factors influencing attack', 'Team of grass, water, and fire Pokemons assembled for Johto League']}, {'end': 2435.158, 'segs': [{'end': 2006.466, 'src': 'embed', 'start': 1979.138, 'weight': 1, 'content': [{'end': 1982.519, 'text': 'Now, let me go ahead and find out the aggregate error in prediction.', 'start': 1979.138, 'duration': 3.381}, {'end': 1987.28, 'text': 'I can do that by implementing the root mean square error for this model.', 'start': 1982.999, 'duration': 4.281}, {'end': 1991.761, 'text': "So I'll take the square of this, then I'll take the mean of this,", 'start': 1987.66, 'duration': 4.101}, {'end': 2001.563, 'text': "then I'll implement the square root for this and this finally gives me the root mean square error for the model which I've just built and the root mean square error value for this is 24..", 'start': 1991.761, 'duration': 9.802}, {'end': 2006.466, 'text': 'Now let me go ahead and build another linear regression model on top of this data set,', 'start': 2001.563, 'duration': 4.903}], 'summary': 'The root mean square error for the model is 24.', 'duration': 27.328, 'max_score': 1979.138, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SeyghJ5cdm4/pics/SeyghJ5cdm41979138.jpg'}, {'end': 2151.628, 'src': 'embed', 'start': 2106.382, 'weight': 0, 'content': [{'end': 2111.023, 'text': "I'll take the mean then I'll finally implement the square root and store it in RMSE 2.", 'start': 2106.382, 'duration': 4.641}, {'end': 2114.404, 'text': 'So this tells me that the RMSE value for this is 21.', 'start': 2111.023, 'duration': 3.381}, {'end': 2120.379, 'text': 'So let me compare RMSE 1 and RMSE 2 RMSE 1, RMSE 2.', 'start': 2114.404, 'duration': 5.975}, {'end': 2126.229, 'text': 'So now I see that the RMSE2 value is less than the RMSE1 value, or in other words,', 'start': 2120.379, 'duration': 5.85}, {'end': 2133.142, 'text': 'the second model is much better than the first linear regression model, and thus we are done with the second task.', 'start': 2126.229, 'duration': 6.913}, {'end': 2135.755, 'text': "So we're done with the second task now.", 'start': 2133.874, 'duration': 1.881}, {'end': 2143.782, 'text': "It's time to head on to the third task and we have to save the entire world by selecting all the legendary Pokemons, or, in other words,", 'start': 2135.796, 'duration': 7.986}, {'end': 2151.628, 'text': 'we would have to implement the classification algorithm on top of the Pokemon data set to find out if the Pokemon is legendary or not.', 'start': 2143.782, 'duration': 7.846}], 'summary': 'Rmse 2 value is 21, indicating second model is better. moving on to classifying legendary pokemons.', 'duration': 45.246, 'max_score': 2106.382, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SeyghJ5cdm4/pics/SeyghJ5cdm42106382.jpg'}, {'end': 2376.789, 'src': 'embed', 'start': 2351.286, 'weight': 3, 'content': [{'end': 2357.414, 'text': 'So this builds up the confusion matrix and I see that the accuracy of the second model is 94%.', 'start': 2351.286, 'duration': 6.128}, {'end': 2365.824, 'text': 'So when I compare the first model with the second model, I see that the first model is much better than the second model.', 'start': 2357.414, 'duration': 8.41}, {'end': 2370.708, 'text': 'So now I have successfully completed all of the three tasks.', 'start': 2366.627, 'duration': 4.081}, {'end': 2376.789, 'text': 'So our first task was to select a fire, water and grass Pokemon to fight in the Johto League.', 'start': 2371.128, 'duration': 5.661}], 'summary': 'First model outperforms second model with 94% accuracy. completed all three tasks.', 'duration': 25.503, 'max_score': 2351.286, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SeyghJ5cdm4/pics/SeyghJ5cdm42351286.jpg'}], 'start': 1859.081, 'title': 'Building linear regression models', 'summary': 'Discusses building two linear regression models to determine the influence of defense on attack and the combined influence of defense, speed, and health points on attack. the second model was found more effective based on comparison of root mean square error values.', 'chapters': [{'end': 2133.142, 'start': 1859.081, 'title': 'Building linear regression models', 'summary': 'Discusses building two linear regression models to determine the influence of defense on attack and the combined influence of defense, speed, and health points on attack, resulting in a comparison of the root mean square error values of the models, with the second model being found more effective.', 'duration': 274.061, 'highlights': ['The root mean square error value for the first linear regression model is 24.', 'The root mean square error value for the second linear regression model is 21.', 'The second linear regression model is found to be much better than the first model.']}, {'end': 2435.158, 'start': 2133.874, 'title': 'Implementing classification algorithm on pokemon dataset', 'summary': 'Discusses implementing a classification algorithm on the pokemon dataset to determine if a pokemon is legendary or not, achieving an accuracy of 98% with the first model and 94% with the second model, and successfully completing the three tasks related to the pokemon dataset.', 'duration': 301.284, 'highlights': ['The accuracy of the first classification model is 98%.', 'The accuracy of the second classification model is 94%.', 'Successfully completed all three tasks related to the Pokemon dataset.']}], 'duration': 576.077, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SeyghJ5cdm4/pics/SeyghJ5cdm41859081.jpg', 'highlights': ['The root mean square error value for the second linear regression model is 21.', 'The root mean square error value for the first linear regression model is 24.', 'The second linear regression model is found to be much better than the first model.', 'The accuracy of the first classification model is 98%.', 'The accuracy of the second classification model is 94%.', 'Successfully completed all three tasks related to the Pokemon dataset.']}], 'highlights': ['75% of marketing enterprises use machine learning to enhance customer satisfaction and improve sales of new products and services, according to Twitter.', 'Achieved 85% model accuracy on car purchase data', 'R and Python are the most widely used languages for implementing machine learning due to their wide variety of packages for data science, machine learning, and visualization.', 'The practical case study involves implementing the learned concepts, using the example of identifying fish to illustrate the concept of machine learning.', 'The chapter delves into the different types of machine learning algorithms.', 'The session covers an introduction to machine learning with R, its applications, and popular languages for implementation.', 'Reinforcement learning maximizes machine performance, e.g., Google self-driving car.', 'Implemented linear regression with 1020 RMSE', 'Applied k-means clustering on iris dataset', 'The chapter outlines the process of building a machine learning algorithm, including data ingestion, cleaning, model training, testing, and evaluation.']}