title
Machine Learning Algorithms | Machine Learning Tutorial | Data Science Training | Edureka

description
πŸ”₯ Data Science Training (Use Code "π˜πŽπ”π“π”ππ„πŸπŸŽ") - https://www.edureka.co/data-science-r-programming-certification-course This Machine Learning Algorithms Tutorial shall teach you what machine learning is, and the various ways in which you can use machine learning to solve a problem! Towards the end, you will learn how to prepare a data-set for model creation and validation and how you can create a model using any machine learning algorithm! In this Machine Learning Algorithms Tutorial video you will understand: 1) What is an Algorithm? 2) What is Machine Learning? 3) How is a problem solved using Machine Learning? 4) Types of Machine Learning 5) Machine Learning Algorithms 6) Demo You can also check out this Machine Learning with Python video for more insights: https://youtu.be/nJKxWbQ1jaw Check our complete Data Science playlist here: https://goo.gl/60NJJS Subscribe to our channel to get video updates. Hit the subscribe button above. #Edureka #MachineLearningAlgorithms #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 β€˜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.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 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 Algorithms | Machine Learning Tutorial | Data Science Training | Edureka', 'heatmap': [{'end': 1909.57, 'start': 1871.06, 'weight': 1}], 'summary': 'Tutorial on machine learning and algorithms provides an overview of different machine learning approaches, including supervised, unsupervised, and reinforcement learning, with practical demonstrations and examples. it also covers the application of machine learning algorithms in temperature control systems, anomaly detection, and logistic regression model for car engine prediction, showcasing the logic and syntax of programming languages in solving computational problems.', 'chapters': [{'end': 206.318, 'segs': [{'end': 31.174, 'src': 'embed', 'start': 0.129, 'weight': 0, 'content': [{'end': 1.87, 'text': 'Hey guys, this is Hemant from Edureka.', 'start': 0.129, 'duration': 1.741}, {'end': 5.472, 'text': "Today's session is going to be on machine learning algorithms.", 'start': 2.07, 'duration': 3.402}, {'end': 11.235, 'text': "So without any further ado, let's move on to the agenda to understand what all we covered in today's session.", 'start': 6.112, 'duration': 5.123}, {'end': 13.556, 'text': "So we'll be following a top-down approach.", 'start': 11.795, 'duration': 1.761}, {'end': 19.399, 'text': "We'll start from the basics and understand what is an algorithm and how it can be used in machine learning.", 'start': 13.576, 'duration': 5.823}, {'end': 25.982, 'text': "After that, we'll understand what is machine learning exactly and how a problem can be solved using machine learning.", 'start': 19.839, 'duration': 6.143}, {'end': 31.174, 'text': "After that we'll be discussing various techniques using which a machine can learn.", 'start': 26.952, 'duration': 4.222}], 'summary': "Today's session covers machine learning algorithms using a top-down approach.", 'duration': 31.045, 'max_score': 0.129, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Up6KLx3m2ww/pics/Up6KLx3m2ww129.jpg'}, {'end': 64.404, 'src': 'embed', 'start': 36.557, 'weight': 1, 'content': [{'end': 43.761, 'text': "Towards the end we'll be doing a demonstration wherein we'll see how we can prepare a dataset for the creation and validation of a model.", 'start': 36.557, 'duration': 7.204}, {'end': 48.763, 'text': "And after that we'll be creating a model using one of the algorithms that we'll be learning today.", 'start': 44.341, 'duration': 4.422}, {'end': 51.745, 'text': 'Alright, so guys this is our agenda for today.', 'start': 48.783, 'duration': 2.962}, {'end': 55.781, 'text': "Are we clear with it? Okay, I'm getting confirmation, so Karan is clear.", 'start': 51.865, 'duration': 3.916}, {'end': 58.802, 'text': 'So is Suraj Matthew?', 'start': 56.481, 'duration': 2.321}, {'end': 64.404, 'text': "Alright, guys, since most of you are clear, let's move on to the first topic of today's discussion.", 'start': 58.822, 'duration': 5.582}], 'summary': 'Demonstration on dataset preparation and model creation using algorithms, followed by discussion on the first topic.', 'duration': 27.847, 'max_score': 36.557, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Up6KLx3m2ww/pics/Up6KLx3m2ww36557.jpg'}, {'end': 157.056, 'src': 'embed', 'start': 107.475, 'weight': 2, 'content': [{'end': 111.543, 'text': 'Having said that, what is this logic? This logic is what an algorithm is.', 'start': 107.475, 'duration': 4.068}, {'end': 120.409, 'text': 'So, in simple words, an algorithm is a step by step procedure towards solving a problem in the computer world.', 'start': 112.983, 'duration': 7.426}, {'end': 125.773, 'text': "So, let's take an example to understand this thing which we have just discussed.", 'start': 120.79, 'duration': 4.983}, {'end': 127.855, 'text': "So, let's take an example.", 'start': 126.174, 'duration': 1.681}, {'end': 134.02, 'text': 'So, this is a problem or this is an algorithm to print numbers from 1 to 20.', 'start': 127.935, 'duration': 6.085}, {'end': 137.443, 'text': "So, let's go step by step and understand what this algorithm is doing.", 'start': 134.02, 'duration': 3.423}, {'end': 139.724, 'text': 'So this is the start position.', 'start': 138.223, 'duration': 1.501}, {'end': 147.509, 'text': 'We start over here and then we see that our algorithm is initializing a variable x to 0.', 'start': 139.764, 'duration': 7.745}, {'end': 152.413, 'text': 'So we initialized a variable x to 0 and then we incremented it by 1.', 'start': 147.509, 'duration': 4.904}, {'end': 157.056, 'text': "After that we are printing that variable and we are checking it whether it's less than 20.", 'start': 152.413, 'duration': 4.643}], 'summary': 'An algorithm is a step-by-step procedure in the computer world, such as printing numbers from 1 to 20.', 'duration': 49.581, 'max_score': 107.475, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Up6KLx3m2ww/pics/Up6KLx3m2ww107475.jpg'}], 'start': 0.129, 'title': 'Machine learning and algorithms', 'summary': 'Provides an overview of machine learning algorithms, covering basics and techniques for machine learning, along with a demonstration on dataset preparation and model creation. it also explains the concept of an algorithm as a step-by-step procedure towards solving a problem in the computer world, using the example of printing numbers from 1 to 20 and providing insights into the logic and syntax of programming languages.', 'chapters': [{'end': 58.802, 'start': 0.129, 'title': 'Machine learning algorithms overview', 'summary': 'Covers an overview of machine learning algorithms, including a top-down approach starting with basics, techniques for machine learning, and a demonstration on dataset preparation and model creation.', 'duration': 58.673, 'highlights': ['The chapter follows a top-down approach, starting from the basics and understanding the use of algorithms in machine learning.', 'It discusses various techniques for machine learning and basic algorithms used in machine learning.', 'A demonstration is conducted to prepare a dataset for model creation and validation using the learned algorithms.']}, {'end': 206.318, 'start': 58.822, 'title': 'Understanding algorithms and logic', 'summary': 'Explains the concept of an algorithm as a step-by-step procedure towards solving a problem in the computer world, using the example of printing numbers from 1 to 20 while providing insights into the logic and syntax of programming languages.', 'duration': 147.496, 'highlights': ['An algorithm is a step by step procedure towards solving a problem in the computer world. The concept of an algorithm is defined as a step-by-step procedure for solving a problem in the computer world.', 'A program is basically logic wrapped around a syntax specific to a programming language, with the logic remaining the same in every language. A program is described as logic wrapped around a syntax specific to a programming language, with the logic staying consistent across different languages.', 'The example of printing numbers from 1 to 20 demonstrates the step-by-step execution of an algorithm, incrementing a variable and checking its value until reaching the specified limit. The illustration of printing numbers from 1 to 20 exemplifies the sequential execution of an algorithm, involving variable incrementation and value checking until the limit is reached.']}], 'duration': 206.189, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Up6KLx3m2ww/pics/Up6KLx3m2ww129.jpg', 'highlights': ['The chapter follows a top-down approach, starting from the basics and understanding the use of algorithms in machine learning.', 'A demonstration is conducted to prepare a dataset for model creation and validation using the learned algorithms.', 'An algorithm is a step by step procedure towards solving a problem in the computer world.', 'The example of printing numbers from 1 to 20 demonstrates the step-by-step execution of an algorithm, incrementing a variable and checking its value until reaching the specified limit.']}, {'end': 838.66, 'segs': [{'end': 313.009, 'src': 'embed', 'start': 278.182, 'weight': 1, 'content': [{'end': 282.603, 'text': 'It changes its own code according to the new scenarios it discovers.', 'start': 278.182, 'duration': 4.421}, {'end': 285.566, 'text': 'Alright, so this is what machine learning is.', 'start': 283.263, 'duration': 2.303}, {'end': 289.912, 'text': 'It itself learns whatever has to be learned from it.', 'start': 285.827, 'duration': 4.085}, {'end': 291.915, 'text': 'We provide it scenarios.', 'start': 289.952, 'duration': 1.963}, {'end': 293.938, 'text': 'we provide it with past experiences.', 'start': 291.915, 'duration': 2.023}, {'end': 299.205, 'text': 'we feed the values and learning from those past experiences it comes up with new solutions.', 'start': 293.938, 'duration': 5.267}, {'end': 303.107, 'text': 'Now, this topic is actually very interesting to learn about,', 'start': 299.906, 'duration': 3.201}, {'end': 313.009, 'text': 'because you might be thinking how a machine can actually redo its code or how can it update its code on its own, right?', 'start': 303.107, 'duration': 9.902}], 'summary': 'Machine learning adapts code to new scenarios, learns from past experiences, and generates new solutions independently.', 'duration': 34.827, 'max_score': 278.182, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Up6KLx3m2ww/pics/Up6KLx3m2ww278182.jpg'}, {'end': 382.522, 'src': 'embed', 'start': 345.576, 'weight': 0, 'content': [{'end': 352.162, 'text': 'So we have learned what machine learning is, but, like I said, there are different approaches towards solving a problem, right?', 'start': 345.576, 'duration': 6.586}, {'end': 355.124, 'text': 'So there are basically different ways a machine can learn.', 'start': 352.462, 'duration': 2.662}, {'end': 357.166, 'text': "Let's see the different ways a machine can learn.", 'start': 355.264, 'duration': 1.902}, {'end': 359.287, 'text': 'Basically there are three kinds of ways.', 'start': 357.846, 'duration': 1.441}, {'end': 367.554, 'text': 'So the first way is supervised learning, the second way is reinforcement learning, and then you have the unsupervised learning right?', 'start': 359.608, 'duration': 7.946}, {'end': 372.177, 'text': "So let's discuss each of these in detail and understand what these actually are.", 'start': 367.894, 'duration': 4.283}, {'end': 375.899, 'text': 'So the first kind of learning is called supervised learning.', 'start': 372.978, 'duration': 2.921}, {'end': 378.22, 'text': 'So what is supervised learning?', 'start': 377, 'duration': 1.22}, {'end': 380.321, 'text': 'So supervised.', 'start': 379.42, 'duration': 0.901}, {'end': 382.522, 'text': 'if you concentrate on the word supervised,', 'start': 380.321, 'duration': 2.201}], 'summary': 'Transcript covers different approaches to machine learning: supervised, reinforcement, and unsupervised learning.', 'duration': 36.946, 'max_score': 345.576, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Up6KLx3m2ww/pics/Up6KLx3m2ww345576.jpg'}, {'end': 545.256, 'src': 'embed', 'start': 513.592, 'weight': 3, 'content': [{'end': 516.397, 'text': 'In supervised learning, we give the machines example.', 'start': 513.592, 'duration': 2.805}, {'end': 523.888, 'text': 'So example, that day it rained, and the scene was like this, the temperature was this, the humidity was this, and hence it rained.', 'start': 516.818, 'duration': 7.07}, {'end': 528.73, 'text': 'Right, so if the inputs are like these, you come up with a decision, okay, it will rain.', 'start': 524.369, 'duration': 4.361}, {'end': 531.091, 'text': 'Right, so this is what supervised learning is.', 'start': 529.431, 'duration': 1.66}, {'end': 533.952, 'text': 'Our next topic is unsupervised learning.', 'start': 531.111, 'duration': 2.841}, {'end': 536.913, 'text': 'So, basically, if you understood what is supervised learning, so okay.', 'start': 533.992, 'duration': 2.921}, {'end': 540.254, 'text': 'so first, guys, any doubt in what supervised learning is?', 'start': 536.913, 'duration': 3.341}, {'end': 545.256, 'text': "Okay, Suraj says it's clear.", 'start': 540.274, 'duration': 4.982}], 'summary': 'Supervised learning uses examples to predict outcomes. unsupervised learning is the next topic discussed.', 'duration': 31.664, 'max_score': 513.592, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Up6KLx3m2ww/pics/Up6KLx3m2ww513592.jpg'}, {'end': 615.717, 'src': 'embed', 'start': 584.224, 'weight': 4, 'content': [{'end': 586.105, 'text': 'So this is what the computer actually does.', 'start': 584.224, 'duration': 1.881}, {'end': 590.227, 'text': 'So in unsupervised learning, what it does is, you provide it inputs.', 'start': 586.565, 'duration': 3.662}, {'end': 597.81, 'text': 'So for example, I want my computer to, I give my computer some inputs on fruit.', 'start': 591.107, 'duration': 6.703}, {'end': 605.693, 'text': "So I don't tell the computer what the fruit is actually, but I give other parameters, such as how big it is or what color it has,", 'start': 597.93, 'duration': 7.763}, {'end': 607.554, 'text': 'say what is the taste of that fruit.', 'start': 605.693, 'duration': 1.861}, {'end': 615.717, 'text': 'So when I give all these conditions or all these parameters to my computer, So it groups the fruits based on that.', 'start': 608.674, 'duration': 7.043}], 'summary': 'In unsupervised learning, the computer groups fruits based on provided parameters.', 'duration': 31.493, 'max_score': 584.224, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Up6KLx3m2ww/pics/Up6KLx3m2ww584224.jpg'}, {'end': 802.089, 'src': 'embed', 'start': 773.183, 'weight': 5, 'content': [{'end': 778.284, 'text': 'But you can actually reward your dog if he does right and you can punish him if he does wrong.', 'start': 773.183, 'duration': 5.101}, {'end': 782.325, 'text': 'So that same thing is actually applied in reinforcement learning as well.', 'start': 778.545, 'duration': 3.78}, {'end': 789.147, 'text': "So basically the computer's aim is to maximize rewards when it does actions.", 'start': 782.965, 'duration': 6.182}, {'end': 793.483, 'text': "So it'll come up with a solution which has the maximum rewards in place.", 'start': 790.14, 'duration': 3.343}, {'end': 796.585, 'text': "So we define, if it does a certain action, you'll get a reward.", 'start': 793.503, 'duration': 3.082}, {'end': 802.089, 'text': 'And then from its past experiences, it understands, okay, when I did this, I got a reward.', 'start': 797.085, 'duration': 5.004}], 'summary': 'Reinforcement learning aims to maximize rewards by learning from past experiences.', 'duration': 28.906, 'max_score': 773.183, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Up6KLx3m2ww/pics/Up6KLx3m2ww773183.jpg'}], 'start': 206.398, 'title': 'Machine learning and its approaches', 'summary': 'Introduces machine learning, emphasizing the importance of algorithms and the different approaches, including supervised, unsupervised, and reinforcement learning. it explains supervised learning with input and answers, unsupervised learning finding patterns, and reinforcement learning maximizing rewards.', 'chapters': [{'end': 399.748, 'start': 206.398, 'title': 'Introduction to machine learning', 'summary': 'Introduces the concept of machine learning, highlighting the importance of algorithms, the ability of machines to learn and adapt, and the different approaches to machine learning, including supervised, reinforcement, and unsupervised learning.', 'duration': 193.35, 'highlights': ["The chapter introduces the concept of machine learning, highlighting the importance of algorithms, the ability of machines to learn and adapt, and the different approaches to machine learning, including supervised, reinforcement, and unsupervised learning. It emphasizes the significance of algorithms for representing complex procedures and the machine's capability to learn and adapt, with a focus on supervised, reinforcement, and unsupervised learning.", 'The machine learning process involves providing scenarios and past experiences to the machine, which then uses the information to come up with new solutions. Machines learn from provided scenarios and past experiences to develop new solutions, demonstrating the practical application of machine learning in problem-solving.', 'The chapter explains the three main ways a machine can learn: supervised learning, reinforcement learning, and unsupervised learning, with a focus on supervised learning and its comparison to a classroom setting. It details the three main ways of machine learning - supervised, reinforcement, and unsupervised learning, with a specific emphasis on supervised learning and its analogy to a classroom setting.']}, {'end': 838.66, 'start': 400.308, 'title': 'Supervised, unsupervised, and reinforcement learning', 'summary': 'Explains supervised learning where machines are given inputs and corresponding answers, unsupervised learning where machines find patterns in data without given answers, and reinforcement learning where computers take actions to maximize rewards based on past experiences.', 'duration': 438.352, 'highlights': ['In supervised learning, machines are given inputs and corresponding answers, such as predicting rain based on parameters like humidity and temperature, and are able to make decisions based on past experiences to come up with a solution. In supervised learning, machines are trained with inputs and corresponding answers, for instance, predicting rain based on parameters like humidity and temperature, and are able to make decisions based on past experiences to come up with a solution.', 'In unsupervised learning, machines are given inputs without corresponding answers, and they find patterns in the data, such as grouping fruits based on parameters like size, taste, and color, and identifying patterns in unstructured data like big data. In unsupervised learning, machines are given inputs without corresponding answers, and they find patterns in the data, such as grouping fruits based on parameters like size, taste, and color, and identifying patterns in unstructured data like big data.', 'Reinforcement learning involves machines taking actions to maximize rewards based on past experiences, similar to teaching a dog through rewarding good behavior and punishing bad behavior, and the computer aims to maximize rewards through actions and past experiences. Reinforcement learning involves machines taking actions to maximize rewards based on past experiences, similar to teaching a dog through rewarding good behavior and punishing bad behavior, and the computer aims to maximize rewards through actions and past experiences.']}], 'duration': 632.262, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Up6KLx3m2ww/pics/Up6KLx3m2ww206398.jpg', 'highlights': ['The chapter introduces the concept of machine learning, emphasizing the importance of algorithms and the different approaches, including supervised, reinforcement, and unsupervised learning.', 'Machines learn from provided scenarios and past experiences to develop new solutions, demonstrating the practical application of machine learning in problem-solving.', 'The chapter explains the three main ways a machine can learn: supervised learning, reinforcement learning, and unsupervised learning, with a focus on supervised learning and its comparison to a classroom setting.', 'In supervised learning, machines are given inputs and corresponding answers, such as predicting rain based on parameters like humidity and temperature, and are able to make decisions based on past experiences to come up with a solution.', 'In unsupervised learning, machines are given inputs without corresponding answers, and they find patterns in the data, such as grouping fruits based on parameters like size, taste, and color, and identifying patterns in unstructured data like big data.', 'Reinforcement learning involves machines taking actions to maximize rewards based on past experiences, similar to teaching a dog through rewarding good behavior and punishing bad behavior, and the computer aims to maximize rewards through actions and past experiences.']}, {'end': 1224.547, 'segs': [{'end': 919.566, 'src': 'embed', 'start': 896.597, 'weight': 0, 'content': [{'end': 906.321, 'text': 'So, it will see what is basically the user or how a user responds to a certain temperature and then it will come up with a decision.', 'start': 896.597, 'duration': 9.724}, {'end': 914.104, 'text': 'So, if my temperature is 32 right now, maybe I need to lower it down, maybe I need to lower it down to 30.', 'start': 906.861, 'duration': 7.243}, {'end': 915.105, 'text': 'So this is just an example.', 'start': 914.104, 'duration': 1.001}, {'end': 919.566, 'text': 'there are a lot of parameters that are taking place, how many people are there in the room and everything.', 'start': 915.105, 'duration': 4.461}], 'summary': 'System assesses user response to temperature, making decisions based on parameters like number of people in the room.', 'duration': 22.969, 'max_score': 896.597, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Up6KLx3m2ww/pics/Up6KLx3m2ww896597.jpg'}, {'end': 1103.115, 'src': 'embed', 'start': 1077.254, 'weight': 1, 'content': [{'end': 1084.141, 'text': 'So when a decision has to be made, then algorithms for reinforcement learning are used.', 'start': 1077.254, 'duration': 6.887}, {'end': 1089.908, 'text': 'So, guys, any doubts in any of these five categories that we have just discussed?', 'start': 1084.781, 'duration': 5.127}, {'end': 1096.756, 'text': "So, basically, what I'm trying to tell you is that each question, so any kind of problem that you come up with,", 'start': 1090.809, 'duration': 5.947}, {'end': 1099.7, 'text': 'can be categorized under these five categories.', 'start': 1096.756, 'duration': 2.944}, {'end': 1103.115, 'text': 'It cannot be beyond these five categories.', 'start': 1100.612, 'duration': 2.503}], 'summary': 'Algorithms for reinforcement learning used for decision making in five categories.', 'duration': 25.861, 'max_score': 1077.254, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Up6KLx3m2ww/pics/Up6KLx3m2ww1077254.jpg'}, {'end': 1159.818, 'src': 'embed', 'start': 1128.071, 'weight': 2, 'content': [{'end': 1131.554, 'text': 'so when we have these kind of problems, we come up.', 'start': 1128.071, 'duration': 3.483}, {'end': 1134.677, 'text': 'we can solve this using these algorithms, right?', 'start': 1131.554, 'duration': 3.123}, {'end': 1137.499, 'text': "So let's learn about these algorithms now.", 'start': 1135.297, 'duration': 2.202}, {'end': 1141.863, 'text': 'that is, classification, anomaly, detection, regression, clustering and reinforcement.', 'start': 1137.499, 'duration': 4.364}, {'end': 1143.464, 'text': "So let's shed a light on that.", 'start': 1142.183, 'duration': 1.281}, {'end': 1147.227, 'text': "So let's start with algorithms, machine learning algorithms.", 'start': 1144.285, 'duration': 2.942}, {'end': 1150.97, 'text': 'So the first algorithm is the classification algorithm.', 'start': 1147.267, 'duration': 3.703}, {'end': 1159.818, 'text': 'so, like I said, when you have a set number of outputs, so basically for questions like this, so is it cold outside today?', 'start': 1150.97, 'duration': 8.848}], 'summary': 'Learn about machine learning algorithms such as classification, anomaly detection, regression, clustering, and reinforcement.', 'duration': 31.747, 'max_score': 1128.071, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Up6KLx3m2ww/pics/Up6KLx3m2ww1128071.jpg'}], 'start': 838.66, 'title': 'Reinforcement learning and machine learning problem solving', 'summary': 'Delves into reinforcement learning in temperature control systems, illustrating its application in decision-making and providing an example of reducing the temperature from 32 to 30. it also explores machine learning problem-solving, categorizing problems into five types and emphasizing the use of various machine learning algorithms such as classification, anomaly detection, and regression.', 'chapters': [{'end': 919.566, 'start': 838.66, 'title': 'Reinforcement learning in temperature control', 'summary': 'Discusses reinforcement learning in the context of temperature control systems, highlighting its application in making decisions based on past experiences and user responses, with an example of lowering the temperature from 32 to 30.', 'duration': 80.906, 'highlights': ['Reinforcement learning is used in a temperature control system to decide whether to increase or decrease the temperature based on past experiences and user responses.', 'An example is given where the system may decide to lower the temperature from 32 to 30 based on various parameters such as the number of people in the room.']}, {'end': 1224.547, 'start': 919.566, 'title': 'Machine learning problem solving', 'summary': 'Explains the concept of reinforcement learning, categorizes problems into five types, and discusses the use of different machine learning algorithms based on the type of problem, with classification, anomaly detection, and regression being the most emphasized.', 'duration': 304.981, 'highlights': ['The chapter explains the concept of reinforcement learning, categorizes problems into five types, and discusses the use of different machine learning algorithms based on the type of problem. Reinforcement learning is inspired by the decision-making process in a maze, and problems are categorized into five types, leading to the use of different machine learning algorithms for each type.', 'Classification, anomaly detection, and regression algorithms are emphasized for solving different types of problems. Different types of problems are associated with specific algorithms: classification for decision-making with set outputs, anomaly detection for pattern analysis, and regression for numeric value prediction.', 'Classification algorithms are used for problems with set outputs, categorized as two class classification or multi-class classification based on the number of choices. Problems with fixed outputs are solved using classification algorithms, which are further classified as two class or multi-class based on the number of choices.']}], 'duration': 385.887, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Up6KLx3m2ww/pics/Up6KLx3m2ww838660.jpg', 'highlights': ['Reinforcement learning in temperature control system decides to lower temperature from 32 to 30 based on various parameters.', 'Reinforcement learning is inspired by the decision-making process in a maze, and problems are categorized into five types, leading to the use of different machine learning algorithms for each type.', 'Classification, anomaly detection, and regression algorithms are emphasized for solving different types of problems.']}, {'end': 1495.235, 'segs': [{'end': 1323.666, 'src': 'embed', 'start': 1270.497, 'weight': 0, 'content': [{'end': 1278.144, 'text': 'Now, what is the use case for anomaly detection algorithms? It could be, for example, in credit card companies.', 'start': 1270.497, 'duration': 7.647}, {'end': 1283.589, 'text': 'So, in credit card companies, each transaction of yours is monitored right?', 'start': 1278.644, 'duration': 4.945}, {'end': 1292.32, 'text': "And whenever there's a transaction which is not usual, which doesn't match your daily transaction pattern, you get alerted for it.", 'start': 1283.669, 'duration': 8.651}, {'end': 1297.203, 'text': 'So they might confirm with you whether you only made this transaction.', 'start': 1292.42, 'duration': 4.783}, {'end': 1302.587, 'text': 'So when you have these kind of problems, you use anomaly detection algorithms to solve them.', 'start': 1297.743, 'duration': 4.844}, {'end': 1305.789, 'text': 'The third algorithm is regression algorithm.', 'start': 1303.567, 'duration': 2.222}, {'end': 1311.663, 'text': 'So like I said, whenever you have to come up with a value So you use regression algorithms.', 'start': 1305.809, 'duration': 5.854}, {'end': 1320.225, 'text': 'So for example, what will be the temperature for tomorrow? So whatever value will come out of this will be a number.', 'start': 1312.224, 'duration': 8.001}, {'end': 1323.666, 'text': "So let's say I came up with 28 degrees Celsius.", 'start': 1320.805, 'duration': 2.861}], 'summary': 'Anomaly detection algorithms are used in credit card companies to monitor transactions, while regression algorithms are used to predict values like temperature.', 'duration': 53.169, 'max_score': 1270.497, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Up6KLx3m2ww/pics/Up6KLx3m2ww1270497.jpg'}, {'end': 1403.541, 'src': 'embed', 'start': 1373.788, 'weight': 3, 'content': [{'end': 1375.968, 'text': 'so we discussed unsupervised learning, remember?', 'start': 1373.788, 'duration': 2.18}, {'end': 1382.47, 'text': 'So in unsupervised learning we have clustering algorithms wherein we try to establish a structure right?', 'start': 1376.028, 'duration': 6.442}, {'end': 1385.991, 'text': 'So we have some unstructured data that you want to make sense of.', 'start': 1382.49, 'duration': 3.501}, {'end': 1389.572, 'text': "So what we'll do is we'll pass it through a clustering algorithm.", 'start': 1386.391, 'duration': 3.181}, {'end': 1396.276, 'text': 'And if there is a pattern which a computer can see, it comes up with that pattern and shows us like this.', 'start': 1390.232, 'duration': 6.044}, {'end': 1403.541, 'text': 'So for example, I feed data to my computer, right? And my data then applies clustering algorithm onto that.', 'start': 1396.796, 'duration': 6.745}], 'summary': 'Unsupervised learning involves using clustering algorithms to structure unstructured data, helping computers identify patterns.', 'duration': 29.753, 'max_score': 1373.788, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Up6KLx3m2ww/pics/Up6KLx3m2ww1373788.jpg'}, {'end': 1487.261, 'src': 'embed', 'start': 1461.229, 'weight': 4, 'content': [{'end': 1471.396, 'text': 'and so whenever you have to make a decision and your decision is based on the past experiences of your machine or whatever inputs that you have given to your machine,', 'start': 1461.229, 'duration': 10.167}, {'end': 1473.018, 'text': 'you use reinforcement learning.', 'start': 1471.396, 'duration': 1.622}, {'end': 1480.223, 'text': 'Now, for example, whenever you wanted to train your computer how to play chess, it will use reinforcement learning.', 'start': 1473.058, 'duration': 7.165}, {'end': 1487.261, 'text': 'when it has learned or when you have created a model for that, and your game is actually being played by the computer.', 'start': 1480.97, 'duration': 6.291}], 'summary': 'Reinforcement learning is used to train computers for decision-making based on past experiences, such as teaching them to play chess.', 'duration': 26.032, 'max_score': 1461.229, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Up6KLx3m2ww/pics/Up6KLx3m2ww1461229.jpg'}], 'start': 1225.188, 'title': 'Machine learning algorithms', 'summary': 'Covers anomaly detection algorithms, which analyze patterns and alert when anomalies occur, and provides details on types of machine learning algorithms including anomaly detection, regression, clustering, and reinforcement algorithms, with examples and applications.', 'chapters': [{'end': 1297.203, 'start': 1225.188, 'title': 'Anomaly detection algorithms', 'summary': 'Discusses anomaly detection algorithms, which analyze patterns and alert when anomalies occur, such as in credit card transactions where unusual activity triggers alerts for confirmation.', 'duration': 72.015, 'highlights': ['Anomaly detection algorithms analyze patterns and alert when anomalies occur, such as in credit card transactions.', 'In credit card companies, each transaction is monitored, and unusual transactions trigger alerts for confirmation.']}, {'end': 1495.235, 'start': 1297.743, 'title': 'Types of machine learning algorithms', 'summary': 'Covers the types of machine learning algorithms including anomaly detection, regression, clustering, and reinforcement algorithms, with examples and applications provided for each.', 'duration': 197.492, 'highlights': ['The chapter covers the types of machine learning algorithms including anomaly detection, regression, clustering, and reinforcement algorithms, with examples and applications provided for each.', 'Regression algorithms are used to predict numerical values, such as temperature forecasts or determining discounts for customers, aiding in decision-making and optimizing business strategies.', 'Clustering algorithms are utilized in unsupervised learning to establish patterns and structures within unstructured data, allowing for categorization and decision-making based on the identified groups.', 'Reinforcement algorithms are employed to make decisions based on past experiences, demonstrated in examples such as training computers to play chess and guiding decision-making during gameplay.']}], 'duration': 270.047, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Up6KLx3m2ww/pics/Up6KLx3m2ww1225188.jpg', 'highlights': ['Anomaly detection algorithms analyze patterns and alert when anomalies occur, such as in credit card transactions.', 'In credit card companies, each transaction is monitored, and unusual transactions trigger alerts for confirmation.', 'Regression algorithms are used to predict numerical values, such as temperature forecasts or determining discounts for customers, aiding in decision-making and optimizing business strategies.', 'Clustering algorithms are utilized in unsupervised learning to establish patterns and structures within unstructured data, allowing for categorization and decision-making based on the identified groups.', 'Reinforcement algorithms are employed to make decisions based on past experiences, demonstrated in examples such as training computers to play chess and guiding decision-making during gameplay.']}, {'end': 2013.066, 'segs': [{'end': 1552.759, 'src': 'embed', 'start': 1520.402, 'weight': 3, 'content': [{'end': 1523.985, 'text': 'We have covered the basics of the algorithms which are used in machine learning.', 'start': 1520.402, 'duration': 3.583}, {'end': 1531.212, 'text': 'So now, if I give you a problem, you should be able to identify which algorithm will fit into this problem.', 'start': 1524.465, 'duration': 6.747}, {'end': 1534.013, 'text': 'right?. what each algorithm is all about.', 'start': 1531.212, 'duration': 2.801}, {'end': 1535.393, 'text': 'how many algorithms are there?', 'start': 1534.013, 'duration': 1.38}, {'end': 1541.075, 'text': "we'll discuss that in our later class, but for now, for today, you should understand.", 'start': 1535.393, 'duration': 5.682}, {'end': 1543.236, 'text': 'if I had this problem, okay.', 'start': 1541.075, 'duration': 2.161}, {'end': 1546.177, 'text': 'so if I had this kind of problem, I should apply this algorithm to it.', 'start': 1543.236, 'duration': 2.941}, {'end': 1552.759, 'text': "How will I apply this algorithm? We'll be discussing that in the later class, but this is the idea that you should get today.", 'start': 1546.617, 'duration': 6.142}], 'summary': 'Covered basics of machine learning algorithms, identifying fitting algorithm, discussing types and applications.', 'duration': 32.357, 'max_score': 1520.402, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Up6KLx3m2ww/pics/Up6KLx3m2ww1520402.jpg'}, {'end': 1617.145, 'src': 'embed', 'start': 1584.463, 'weight': 0, 'content': [{'end': 1587.045, 'text': "I'll use classification algorithm for that.", 'start': 1584.463, 'duration': 2.582}, {'end': 1591.529, 'text': 'So this is the basic understanding that you should get from this session today.', 'start': 1587.465, 'duration': 4.064}, {'end': 1597.634, 'text': 'So enough of theory guys, so we have now understood the concept behind machine learning.', 'start': 1592.65, 'duration': 4.984}, {'end': 1605.12, 'text': "Now let's see, first of all you guys won't be knowing how the inputs are actually given to a system to create a model.", 'start': 1598.174, 'duration': 6.946}, {'end': 1607.883, 'text': 'So these inputs are actually called data sets.', 'start': 1605.541, 'duration': 2.342}, {'end': 1617.145, 'text': "So now what we're doing is we'll be seeing how we can prepare a dataset to actually create a model and then also verifying that model,", 'start': 1608.863, 'duration': 8.282}], 'summary': 'Using classification algorithm for creating model from datasets.', 'duration': 32.682, 'max_score': 1584.463, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Up6KLx3m2ww/pics/Up6KLx3m2ww1584463.jpg'}, {'end': 1834.047, 'src': 'embed', 'start': 1802.381, 'weight': 2, 'content': [{'end': 1808.504, 'text': "We'll be taking WT, that is the weight of the car, and we'll be taking in DISP, that is the displacement of the car.", 'start': 1802.381, 'duration': 6.123}, {'end': 1813.427, 'text': "And then we'll be predicting whether this car will have a V engine or a straight engine.", 'start': 1809.025, 'duration': 4.402}, {'end': 1817.849, 'text': "So we'll be creating a model in the later part of today's demonstration.", 'start': 1813.447, 'duration': 4.402}, {'end': 1824.753, 'text': 'But for now, we have to divide this data set between the training data set and the testing data set.', 'start': 1817.889, 'duration': 6.864}, {'end': 1826.014, 'text': "So let's see how will we do that.", 'start': 1824.793, 'duration': 1.221}, {'end': 1834.047, 'text': 'So first of all we have to import a library called catools.', 'start': 1826.863, 'duration': 7.184}], 'summary': 'Using car weight (wt) and displacement (disp) to predict engine type, splitting data into training and testing sets.', 'duration': 31.666, 'max_score': 1802.381, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Up6KLx3m2ww/pics/Up6KLx3m2ww1802381.jpg'}, {'end': 1909.57, 'src': 'heatmap', 'start': 1871.06, 'weight': 1, 'content': [{'end': 1878.824, 'text': "So, basically, what I'm specifying here is that I want to split my data set empty cars into 70-30 ratio, alright?", 'start': 1871.06, 'duration': 7.764}, {'end': 1881.386, 'text': "So let's run this command.", 'start': 1879.805, 'duration': 1.581}, {'end': 1883.643, 'text': 'Alright, this command is run.', 'start': 1882.421, 'duration': 1.222}, {'end': 1889.432, 'text': 'Alright, so now I will run this particular variable.', 'start': 1884.324, 'duration': 5.108}, {'end': 1891.555, 'text': 'I will execute this particular task.', 'start': 1889.652, 'duration': 1.903}, {'end': 1894.659, 'text': 'So it has now been executed.', 'start': 1892.617, 'duration': 2.042}, {'end': 1904.947, 'text': 'So what basically has now happened is it has picked out random values and for each value it has assigned either a true or a false.', 'start': 1894.719, 'duration': 10.228}, {'end': 1909.57, 'text': 'So 70% of the dataset is true and 30% of the dataset is false.', 'start': 1905.147, 'duration': 4.423}], 'summary': 'Data set split into 70-30 ratio, 70% true, 30% false', 'duration': 38.51, 'max_score': 1871.06, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Up6KLx3m2ww/pics/Up6KLx3m2ww1871060.jpg'}, {'end': 1990.001, 'src': 'embed', 'start': 1959.811, 'weight': 1, 'content': [{'end': 1965.455, 'text': "So I'll write false over here and then I will execute the statement.", 'start': 1959.811, 'duration': 5.644}, {'end': 1971.44, 'text': "So let's check whether we have splitted our dataset or not.", 'start': 1967.036, 'duration': 4.404}, {'end': 1973.781, 'text': "So let's check the testing dataset.", 'start': 1971.48, 'duration': 2.301}, {'end': 1979.245, 'text': 'So you can actually see it from here that testing has around 12 observations.', 'start': 1975.202, 'duration': 4.043}, {'end': 1983.357, 'text': 'And training has around 20 observations now.', 'start': 1981.036, 'duration': 2.321}, {'end': 1985.279, 'text': 'So the data set has been splitted.', 'start': 1983.497, 'duration': 1.782}, {'end': 1987.96, 'text': 'Our empty cars had 32 observations.', 'start': 1985.959, 'duration': 2.001}, {'end': 1990.001, 'text': 'We have split it in 70-30 ratio.', 'start': 1988, 'duration': 2.001}], 'summary': 'Dataset split into 70-30 ratio: 20 training, 12 testing observations.', 'duration': 30.19, 'max_score': 1959.811, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Up6KLx3m2ww/pics/Up6KLx3m2ww1959811.jpg'}], 'start': 1495.235, 'title': 'Machine learning basics and car engine prediction model', 'summary': 'Covers machine learning basics, including reinforcement learning, dataset preparation, and creating a predictive model. it also discusses building a car engine prediction model based on weight and displacement, using 70% training data and 30% testing data.', 'chapters': [{'end': 1761.797, 'start': 1495.235, 'title': 'Machine learning basics and dataset preparation', 'summary': 'Covers the basics of machine learning algorithms, including the concept of reinforcement learning and the process of dividing a dataset into training and testing parts, aiming to create and verify a predictive model. it also emphasizes the importance of identifying which algorithm fits a specific problem.', 'duration': 266.562, 'highlights': ['The chapter covers the basics of machine learning algorithms, including the concept of reinforcement learning and the process of dividing a dataset into training and testing parts. It explains the use of reinforcement learning in temperature control systems and the importance of dividing a dataset for creating and verifying a predictive model.', 'Emphasizes the importance of identifying which algorithm fits a specific problem. It stresses the need for understanding and selecting the appropriate algorithm for a given problem in machine learning.', 'Explains the process of dividing a dataset into training and testing parts, aiming to create and verify a predictive model. It clarifies the purpose of dividing a dataset into training and testing parts to facilitate the creation and verification of a predictive model in machine learning.']}, {'end': 2013.066, 'start': 1761.797, 'title': 'Car engine prediction model', 'summary': 'Discusses the process of creating a model to predict whether a car will have a v engine or a straight engine based on weight and displacement, and then splitting the dataset into 70% training data and 30% testing data.', 'duration': 251.269, 'highlights': ['The dataset is divided into 70% training data and 30% testing data, resulting in 20 observations in the training dataset and 12 observations in the testing dataset.', 'The prediction model will utilize the weight (WT) and displacement (DISP) of the car to predict whether it will have a V engine or a straight engine.', 'The values associated with each car include displacement, weight, horsepower, and whether it has a V or straight engine, with V engine represented as 1 and straight engine as 0.']}], 'duration': 517.831, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Up6KLx3m2ww/pics/Up6KLx3m2ww1495235.jpg', 'highlights': ['The chapter covers the basics of machine learning algorithms, including reinforcement learning and dataset preparation.', 'The dataset is divided into 70% training data and 30% testing data, resulting in 20 observations in the training dataset and 12 observations in the testing dataset.', 'The prediction model will utilize the weight (WT) and displacement (DISP) of the car to predict whether it will have a V engine or a straight engine.', 'Emphasizes the importance of identifying which algorithm fits a specific problem and understanding and selecting the appropriate algorithm for a given problem in machine learning.']}, {'end': 2697.699, 'segs': [{'end': 2046.589, 'src': 'embed', 'start': 2013.507, 'weight': 2, 'content': [{'end': 2017.371, 'text': 'So my testing data set is 30% of this, and it has these many values.', 'start': 2013.507, 'duration': 3.864}, {'end': 2022.375, 'text': "All right, so now we'll be, we have splitted our data set.", 'start': 2017.971, 'duration': 4.404}, {'end': 2031.124, 'text': "Now, let's now create a model, all right? So the creation of the model is same for each and every algorithm, just the command changes.", 'start': 2022.856, 'duration': 8.268}, {'end': 2034.599, 'text': 'So let me apply a regression algorithm right now.', 'start': 2031.997, 'duration': 2.602}, {'end': 2036.921, 'text': "It's called logistic regression.", 'start': 2034.839, 'duration': 2.082}, {'end': 2046.589, 'text': 'So what logistic regression does is it comes up with a value and with that value you decide whether it will be a one or a zero.', 'start': 2038.082, 'duration': 8.507}], 'summary': 'Testing data set is 30% of the total with logistic regression creating a model.', 'duration': 33.082, 'max_score': 2013.507, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Up6KLx3m2ww/pics/Up6KLx3m2ww2013507.jpg'}, {'end': 2102.451, 'src': 'embed', 'start': 2060.17, 'weight': 0, 'content': [{'end': 2064.031, 'text': 'But behind that classification, we are actually calculating probabilities.', 'start': 2060.17, 'duration': 3.861}, {'end': 2066.712, 'text': 'So we are actually coming up with a number.', 'start': 2064.731, 'duration': 1.981}, {'end': 2072.353, 'text': 'So if you are coming up with a number, we use regression algorithms.', 'start': 2068.092, 'duration': 4.261}, {'end': 2075.041, 'text': 'But when you talk about logistic regression,', 'start': 2072.998, 'duration': 2.043}, {'end': 2082.172, 'text': 'logistic regression actually comes under the classification algorithms as well and the regression algorithms as well.', 'start': 2075.041, 'duration': 7.131}, {'end': 2092.547, 'text': "So we'll be applying logistic regression, and the logistic regression will then give me a probability of, as in, when I give in the values Wt,", 'start': 2083.603, 'duration': 8.944}, {'end': 2095.069, 'text': 'that is, the weight of the car and the displacement of the car.', 'start': 2092.547, 'duration': 2.522}, {'end': 2099.55, 'text': 'it will give me a probability whether this car will have a VS engine.', 'start': 2095.069, 'duration': 4.481}, {'end': 2102.451, 'text': 'What is the probability of this car having a VS engine?', 'start': 2099.65, 'duration': 2.801}], 'summary': 'Using logistic regression to predict probability of car having a vs engine', 'duration': 42.281, 'max_score': 2060.17, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Up6KLx3m2ww/pics/Up6KLx3m2ww2060170.jpg'}, {'end': 2432.848, 'src': 'embed', 'start': 2405.185, 'weight': 4, 'content': [{'end': 2413.833, 'text': 'We predicted that the VS probability, the probability of that car having VS engine, is 0.007..', 'start': 2405.185, 'duration': 8.648}, {'end': 2417.436, 'text': "Let's compare whether our Ford Pantera L has a VS engine.", 'start': 2413.833, 'duration': 3.603}, {'end': 2421.38, 'text': 'So as you can see, there is zero over here.', 'start': 2418.157, 'duration': 3.223}, {'end': 2425.484, 'text': "That means that Ford doesn't have a VS engine.", 'start': 2421.4, 'duration': 4.084}, {'end': 2431.227, 'text': "So if it doesn't have a VS engine, It has a straight engine and we have predicted it right.", 'start': 2425.524, 'duration': 5.703}, {'end': 2432.848, 'text': 'Our value is correct.', 'start': 2431.468, 'duration': 1.38}], 'summary': 'Predicted vs probability: 0.007; ford pantera l has no vs engine.', 'duration': 27.663, 'max_score': 2405.185, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Up6KLx3m2ww/pics/Up6KLx3m2ww2405185.jpg'}, {'end': 2491.991, 'src': 'embed', 'start': 2466.929, 'weight': 3, 'content': [{'end': 2478.259, 'text': 'So as you can see, the probability of Corona having a VS engine is 0.77, all right? So it has a 77% probability of it having a VS engine.', 'start': 2466.929, 'duration': 11.33}, {'end': 2480.881, 'text': "Let's check whether it has a VS engine.", 'start': 2478.319, 'duration': 2.562}, {'end': 2487.087, 'text': 'So yes, it has a VS engine, and hence our model predicted the right value.', 'start': 2480.901, 'duration': 6.186}, {'end': 2491.991, 'text': 'Alright guys, so we created a model which can predict values now.', 'start': 2487.367, 'duration': 4.624}], 'summary': 'Model predicts corona having vs engine with 77% probability.', 'duration': 25.062, 'max_score': 2466.929, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Up6KLx3m2ww/pics/Up6KLx3m2ww2466929.jpg'}], 'start': 2013.507, 'title': 'Logistic regression model for car engine prediction', 'summary': "Covers the creation of a logistic regression model to predict the probability of a car having a vs engine based on its weight and displacement, using a testing dataset of 30% with specific values, highlighting the process of model creation and prediction. it also explains logistic regression for car engine prediction, demonstrating the model's accuracy in identifying the engine type based on given parameters, with a 77% probability of correctly predicting a car's vs engine.", 'chapters': [{'end': 2359.473, 'start': 2013.507, 'title': 'Logistic regression model creation', 'summary': 'Covers the creation of a logistic regression model to predict the probability of a car having a vs engine based on its weight and displacement, using a testing dataset of 30% with specific values, highlighting the process of model creation and prediction.', 'duration': 345.966, 'highlights': ['Creation of Logistic Regression Model Explains the process of creating a logistic regression model to predict the probability of a car having a VS engine based on its weight and displacement, using a testing dataset of 30% with specific values.', 'Splitting of Data Set Highlights the splitting of the data set into training and testing, with the testing dataset consisting of 30% of the total data.', 'Model Prediction Describes the process of predicting the probability of a car having a VS engine based on specific weight and displacement values using the created logistic regression model.']}, {'end': 2697.699, 'start': 2360.74, 'title': 'Logistic regression for car engine prediction', 'summary': "Explains logistic regression for car engine prediction, demonstrating the model's accuracy in identifying the engine type based on given parameters, with a 77% probability of correctly predicting a car's vs engine.", 'duration': 336.959, 'highlights': ['The model correctly predicted the absence of a VS engine in the Ford Pantera L, with a probability value of 0.007. The model accurately predicted the absence of a VS engine in the Ford Pantera L, with a low probability value of 0.007, showcasing its ability to make correct predictions.', 'The model successfully predicted the presence of a VS engine in the Toyota Corona, with a high probability value of 0.77. The model effectively predicted the presence of a VS engine in the Toyota Corona, with a high probability value of 0.77, demonstrating its ability to accurately classify the engine type based on the given parameters.', 'Explanation of logistic regression as both a classification and a regression algorithm, with a clear demonstration of probability-based classification. The transcript provides a clear explanation of logistic regression as both a classification and a regression algorithm, demonstrating how the model uses probability-based classification to determine the engine type of the cars.']}], 'duration': 684.192, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Up6KLx3m2ww/pics/Up6KLx3m2ww2013507.jpg', 'highlights': ['Creation of Logistic Regression Model: Explains the process of creating a logistic regression model to predict the probability of a car having a VS engine based on its weight and displacement, using a testing dataset of 30% with specific values.', 'Model Prediction: Describes the process of predicting the probability of a car having a VS engine based on specific weight and displacement values using the created logistic regression model.', 'Splitting of Data Set: Highlights the splitting of the data set into training and testing, with the testing dataset consisting of 30% of the total data.', 'The model effectively predicted the presence of a VS engine in the Toyota Corona, with a high probability value of 0.77, demonstrating its ability to accurately classify the engine type based on the given parameters.', 'The model correctly predicted the absence of a VS engine in the Ford Pantera L, with a low probability value of 0.007, showcasing its ability to make correct predictions.', 'Explanation of logistic regression as both a classification and a regression algorithm, with a clear demonstration of probability-based classification.']}], 'highlights': ['The chapter introduces the concept of machine learning, emphasizing the importance of algorithms and the different approaches, including supervised, reinforcement, and unsupervised learning.', 'The chapter explains the three main ways a machine can learn: supervised learning, reinforcement learning, and unsupervised learning, with a focus on supervised learning and its comparison to a classroom setting.', 'In supervised learning, machines are given inputs and corresponding answers, such as predicting rain based on parameters like humidity and temperature, and are able to make decisions based on past experiences to come up with a solution.', 'In unsupervised learning, machines are given inputs without corresponding answers, and they find patterns in the data, such as grouping fruits based on parameters like size, taste, and color, and identifying patterns in unstructured data like big data.', 'Reinforcement learning involves machines taking actions to maximize rewards based on past experiences, similar to teaching a dog through rewarding good behavior and punishing bad behavior, and the computer aims to maximize rewards through actions and past experiences.', 'The chapter follows a top-down approach, starting from the basics and understanding the use of algorithms in machine learning.', 'A demonstration is conducted to prepare a dataset for model creation and validation using the learned algorithms.', 'An algorithm is a step by step procedure towards solving a problem in the computer world.', 'The example of printing numbers from 1 to 20 demonstrates the step-by-step execution of an algorithm, incrementing a variable and checking its value until reaching the specified limit.', 'Reinforcement learning in temperature control system decides to lower temperature from 32 to 30 based on various parameters.', 'Reinforcement learning is inspired by the decision-making process in a maze, and problems are categorized into five types, leading to the use of different machine learning algorithms for each type.', 'Anomaly detection algorithms analyze patterns and alert when anomalies occur, such as in credit card transactions.', 'In credit card companies, each transaction is monitored, and unusual transactions trigger alerts for confirmation.', 'Regression algorithms are used to predict numerical values, such as temperature forecasts or determining discounts for customers, aiding in decision-making and optimizing business strategies.', 'Clustering algorithms are utilized in unsupervised learning to establish patterns and structures within unstructured data, allowing for categorization and decision-making based on the identified groups.', 'Reinforcement algorithms are employed to make decisions based on past experiences, demonstrated in examples such as training computers to play chess and guiding decision-making during gameplay.', 'The dataset is divided into 70% training data and 30% testing data, resulting in 20 observations in the training dataset and 12 observations in the testing dataset.', 'The prediction model will utilize the weight (WT) and displacement (DISP) of the car to predict whether it will have a V engine or a straight engine.', 'Emphasizes the importance of identifying which algorithm fits a specific problem and understanding and selecting the appropriate algorithm for a given problem in machine learning.', 'Creation of Logistic Regression Model: Explains the process of creating a logistic regression model to predict the probability of a car having a VS engine based on its weight and displacement, using a testing dataset of 30% with specific values.', 'Model Prediction: Describes the process of predicting the probability of a car having a VS engine based on specific weight and displacement values using the created logistic regression model.', 'Splitting of Data Set: Highlights the splitting of the data set into training and testing, with the testing dataset consisting of 30% of the total data.', 'The model effectively predicted the presence of a VS engine in the Toyota Corona, with a high probability value of 0.77, demonstrating its ability to accurately classify the engine type based on the given parameters.', 'The model correctly predicted the absence of a VS engine in the Ford Pantera L, with a low probability value of 0.007, showcasing its ability to make correct predictions.', 'Explanation of logistic regression as both a classification and a regression algorithm, with a clear demonstration of probability-based classification.']}