title

Machine Learning Crash Course-2 Hours | Learn Machine Learning | Machine Learning Tutorial | Edureka

description

๐ฅ Post Graduate Diploma in Artificial Intelligence by E&ICT Academy
NIT Warangal: https://www.edureka.co/executive-programs/machine-learning-and-ai
๐ฅ Topics Wise Machine Learning Podcast : https://castbox.fm/channel/id1832236?country=us
This Edureka Machine Learning video on "Machine Learning Full Course" will provide you with detailed and comprehensive knowledge of Machine Learning. It will provide you with the in-depth knowledge of the different types of Machine Learning with the different algorithms that lie under each category with a demo for each algorithm and the approach one should take to solve these problems. This Machine Learning tutorial will be covering the following topics:
1:44 What is Data Science?
3:09 Data Science Peripherals
3:37 What is Machine learning?
4:10 Features of Machine Learning
4:46 How it works?
5:36 Applications of Machine Learning
13:21 Market Trend of Machine Learning
14:29 Machine Learning Life Cycle
17:26 Important Python Libraries
19:20 Types of Machine Learning
19:33 Supervised Learning
20:50 Unsupervised Learning
21:52 Reinforcement Learning
23:23 Detailed Supervised Learning
24:50 Supervised Learning Algorithms
26:28 Linear Regression
28:53 Use Case(with Demo)
35:23 Model Fitting
36:36 Need for Logistic Regression
37:33 What is Logistic Regression?
39:46 What is Decision Tree?
49:33 What is Random Forest?
57:10 What is Naรฏve Bayes?
1:09:16 Detailed Unsupervised Learning
1:10:15 What is Clustering?
1:12:13 Types of Clustering
1:25:57 Market Basket Analysis
1:27:02 Association Rule Mining
1:29:06 Example
1:29:44 Apriori Algorithm
1:39:11 Detailed Reinforcement Learning
1:41:46 Reward Maximization
1:44:13 The Epsilon Greedy Algorithm
1:44:59 Markov Decision Process
1:47:19 Q-Learning
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๐ตPython Programming Certification: http://bit.ly/37rEsnA
๐ตPython Certification Training for Data Science: http://bit.ly/2Gj6fux
----------๐๐๐ฎ๐ซ๐๐ค๐ ๐๐๐ฌ๐ญ๐๐ซ๐ฌ ๐๐ซ๐จ๐ ๐ซ๐๐ฆ----------
๐ตData Scientist Masters Program: http://bit.ly/2t1snGM
๐ตMachine Learning Engineer Masters Program: https://bit.ly/3Hi1sXN
-----------๐๐๐ฎ๐ซ๐๐ค๐ ๐๐ง๐ข๐ฏ๐๐ซ๐ฌ๐ข๐ญ๐ฒ ๐๐ซ๐จ๐ ๐ซ๐๐ฆ----------
๐Post Graduate Diploma in Artificial Intelligence Course offered by E&ICT Academy
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#edureka #edurekamachinelearning #machinelearningcourse #machinelearningforbeginners #machinelearningtraining #machinelearningalgorithms #machinelearningusingpython #machinelearningproject
About the Masters Program
Edurekaโs Machine Learning Certification Training using Python helps you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naรฏve Bayes and Q-Learning. This Machine Learning using Python Training exposes you to concepts of Statistics, Time Series and different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. Throughout the Data Science Certification Course, youโll be solving real-life case studies on Media, Healthcare, Social Media, Aviation, HR.
Why Go for this Course?
Data Science is a set of techniques that enables the computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning
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Who should go for this course?
Edurekaโs Python Machine Learning Certification Course is a good fit for the below professionals:
Developers aspiring to be a โMachine Learning Engineer'
Analytics Managers who are leading a team of analysts
Business Analysts who want to understand Machine Learning (ML) Techniques
Information Architects who want to gain expertise in Predictive Analytics
'Python' professionals who want to design automatic predictive models
Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For TensorflowTraining and Certification, Call us at US: +18336900808 (Toll Free) or India: +918861301699 Or, write back to us at sales@edureka.co

detail

{'title': 'Machine Learning Crash Course-2 Hours | Learn Machine Learning | Machine Learning Tutorial | Edureka', 'heatmap': [{'end': 996.679, 'start': 850.903, 'weight': 0.861}, {'end': 1350.471, 'start': 1131.957, 'weight': 0.778}, {'end': 1634.817, 'start': 1416.32, 'weight': 0.761}, {'end': 2200.123, 'start': 2123.249, 'weight': 0.828}, {'end': 2412.013, 'start': 2338.551, 'weight': 0.705}, {'end': 4406.285, 'start': 4324.729, 'weight': 0.733}], 'summary': "This 2-hour machine learning tutorial covers an overview of machine learning, its applications in various industries with examples, python's significance in machine learning, sklearn for data set splitting and regression models, implementation of decision tree and logistic regression, predictive modeling and data analysis achieving 95.66% accuracy, k-means clustering for movie data, retail market basket analysis, and association rule mining, reinforcement learning, and q learning with practical examples and algorithms.", 'chapters': [{'end': 116.85, 'segs': [{'end': 53.777, 'src': 'embed', 'start': 10.902, 'weight': 1, 'content': [{'end': 14.844, 'text': 'Hello everyone and welcome to this interesting session on machine learning.', 'start': 10.902, 'duration': 3.942}, {'end': 19.986, 'text': "So before we move forward with our session, let's have a quick look at the agenda.", 'start': 15.584, 'duration': 4.402}, {'end': 23.548, 'text': "So, first of all, I'll be starting with an introduction to data science,", 'start': 20.546, 'duration': 3.002}, {'end': 32.712, 'text': "wherein I'll discuss how the growth of data led to the introduction of data science and how machine learning became a very important part of data science.", 'start': 23.548, 'duration': 9.164}, {'end': 34.123, 'text': 'So, then again,', 'start': 33.502, 'duration': 0.621}, {'end': 42.149, 'text': "we'll discuss what exactly is machine learning and the various application of machine learning in the day-to-day life and in the industry as well.", 'start': 34.123, 'duration': 8.026}, {'end': 49.975, 'text': "and moving forward, we'll discuss the various types of machine learning, which are namely the supervised, unsupervised and the reinforcement learning,", 'start': 42.149, 'duration': 7.826}, {'end': 53.777, 'text': "and we'll discuss all of these three types of machine learning in depth,", 'start': 49.975, 'duration': 3.802}], 'summary': 'Intro to machine learning, data science growth, types of ml discussed.', 'duration': 42.875, 'max_score': 10.902, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A10902.jpg'}, {'end': 97.053, 'src': 'embed', 'start': 73.135, 'weight': 0, 'content': [{'end': 80.263, 'text': 'data, as we know, is increasing at a really alarming rate and we are generating 2.5 quintillion bytes of it every day.', 'start': 73.135, 'duration': 7.128}, {'end': 87.044, 'text': 'We are living in an era of technological transformation that is bringing about changes in the way we take decisions.', 'start': 80.899, 'duration': 6.145}, {'end': 91.848, 'text': 'As big data is becoming pervasive across all the industries,', 'start': 87.665, 'duration': 4.183}, {'end': 97.053, 'text': 'use of machines to find patterns and predict futures is gaining a lot of prominence in the market.', 'start': 91.848, 'duration': 5.205}], 'summary': '2.5 quintillion bytes of data generated daily, fueling technological transformation and machine-driven pattern prediction.', 'duration': 23.918, 'max_score': 73.135, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A73135.jpg'}], 'start': 10.902, 'title': 'An overview of machine learning session', 'summary': 'Provides an overview of the session on machine learning, including data science introduction, types of machine learning, and the increasing daily data generation of 2.5 quintillion bytes.', 'chapters': [{'end': 116.85, 'start': 10.902, 'title': 'Machine learning session overview', 'summary': 'Provides an overview of the session on machine learning, covering topics such as data science introduction, types of machine learning, and the growing data generation with a daily output of 2.5 quintillion bytes.', 'duration': 105.948, 'highlights': ['The session covers an introduction to data science and its relation to the growth of data, as well as the importance of machine learning in data science. The session includes an introduction to data science and its relation to the growth of data, along with highlighting the importance of machine learning in data science.', 'The types of machine learning - supervised, unsupervised, and reinforcement learning - and their respective algorithms are discussed in depth. The session delves into the various types of machine learning, including supervised, unsupervised, and reinforcement learning, and provides in-depth discussions on their respective algorithms.', 'The daily data generation has reached 2.5 quintillion bytes, emphasizing the need for machine learning in analyzing and predicting patterns in the growing data. The chapter emphasizes the rapid increase in data generation, reaching 2.5 quintillion bytes daily, highlighting the necessity for machine learning in analyzing and predicting patterns in this growing data.']}], 'duration': 105.948, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A10902.jpg', 'highlights': ['The daily data generation has reached 2.5 quintillion bytes, emphasizing the need for machine learning in analyzing and predicting patterns in the growing data.', 'The types of machine learning - supervised, unsupervised, and reinforcement learning - and their respective algorithms are discussed in depth.', 'The session covers an introduction to data science and its relation to the growth of data, as well as the importance of machine learning in data science.']}, {'end': 1021.797, 'segs': [{'end': 196.073, 'src': 'embed', 'start': 172.157, 'weight': 2, 'content': [{'end': 179.045, 'text': 'Data science employs many techniques and theories from fields like mathematics, statistics, information science and computer science.', 'start': 172.157, 'duration': 6.888}, {'end': 181.488, 'text': 'It can be applied to small data sets.', 'start': 179.606, 'duration': 1.882}, {'end': 187.275, 'text': 'Also, yet most people think data science is when you are dealing with big data or larger amounts of data.', 'start': 182.089, 'duration': 5.186}, {'end': 195.152, 'text': 'Now, if you have a look at the peripherals of the data science, we have statistics, we have different programming languages, we have R Python,', 'start': 187.805, 'duration': 7.347}, {'end': 196.073, 'text': 'we have SS.', 'start': 195.152, 'duration': 0.921}], 'summary': 'Data science encompasses techniques from mathematics, statistics, and computer science, applicable to small and big data sets.', 'duration': 23.916, 'max_score': 172.157, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A172157.jpg'}, {'end': 239.394, 'src': 'embed', 'start': 209.225, 'weight': 3, 'content': [{'end': 211.247, 'text': 'And then again finally we have big data.', 'start': 209.225, 'duration': 2.022}, {'end': 215.988, 'text': "So let's focus on machine learning today and understand what exactly is machine learning.", 'start': 211.827, 'duration': 4.161}, {'end': 227.791, 'text': 'So machine learning is an application of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.', 'start': 216.428, 'duration': 11.363}, {'end': 235.453, 'text': 'Now getting computers to program themselves and also teaching them to make decisions using data.', 'start': 228.631, 'duration': 6.822}, {'end': 239.394, 'text': 'where writing software is a bottleneck, let the data do the work instead.', 'start': 235.453, 'duration': 3.941}], 'summary': 'Machine learning is an ai application that enables systems to learn and improve from experience, making decisions using data.', 'duration': 30.169, 'max_score': 209.225, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A209225.jpg'}, {'end': 391.074, 'src': 'embed', 'start': 359.809, 'weight': 4, 'content': [{'end': 364.77, 'text': 'the historic data of that route collected over time and a few tricks acquired from the other companies.', 'start': 359.809, 'duration': 4.961}, {'end': 370.912, 'text': 'Everyone using Maps is providing their location, their average speed, the route in which they are traveling,', 'start': 365.371, 'duration': 5.541}, {'end': 374.634, 'text': 'which in turn helps Google collect massive data about the traffic,', 'start': 370.912, 'duration': 3.722}, {'end': 380.696, 'text': 'which makes them predict the upcoming traffic and adjust your route according to it, which is pretty amazing, right?', 'start': 374.634, 'duration': 6.062}, {'end': 384.733, 'text': 'now coming to the second application, which is the social media.', 'start': 381.292, 'duration': 3.441}, {'end': 391.074, 'text': 'if we talk about Facebook, so one of the most common application is automatic friend tank suggestion in Facebook,', 'start': 384.733, 'duration': 6.341}], 'summary': 'Google maps collects massive traffic data to predict and adjust routes, while facebook uses automatic friend suggestions.', 'duration': 31.265, 'max_score': 359.809, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A359809.jpg'}, {'end': 480.477, 'src': 'embed', 'start': 456.802, 'weight': 1, 'content': [{'end': 465.326, 'text': 'it automatically detects your location and provides option to either go home or office or any other frequent places based on your history and patterns.', 'start': 456.802, 'duration': 8.524}, {'end': 473.031, 'text': 'it uses machine learning algorithm layered on top of historic trip data to make more accurate eta predictions.', 'start': 465.826, 'duration': 7.205}, {'end': 480.477, 'text': 'now uber, with the implementation of machine learning on their app and their website, saw a 26 accuracy in delivery and pickup.', 'start': 473.031, 'duration': 7.446}], 'summary': "Uber's use of machine learning led to a 26% accuracy improvement in delivery and pickup, and provided location-based trip options.", 'duration': 23.675, 'max_score': 456.802, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A456802.jpg'}, {'end': 582.306, 'src': 'embed', 'start': 557.51, 'weight': 0, 'content': [{'end': 565.116, 'text': "This is one of the coolest application of machine learning and in fact 35% of Amazon's revenue is generated by the product's recommendation.", 'start': 557.51, 'duration': 7.606}, {'end': 571, 'text': 'Now coming to the cool and highly technological side of machine learning, we have self-driving cars.', 'start': 565.676, 'duration': 5.324}, {'end': 575.82, 'text': "we talk about self-driving car, it's here and people are already using it now.", 'start': 571.676, 'duration': 4.144}, {'end': 582.306, 'text': "machine learning plays a very important role in self-driving cars, as i'm sure you guys might have heard about tesla,", 'start': 575.82, 'duration': 6.486}], 'summary': "35% of amazon's revenue is from ml product recommendations. self-driving cars are already in use and rely on machine learning.", 'duration': 24.796, 'max_score': 557.51, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A557510.jpg'}, {'end': 996.679, 'src': 'heatmap', 'start': 850.903, 'weight': 0.861, 'content': [{'end': 858.566, 'text': 'So that is why you see very high inclination during the 2016 period time as compared to 2012..', 'start': 850.903, 'duration': 7.663}, {'end': 868.849, 'text': 'So because during 2016 we got new hardware and we were able to find insights using those hardware and program and create models which would work on heavy data.', 'start': 858.566, 'duration': 10.283}, {'end': 872.251, 'text': "Now let's have a look at the life cycle of machine learning.", 'start': 869.35, 'duration': 2.901}, {'end': 875.892, 'text': 'So a typical machine learning life cycle has six steps.', 'start': 872.731, 'duration': 3.161}, {'end': 878.46, 'text': 'So the first step is collecting data.', 'start': 876.579, 'duration': 1.881}, {'end': 880.321, 'text': 'Second is data wrangling.', 'start': 878.92, 'duration': 1.401}, {'end': 884.124, 'text': 'Then we have the third step where we analyze the data.', 'start': 880.401, 'duration': 3.723}, {'end': 886.385, 'text': 'Fourth step where we train the algorithm.', 'start': 884.224, 'duration': 2.161}, {'end': 894.41, 'text': 'The fifth step is when we test the algorithm and the sixth step is when we deploy that particular algorithm for industrial uses.', 'start': 886.925, 'duration': 7.485}, {'end': 898.386, 'text': 'So, when we talk about the first step, which is collecting data,', 'start': 895.183, 'duration': 3.203}, {'end': 905.271, 'text': 'so here data is being collected from various sources and this stage involves the collection of all the relevant data from various sources.', 'start': 898.386, 'duration': 6.885}, {'end': 907.993, 'text': 'Now, if we talk about data wrangling,', 'start': 905.811, 'duration': 2.182}, {'end': 913.678, 'text': 'so data wrangling is the process of cleaning and converting raw data into a format that allows convenient consumption.', 'start': 907.993, 'duration': 5.685}, {'end': 918.041, 'text': 'Now. this is a very important part in the machine learning lifecycle,', 'start': 914.178, 'duration': 3.863}, {'end': 923.085, 'text': "as it's not every time that we receive a data which is clean and is in a proper format.", 'start': 918.041, 'duration': 5.044}, {'end': 928.926, 'text': 'sometimes there are values missing, sometimes there are wrong values, sometimes data format is different.', 'start': 923.585, 'duration': 5.341}, {'end': 934.348, 'text': 'so a major part in a machine learning lifecycle goes in data wrangling and data cleaning.', 'start': 928.926, 'duration': 5.422}, {'end': 943.55, 'text': 'so if we talk about the next step, which is data analysis, so data is analyzed to select and filter the data required to prepare the model.', 'start': 934.348, 'duration': 9.202}, {'end': 950.792, 'text': 'so in this step we take the data, use machine learning algorithms to create a particular model.', 'start': 943.55, 'duration': 7.242}, {'end': 955.458, 'text': 'now, next again, when we have a model, what we do is train the model.', 'start': 951.232, 'duration': 4.226}, {'end': 960.844, 'text': 'now, here we use the data sets and the algorithm is trained on the training data set,', 'start': 955.458, 'duration': 5.386}, {'end': 965.971, 'text': 'through which algorithm understand the pattern and the rules which govern the particular data.', 'start': 960.844, 'duration': 5.127}, {'end': 970.14, 'text': 'Once we have trained the algorithm, next comes testing.', 'start': 966.859, 'duration': 3.281}, {'end': 974.081, 'text': 'So the testing data set determines the accuracy of our model.', 'start': 970.52, 'duration': 3.561}, {'end': 983.004, 'text': "So what we do is provide the test data set to the model and which tells us the accuracy of the particular model, whether it's 60%, 70%, 80%,", 'start': 974.101, 'duration': 8.903}, {'end': 985.085, 'text': 'depending upon the requirement of the company.', 'start': 983.004, 'duration': 2.081}, {'end': 990.072, 'text': 'and finally, we have the operation and optimization.', 'start': 985.868, 'duration': 4.204}, {'end': 996.679, 'text': 'so if the speed and accuracy of the model is acceptable, then that model should be deployed in the real system.', 'start': 990.072, 'duration': 6.607}], 'summary': 'In 2016, new hardware improved data insights, leading to a 60-80% model accuracy in machine learning lifecycle.', 'duration': 145.776, 'max_score': 850.903, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A850903.jpg'}], 'start': 116.85, 'title': 'Machine learning applications', 'summary': "Discusses the significance of machine learning in analyzing big data and its applications in various industries, such as traffic prediction, social media face detection, personalized transportation, virtual personal assistants, and product recommendations. it also highlights examples of machine learning implementation, including a 26% accuracy improvement in uber's delivery and pickup and 35% of amazon's revenue being generated from product recommendations.", 'chapters': [{'end': 374.634, 'start': 116.85, 'title': 'Data science and machine learning', 'summary': 'Discusses the growing demand for data science professionals and the importance of machine learning in analyzing big data, emphasizing its applications and the ability to provide valuable insights, with a focus on navigation systems like google maps.', 'duration': 257.784, 'highlights': ['The industry will require 1.5 million managers and analysts with the skills to make decisions based on the analysis of big data, emphasizing the demand for individuals who can interpret and utilize data-driven insights. There is a need for 1.5 million managers and analysts with the skills to understand and make decisions based on the analysis of big data.', 'Data science is an interdisciplinary field that extracts knowledge from structured or unstructured data, employing techniques and theories from mathematics, statistics, information science, and computer science, indicating the diverse nature of data science and its broad application across various fields. Data science is an interdisciplinary field that extracts knowledge from structured or unstructured data, employing techniques and theories from mathematics, statistics, information science, and computer science.', 'Machine learning is an application of artificial intelligence that allows systems to automatically learn and improve from experience without explicit programming, enabling computers to find hidden insights using iterative algorithms without being explicitly programmed, showcasing the self-learning capability of machine learning algorithms. Machine learning is an application of artificial intelligence that allows systems to automatically learn and improve from experience without being explicitly programmed, enabling computers to find hidden insights using iterative algorithms without being explicitly programmed.', 'Google Maps utilizes data from users, historic data of routes, and other sources to provide real-time traffic information and route suggestions, demonstrating the practical application of machine learning in navigation systems. Google Maps utilizes data from users, historic data of routes, and other sources to provide real-time traffic information and route suggestions.']}, {'end': 557.29, 'start': 374.634, 'title': 'Applications of machine learning', 'summary': "Discusses various applications of machine learning including traffic prediction, social media face detection, personalized transportation, virtual personal assistants, and product recommendations, with examples such as 26% accuracy improvement in uber's delivery and pickup with machine learning implementation.", 'duration': 182.656, 'highlights': ["Uber saw a 26% accuracy improvement in delivery and pickup with the implementation of machine learning on their app and website. Uber's 26% accuracy improvement in delivery and pickup with machine learning implementation.", "Facebook uses face detection and image recognition to automatically suggest tagging people based on deep face, a machine learning project responsible for recognizing faces and providing alternative tags to images. Facebook's use of face detection and image recognition for automatic tagging based on deep face, a machine learning project.", 'The chapter also covers the use of machine learning in personalized transportation, virtual personal assistants, and product recommendations, with examples such as the tracking of search history for personalized ad recommendations. Various applications of machine learning in personalized transportation, virtual personal assistants, and product recommendations, including the tracking of search history for personalized ad recommendations.']}, {'end': 1021.797, 'start': 557.51, 'title': 'Applications of machine learning', 'summary': "Highlights the significant impact of machine learning in various industries, including 35% of amazon's revenue being generated from product recommendations, nvidia's use of unsupervised learning for self-driving cars, google translate's neural machine translation, dynamic pricing enabled by machine learning for services like uber, and the extensive use of machine learning by netflix to enhance user experience and retention.", 'duration': 464.287, 'highlights': ["35% of Amazon's revenue is generated by product recommendations. Amazon's revenue is significantly influenced by product recommendations which are powered by machine learning, contributing to 35% of the company's total revenue.", "NVIDIA's use of unsupervised learning for self-driving cars. NVIDIA employs unsupervised learning algorithms for self-driving cars, utilizing a vast amount of sensor data and IoT technology to facilitate autonomous driving.", "Google Translate's neural machine translation capabilities. Google Translate leverages neural machine translation and natural language processing to accurately translate content across thousands of languages, utilizing techniques like POS tagging and name entity recognition for enhanced accuracy.", 'Dynamic pricing enabled by machine learning for services like Uber. Machine learning enables dynamic pricing strategies for services like Uber, tracking buying trends and adjusting prices based on demand, leading to higher revenues during peak periods.', "Netflix's extensive use of machine learning for user experience and retention. Netflix employs machine learning algorithms to gather and analyze vast amounts of user data, enhancing user experience through personalized recommendations and achieving a substantial customer retention rate."]}], 'duration': 904.947, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A116850.jpg', 'highlights': ["35% of Amazon's revenue is generated by product recommendations.", 'Uber saw a 26% accuracy improvement in delivery and pickup with machine learning implementation.', 'Data science is an interdisciplinary field that extracts knowledge from structured or unstructured data, employing techniques and theories from mathematics, statistics, information science, and computer science.', 'Machine learning is an application of artificial intelligence that allows systems to automatically learn and improve from experience without explicit programming.', 'Google Maps utilizes data from users, historic data of routes, and other sources to provide real-time traffic information and route suggestions.']}, {'end': 1967.043, 'segs': [{'end': 1062.764, 'src': 'embed', 'start': 1022.437, 'weight': 0, 'content': [{'end': 1031.119, 'text': 'Now before we move forward, since machine learning is mostly done in Python and R, and if we have a look at the difference between Python and R,', 'start': 1022.437, 'duration': 8.682}, {'end': 1033.981, 'text': "I'm pretty sure most of the people would go for Python.", 'start': 1031.119, 'duration': 2.862}, {'end': 1044.973, 'text': 'And the major reason why people go for Python is because Python has more number of libraries and Python is being used in just more than data analysis and machine learning.', 'start': 1034.728, 'duration': 10.245}, {'end': 1049.637, 'text': 'So some of the important Python libraries here, which I want to discuss here.', 'start': 1045.374, 'duration': 4.263}, {'end': 1052.078, 'text': "So first of all, I'll talk about Matplotlib.", 'start': 1049.997, 'duration': 2.081}, {'end': 1059.282, 'text': 'Now, what Matplotlib does is that it enables you to make bar charts, scatter plots, the line charts, histogram.', 'start': 1052.398, 'duration': 6.884}, {'end': 1062.764, 'text': 'Basically, what it does is helps in the visualization aspect.', 'start': 1059.442, 'duration': 3.322}], 'summary': 'Python is preferred for machine learning due to more libraries and broader usage, with matplotlib enabling visualization.', 'duration': 40.327, 'max_score': 1022.437, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A1022437.jpg'}, {'end': 1350.471, 'src': 'heatmap', 'start': 1131.957, 'weight': 0.778, 'content': [{'end': 1139.026, 'text': 'It provides an abundance of useful features for operation on N arrays, which has a NumPy arrays and matrices in Python.', 'start': 1131.957, 'duration': 7.069}, {'end': 1141.81, 'text': 'And mostly it is used for mathematical purposes.', 'start': 1139.467, 'duration': 2.343}, {'end': 1145.175, 'text': 'So which gives a plus point to any machine learning algorithm.', 'start': 1141.89, 'duration': 3.285}, {'end': 1155.364, 'text': 'so, guys, these were the important python libraries which one must know in order to do any python programming for machine learning or as such.', 'start': 1145.615, 'duration': 9.749}, {'end': 1159.708, 'text': 'if you are doing python programming, you need to know about all of these libraries.', 'start': 1155.364, 'duration': 4.344}, {'end': 1162.951, 'text': 'so, guys, next, what we are going to discuss are the types of machine learning.', 'start': 1159.708, 'duration': 3.243}, {'end': 1170.337, 'text': 'So, then again, we have three types of machine learning which are supervised reinforcement and unsupervised machine learning.', 'start': 1163.431, 'duration': 6.906}, {'end': 1172.979, 'text': 'So if we talk about supervised machine learning.', 'start': 1170.737, 'duration': 2.242}, {'end': 1184.368, 'text': 'So supervised learning is where you have the input variables x and the output variable y and you use an algorithm to learn the mapping function from the input to the output.', 'start': 1173.179, 'duration': 11.189}, {'end': 1190.517, 'text': "so if we take the case of object detection here, so, or face detection, I'd rather say so.", 'start': 1184.708, 'duration': 5.809}, {'end': 1196.385, 'text': 'first of all, what we do is input the raw data in the form of labeled faces, and again,', 'start': 1190.517, 'duration': 5.868}, {'end': 1199.67, 'text': "it's not necessary that we just input faces to train the model.", 'start': 1196.385, 'duration': 3.285}, {'end': 1205.105, 'text': 'what we do is input a mixture of faces and non-faces images.', 'start': 1200.16, 'duration': 4.945}, {'end': 1209.029, 'text': 'so as you can see here we have label face and label non-faces.', 'start': 1205.105, 'duration': 3.924}, {'end': 1211.512, 'text': 'what we do is provide the data to the algorithm.', 'start': 1209.029, 'duration': 2.483}, {'end': 1214.175, 'text': 'the algorithm creates a model.', 'start': 1211.512, 'duration': 2.663}, {'end': 1223.804, 'text': 'it uses the training data set to understand what exactly is in a face, what exactly is in a picture which is not a face, and after the model is done,', 'start': 1214.175, 'duration': 9.629}, {'end': 1231.967, 'text': 'with the training and processing, so to test it, what we do is provide particular input of a phase or a non-phase.', 'start': 1223.804, 'duration': 8.163}, {'end': 1239.089, 'text': 'what we know see the major part of supervised learning here is that we exactly know the output.', 'start': 1231.967, 'duration': 7.122}, {'end': 1243.351, 'text': "so when we are providing a phase, we ourselves know that it's a phase.", 'start': 1239.089, 'duration': 4.262}, {'end': 1249.493, 'text': 'so to test that particular model and get the accuracy, we use the labeled, input raw data.', 'start': 1243.351, 'duration': 6.142}, {'end': 1252.952, 'text': 'So next, when we talk about unsupervised learning,', 'start': 1250.046, 'duration': 2.906}, {'end': 1258.643, 'text': 'unsupervised learning is the training of a model using information that is neither classified nor labeled.', 'start': 1252.952, 'duration': 5.691}, {'end': 1266.324, 'text': 'Now, this model can be used to cluster the input data in classes or the basis of the statistical properties.', 'start': 1259.119, 'duration': 7.205}, {'end': 1272.527, 'text': 'For example, for a basket full of vegetables, we can cluster different vegetables based upon their color or sizes.', 'start': 1266.444, 'duration': 6.083}, {'end': 1276.51, 'text': 'So, if I have a look at this particular example, here we have.', 'start': 1273.028, 'duration': 3.482}, {'end': 1281.813, 'text': 'what we are doing is we are inputting the raw data, which can be either apple, banana or mango.', 'start': 1276.51, 'duration': 5.303}, {'end': 1286.236, 'text': "What we don't have here, which was previously there in supervised learning, are the labels.", 'start': 1282.233, 'duration': 4.003}, {'end': 1292.357, 'text': 'so what the algorithm does is that it visually gets the features of a particular set of data.', 'start': 1286.856, 'duration': 5.501}, {'end': 1293.778, 'text': 'it makes clusters.', 'start': 1292.357, 'duration': 1.421}, {'end': 1301.52, 'text': 'so what will happen is that it will make a cluster of red looking fruits which are apple, yellow looking fruits which are banana and,', 'start': 1293.778, 'duration': 7.742}, {'end': 1309.162, 'text': 'based upon the shape also, it determines what exactly the fruit is and categorizes it as mango, banana or apple.', 'start': 1301.52, 'duration': 7.642}, {'end': 1310.982, 'text': 'so this is unsupervised learning.', 'start': 1309.162, 'duration': 1.82}, {'end': 1314.863, 'text': 'now, the third type of learning which we have here is reinforcement learning.', 'start': 1310.982, 'duration': 3.881}, {'end': 1319.728, 'text': 'So reinforcement learning is the learning by interacting with a space or an environment.', 'start': 1315.363, 'duration': 4.365}, {'end': 1327.076, 'text': 'It selects the action on the basis of its past experience, the exploration and also by new choices.', 'start': 1320.148, 'duration': 6.928}, {'end': 1334.604, 'text': 'A reinforcement learning agent learns from the consequences of its action rather than from being taught explicitly.', 'start': 1327.696, 'duration': 6.908}, {'end': 1343.588, 'text': 'So if we have a look at the example here, the input data we have, what it does is goes to the training, goes to the agent,', 'start': 1335.144, 'duration': 8.444}, {'end': 1350.471, 'text': 'where the agent selects the algorithm, it takes the best action from the environment, gets the reward and the model is trained.', 'start': 1343.588, 'duration': 6.883}], 'summary': 'Python libraries for machine learning and types of machine learning: supervised, unsupervised, and reinforcement learning, explained with examples.', 'duration': 218.514, 'max_score': 1131.957, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A1131957.jpg'}, {'end': 1184.368, 'src': 'embed', 'start': 1155.364, 'weight': 2, 'content': [{'end': 1159.708, 'text': 'if you are doing python programming, you need to know about all of these libraries.', 'start': 1155.364, 'duration': 4.344}, {'end': 1162.951, 'text': 'so, guys, next, what we are going to discuss are the types of machine learning.', 'start': 1159.708, 'duration': 3.243}, {'end': 1170.337, 'text': 'So, then again, we have three types of machine learning which are supervised reinforcement and unsupervised machine learning.', 'start': 1163.431, 'duration': 6.906}, {'end': 1172.979, 'text': 'So if we talk about supervised machine learning.', 'start': 1170.737, 'duration': 2.242}, {'end': 1184.368, 'text': 'So supervised learning is where you have the input variables x and the output variable y and you use an algorithm to learn the mapping function from the input to the output.', 'start': 1173.179, 'duration': 11.189}], 'summary': 'Python programming requires knowledge of various libraries. three types of machine learning: supervised, reinforcement, and unsupervised. supervised learning involves mapping input to output using x and y variables.', 'duration': 29.004, 'max_score': 1155.364, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A1155364.jpg'}, {'end': 1286.236, 'src': 'embed', 'start': 1259.119, 'weight': 4, 'content': [{'end': 1266.324, 'text': 'Now, this model can be used to cluster the input data in classes or the basis of the statistical properties.', 'start': 1259.119, 'duration': 7.205}, {'end': 1272.527, 'text': 'For example, for a basket full of vegetables, we can cluster different vegetables based upon their color or sizes.', 'start': 1266.444, 'duration': 6.083}, {'end': 1276.51, 'text': 'So, if I have a look at this particular example, here we have.', 'start': 1273.028, 'duration': 3.482}, {'end': 1281.813, 'text': 'what we are doing is we are inputting the raw data, which can be either apple, banana or mango.', 'start': 1276.51, 'duration': 5.303}, {'end': 1286.236, 'text': "What we don't have here, which was previously there in supervised learning, are the labels.", 'start': 1282.233, 'duration': 4.003}], 'summary': 'Model clusters input data based on statistical properties, such as grouping vegetables by color or size.', 'duration': 27.117, 'max_score': 1259.119, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A1259119.jpg'}, {'end': 1343.588, 'src': 'embed', 'start': 1320.148, 'weight': 5, 'content': [{'end': 1327.076, 'text': 'It selects the action on the basis of its past experience, the exploration and also by new choices.', 'start': 1320.148, 'duration': 6.928}, {'end': 1334.604, 'text': 'A reinforcement learning agent learns from the consequences of its action rather than from being taught explicitly.', 'start': 1327.696, 'duration': 6.908}, {'end': 1343.588, 'text': 'So if we have a look at the example here, the input data we have, what it does is goes to the training, goes to the agent,', 'start': 1335.144, 'duration': 8.444}], 'summary': 'Reinforcement learning agent learns from experience and consequences, not explicit teaching.', 'duration': 23.44, 'max_score': 1320.148, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A1320148.jpg'}, {'end': 1637.719, 'src': 'heatmap', 'start': 1406.417, 'weight': 3, 'content': [{'end': 1415.86, 'text': 'So supervised learning is where you have the input variable x and the output variable y and you use an algorithm to learn the MAPIC function from the input to the output.', 'start': 1406.417, 'duration': 9.443}, {'end': 1419.941, 'text': 'As I mentioned earlier with the example of face detection.', 'start': 1416.32, 'duration': 3.621}, {'end': 1429.264, 'text': 'So it is called supervised learning because the process of an algorithm learning from the training data set can be thought of as a teacher supervising the learning process.', 'start': 1420.301, 'duration': 8.963}, {'end': 1436.652, 'text': "So if we have a look at the supervised learning steps or what we'd rather say the workflow.", 'start': 1430.145, 'duration': 6.507}, {'end': 1439.395, 'text': 'So the model is used.', 'start': 1436.892, 'duration': 2.503}, {'end': 1444.181, 'text': 'as you can see here we have the historic data, then we again we have the random sampling.', 'start': 1439.395, 'duration': 4.786}, {'end': 1447.425, 'text': 'we split the data into training data set and the testing data set.', 'start': 1444.181, 'duration': 3.244}, {'end': 1455.651, 'text': 'Using the training data set, we, with the help of machine learning, which is supervised machine learning we create statistical model and then,', 'start': 1448.065, 'duration': 7.586}, {'end': 1460.575, 'text': 'after we have a model which is being generated with the help of the training data set,', 'start': 1455.651, 'duration': 4.924}, {'end': 1464.859, 'text': 'what we do is use the testing data set for prediction and testing.', 'start': 1460.575, 'duration': 4.284}, {'end': 1470.143, 'text': 'What we do is get the output and finally we have the model validation outcome.', 'start': 1465.179, 'duration': 4.964}, {'end': 1472.265, 'text': 'that was the training and testing.', 'start': 1470.683, 'duration': 1.582}, {'end': 1477.19, 'text': 'so if we have a look at the prediction part of any particular supervised learning algorithm,', 'start': 1472.265, 'duration': 4.925}, {'end': 1481.095, 'text': 'so the model is used for operating outcome of a new data set.', 'start': 1477.19, 'duration': 3.905}, {'end': 1487.562, 'text': 'so whenever performance of the model degraded, the model is retrained or if there are any performance issues,', 'start': 1481.095, 'duration': 6.467}, {'end': 1490.005, 'text': 'the model is retained with the help of the new data.', 'start': 1487.562, 'duration': 2.443}, {'end': 1495.6, 'text': 'Now, when we talk about supervising, there are not just one, but quite a few algorithms here.', 'start': 1490.635, 'duration': 4.965}, {'end': 1502.706, 'text': 'So we have linear regression, logistic regression, decision tree, we have random forest, we have nape bias classifiers.', 'start': 1495.66, 'duration': 7.046}, {'end': 1506.99, 'text': 'So linear regression is used to estimate real values.', 'start': 1503.227, 'duration': 3.763}, {'end': 1511.014, 'text': 'For example, the cost of houses, the number of calls, the total sales.', 'start': 1507.37, 'duration': 3.644}, {'end': 1513.528, 'text': 'based on the continuous variables.', 'start': 1511.627, 'duration': 1.901}, {'end': 1516.389, 'text': 'so that is what regular regression is.', 'start': 1513.528, 'duration': 2.861}, {'end': 1523.852, 'text': 'now, when we talk about logistic regression, it is used to estimate discrete values, for example, which are binary values like zero and one,', 'start': 1516.389, 'duration': 7.463}, {'end': 1528.114, 'text': 'yes or no, true and false, based on the given set of independent variables.', 'start': 1523.852, 'duration': 4.262}, {'end': 1536.097, 'text': 'so, for example, when we are talking about something like the chances of winning, Or if we talk about winning, which can be the true or false,', 'start': 1528.114, 'duration': 7.983}, {'end': 1539.318, 'text': 'Will it rain today? It can be the yes or no.', 'start': 1536.397, 'duration': 2.921}, {'end': 1546.98, 'text': 'So it cannot be like when the output of a particular algorithm or the particular question is either yes, no or binary,', 'start': 1539.738, 'duration': 7.242}, {'end': 1548.881, 'text': 'then only we use a logistic regression.', 'start': 1546.98, 'duration': 1.901}, {'end': 1554.404, 'text': 'now. next we have decision trees, so now these are used for classification problems.', 'start': 1549.361, 'duration': 5.043}, {'end': 1558.827, 'text': 'it works for both categorical and continuous dependent variables.', 'start': 1554.404, 'duration': 4.423}, {'end': 1564.291, 'text': 'and if we talk about random forest, so random forest is an ensemble of a decision tree.', 'start': 1558.827, 'duration': 5.464}, {'end': 1567.273, 'text': 'it gives better prediction and accuracy than decision tree.', 'start': 1564.291, 'duration': 2.982}, {'end': 1571.175, 'text': 'so that is another type of supervised learning algorithm.', 'start': 1567.273, 'duration': 3.902}, {'end': 1573.477, 'text': 'and finally we have the napis classifier.', 'start': 1571.175, 'duration': 2.302}, {'end': 1580.399, 'text': 'So it was a classification technique based on the Bayes theorem, with an assumption of independence between predictors.', 'start': 1573.937, 'duration': 6.462}, {'end': 1584.52, 'text': "So we'll get more into the details of all of these algorithms one by one.", 'start': 1580.939, 'duration': 3.581}, {'end': 1587.201, 'text': "So let's get started with linear regression.", 'start': 1584.84, 'duration': 2.361}, {'end': 1590.922, 'text': "So first of all, let's understand what exactly linear regression is.", 'start': 1587.781, 'duration': 3.141}, {'end': 1598.464, 'text': 'So linear regression analysis is a powerful technique used for operating the unknown value of a variable, which is the dependent variable,', 'start': 1591.002, 'duration': 7.462}, {'end': 1601.865, 'text': 'from the known value of another variable, which is the independent variable.', 'start': 1598.464, 'duration': 3.401}, {'end': 1608.108, 'text': 'so a dependent variable is the variable to be predicted or explained in a regression model,', 'start': 1602.205, 'duration': 5.903}, {'end': 1613.511, 'text': 'whereas an independent variable is a variable related to the dependent variable in a regression equation.', 'start': 1608.108, 'duration': 5.403}, {'end': 1620.535, 'text': "so if you have a look here at the simple linear regression, so it's basically equivalent to a simple line,", 'start': 1613.511, 'duration': 7.024}, {'end': 1624.777, 'text': 'which is with a slope which is y equals a plus bx.', 'start': 1620.535, 'duration': 4.242}, {'end': 1627.171, 'text': 'where y is the dependent variable.', 'start': 1625.47, 'duration': 1.701}, {'end': 1630.033, 'text': 'a is the y intercept.', 'start': 1627.171, 'duration': 2.862}, {'end': 1634.817, 'text': 'we have b, which is the slope of the line, and x, which is the independent variable.', 'start': 1630.033, 'duration': 4.784}, {'end': 1637.719, 'text': 'so intercept is the value of the dependent variable y.', 'start': 1634.817, 'duration': 2.902}], 'summary': 'Supervised learning involves training data, algorithms, and various models for prediction and testing.', 'duration': 231.302, 'max_score': 1406.417, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A1406417.jpg'}, {'end': 1472.265, 'src': 'embed', 'start': 1444.181, 'weight': 6, 'content': [{'end': 1447.425, 'text': 'we split the data into training data set and the testing data set.', 'start': 1444.181, 'duration': 3.244}, {'end': 1455.651, 'text': 'Using the training data set, we, with the help of machine learning, which is supervised machine learning we create statistical model and then,', 'start': 1448.065, 'duration': 7.586}, {'end': 1460.575, 'text': 'after we have a model which is being generated with the help of the training data set,', 'start': 1455.651, 'duration': 4.924}, {'end': 1464.859, 'text': 'what we do is use the testing data set for prediction and testing.', 'start': 1460.575, 'duration': 4.284}, {'end': 1470.143, 'text': 'What we do is get the output and finally we have the model validation outcome.', 'start': 1465.179, 'duration': 4.964}, {'end': 1472.265, 'text': 'that was the training and testing.', 'start': 1470.683, 'duration': 1.582}], 'summary': 'Data split for training and testing; supervised machine learning used to create and validate statistical model.', 'duration': 28.084, 'max_score': 1444.181, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A1444181.jpg'}, {'end': 1528.114, 'src': 'embed', 'start': 1487.562, 'weight': 7, 'content': [{'end': 1490.005, 'text': 'the model is retained with the help of the new data.', 'start': 1487.562, 'duration': 2.443}, {'end': 1495.6, 'text': 'Now, when we talk about supervising, there are not just one, but quite a few algorithms here.', 'start': 1490.635, 'duration': 4.965}, {'end': 1502.706, 'text': 'So we have linear regression, logistic regression, decision tree, we have random forest, we have nape bias classifiers.', 'start': 1495.66, 'duration': 7.046}, {'end': 1506.99, 'text': 'So linear regression is used to estimate real values.', 'start': 1503.227, 'duration': 3.763}, {'end': 1511.014, 'text': 'For example, the cost of houses, the number of calls, the total sales.', 'start': 1507.37, 'duration': 3.644}, {'end': 1513.528, 'text': 'based on the continuous variables.', 'start': 1511.627, 'duration': 1.901}, {'end': 1516.389, 'text': 'so that is what regular regression is.', 'start': 1513.528, 'duration': 2.861}, {'end': 1523.852, 'text': 'now, when we talk about logistic regression, it is used to estimate discrete values, for example, which are binary values like zero and one,', 'start': 1516.389, 'duration': 7.463}, {'end': 1528.114, 'text': 'yes or no, true and false, based on the given set of independent variables.', 'start': 1523.852, 'duration': 4.262}], 'summary': 'Various algorithms, including linear regression and logistic regression, are used to estimate real and discrete values based on different sets of independent variables.', 'duration': 40.552, 'max_score': 1487.562, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A1487562.jpg'}, {'end': 1620.535, 'src': 'embed', 'start': 1591.002, 'weight': 9, 'content': [{'end': 1598.464, 'text': 'So linear regression analysis is a powerful technique used for operating the unknown value of a variable, which is the dependent variable,', 'start': 1591.002, 'duration': 7.462}, {'end': 1601.865, 'text': 'from the known value of another variable, which is the independent variable.', 'start': 1598.464, 'duration': 3.401}, {'end': 1608.108, 'text': 'so a dependent variable is the variable to be predicted or explained in a regression model,', 'start': 1602.205, 'duration': 5.903}, {'end': 1613.511, 'text': 'whereas an independent variable is a variable related to the dependent variable in a regression equation.', 'start': 1608.108, 'duration': 5.403}, {'end': 1620.535, 'text': "so if you have a look here at the simple linear regression, so it's basically equivalent to a simple line,", 'start': 1613.511, 'duration': 7.024}], 'summary': 'Linear regression predicts dependent variable from independent variable.', 'duration': 29.533, 'max_score': 1591.002, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A1591002.jpg'}, {'end': 1689.929, 'src': 'embed', 'start': 1664.977, 'weight': 10, 'content': [{'end': 1674.442, 'text': 'What it does is it gives us an insight of the mutual relationship among variables and it is used for creating a correlation plot with the help of the seaborn library,', 'start': 1664.977, 'duration': 9.465}, {'end': 1678.284, 'text': 'which I mentioned earlier, which is one of the most important libraries in Python.', 'start': 1674.442, 'duration': 3.842}, {'end': 1681.726, 'text': 'So correlation is very important term to know about.', 'start': 1678.824, 'duration': 2.902}, {'end': 1689.929, 'text': 'Now, if we talk about regression lines, so Greenian regression analysis is a powerful technique used for predicting the unknown value of a variable,', 'start': 1682.126, 'duration': 7.803}], 'summary': 'Seaborn library creates correlation plots to analyze mutual relationships among variables, while greenian regression analysis predicts unknown variable values.', 'duration': 24.952, 'max_score': 1664.977, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A1664977.jpg'}, {'end': 1906.091, 'src': 'embed', 'start': 1878.896, 'weight': 11, 'content': [{'end': 1883.759, 'text': 'and Y here is dependent variable, which is the MEDV, which is the final price.', 'start': 1878.896, 'duration': 4.863}, {'end': 1887.921, 'text': 'So, first of all, what we need to do is plot a correlation.', 'start': 1884.419, 'duration': 3.502}, {'end': 1892.663, 'text': "So what we're going to do is import the seaborn library as SNS.", 'start': 1887.921, 'duration': 4.742}, {'end': 1897.985, 'text': "We're going to use the correlations to plot the correlation between the different 0 to 13 variables.", 'start': 1892.663, 'duration': 5.322}, {'end': 1902.928, 'text': "What we're going to do is also use METV here also.", 'start': 1900.446, 'duration': 2.482}, {'end': 1906.091, 'text': "So what we're going to do is SNS dot heat map correlations.", 'start': 1903.128, 'duration': 2.963}], 'summary': 'Plot correlation using seaborn library, visualize medv price correlation with 0 to 13 variables.', 'duration': 27.195, 'max_score': 1878.896, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A1878896.jpg'}], 'start': 1022.437, 'title': 'Machine learning with python', 'summary': "Discusses python's significance in machine learning over r, emphasizing key libraries such as matplotlib, seaborn, scikit-learn, pandas, and numpy. it also covers the types of machine learning, including supervised, reinforcement, and unsupervised learning.", 'chapters': [{'end': 1172.979, 'start': 1022.437, 'title': 'Python libraries for machine learning', 'summary': 'Discusses the importance of python over r for machine learning, highlighting key python libraries such as matplotlib, seaborn, scikit-learn, pandas, and numpy, and later touches on the types of machine learning, including supervised, reinforcement, and unsupervised learning.', 'duration': 150.542, 'highlights': ["Python is preferred over R for machine learning due to its extensive libraries and broader application beyond data analysis and machine learning. Python's popularity is attributed to its extensive libraries and diverse application in various domains.", 'Key Python libraries discussed include Matplotlib, Seaborn, scikit-learn, pandas, and NumPy, each serving specific purposes in data visualization, statistical modeling, data mining, data manipulation, and mathematical operations. The chapter delves into the significance of Matplotlib, Seaborn, scikit-learn, pandas, and NumPy, highlighting their distinct roles in data-related tasks.', 'The chapter also touches on the three types of machine learning: supervised, reinforcement, and unsupervised machine learning. A brief overview of the three main types of machine learning - supervised, reinforcement, and unsupervised learning - is provided.']}, {'end': 1363.298, 'start': 1173.179, 'title': 'Supervised vs unsupervised vs reinforcement learning', 'summary': 'Discusses the concepts of supervised, unsupervised, and reinforcement learning, highlighting the process, input data types, and outcomes of each, with examples and comparisons.', 'duration': 190.119, 'highlights': ['In supervised learning, a model is trained using labeled input data to understand the mapping function from input to output, providing a known output for testing and accuracy assessment.', 'Unsupervised learning involves training a model with unlabeled data to cluster input based on statistical properties, demonstrated by the clustering of fruits based on visual features and characteristics.', 'Reinforcement learning is characterized by learning through interaction with an environment, selecting actions based on past experiences and consequences, illustrated by the process of an agent selecting actions, receiving rewards, and training the model.']}, {'end': 1967.043, 'start': 1363.298, 'title': 'Understanding supervised learning', 'summary': 'Provides an in-depth understanding of supervised learning, covering the concept, workflow, and algorithms including linear regression, logistic regression, decision trees, random forest, and naive bayes classifier, with an emphasis on correlation and practical applications. it also includes a demonstration of using the boston housing dataset for pricing prediction.', 'duration': 603.745, 'highlights': ["Supervised learning workflow involves creating a statistical model using historic data, splitting it into training and testing datasets, and using the model for prediction and testing, with model validation outcome. The model is used for operating outcome of a new dataset, and when the model's performance degrades, it is retrained with new data.", 'Different types of supervised learning algorithms include linear regression, logistic regression, decision trees, random forest, and Naive Bayes classifier, each serving specific purposes such as estimating real values, discrete values, classification problems, and making predictions based on independence assumptions. Linear regression is used to estimate real values, while logistic regression is used to estimate discrete values. Decision trees work for both categorical and continuous dependent variables, and random forest provides better prediction and accuracy than decision trees.', 'Linear regression is a powerful technique used for predicting the unknown value of a variable from the known value of another variable, and correlation plays a crucial role in checking dependencies among variables. Linear regression analysis involves the dependent variable to be predicted and the independent variable related to it in a regression equation, and correlation is used to check the mutual relationship among variables and create correlation plots.', 'A demonstration of using the Boston housing dataset for pricing prediction involves plotting the correlation between different variables and using the seaborn library for visualization. The demonstration includes importing necessary libraries, creating variables X and Y, plotting the correlation, and visualizing the correlation plot to understand the dependencies.']}], 'duration': 944.606, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A1022437.jpg', 'highlights': ["Python's extensive libraries and diverse application make it preferred over R for machine learning.", 'Key Python libraries like Matplotlib, Seaborn, scikit-learn, pandas, and NumPy serve specific purposes in data-related tasks.', 'The chapter provides a brief overview of supervised, reinforcement, and unsupervised machine learning.', 'Supervised learning involves training a model using labeled input data to understand the mapping function from input to output.', 'Unsupervised learning clusters input based on statistical properties using unlabeled data.', 'Reinforcement learning involves learning through interaction with an environment and selecting actions based on past experiences and consequences.', 'The supervised learning workflow includes creating a statistical model, splitting data into training and testing datasets, and using the model for prediction and testing.', 'Different types of supervised learning algorithms include linear regression, logistic regression, decision trees, random forest, and Naive Bayes classifier.', 'Linear regression is used to estimate real values, logistic regression for discrete values, and decision trees for both categorical and continuous dependent variables.', 'Linear regression analysis involves the dependent variable to be predicted and the independent variable related to it in a regression equation.', 'Correlation plays a crucial role in checking dependencies among variables and creating correlation plots.', 'The demonstration involves using the Boston housing dataset for pricing prediction and visualizing the correlation plot using the seaborn library.']}, {'end': 2472.827, 'segs': [{'end': 2018.282, 'src': 'embed', 'start': 1989.207, 'weight': 0, 'content': [{'end': 1993.51, 'text': "and we're going to use the train test split function to split the x and y.", 'start': 1989.207, 'duration': 4.303}, {'end': 1999.793, 'text': "and here we're going to use the test size is 0.33, which will split the data set into.", 'start': 1993.51, 'duration': 6.283}, {'end': 2006.677, 'text': 'the test size will be 33%, well as the training size will be 67%.', 'start': 1999.793, 'duration': 6.884}, {'end': 2008.277, 'text': 'now this is dependent on you.', 'start': 2006.677, 'duration': 1.6}, {'end': 2018.282, 'text': 'usually it is either 60, 40, 70, 30 this depends on your use case, your data, you have, the kind of output you are getting,', 'start': 2008.277, 'duration': 10.005}], 'summary': 'Using train test split with 33% test data and 67% training data.', 'duration': 29.075, 'max_score': 1989.207, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A1989207.jpg'}, {'end': 2200.123, 'src': 'heatmap', 'start': 2123.249, 'weight': 0.828, 'content': [{'end': 2132.275, 'text': 'So, fitting a model means that you are making your algorithm learn the relationship between predictors and the outcomes so that you can predict the future values of the outcome.', 'start': 2123.249, 'duration': 9.026}, {'end': 2139.416, 'text': 'so the best failure model has a specific set of parameters which best defines the problem at hand.', 'start': 2132.731, 'duration': 6.685}, {'end': 2143.499, 'text': 'since this is a linear model with the equation y equals mx plus c.', 'start': 2139.416, 'duration': 4.083}, {'end': 2147.723, 'text': 'so in this case the parameters of the model learns from the data that are m and c.', 'start': 2143.499, 'duration': 4.224}, {'end': 2153.654, 'text': 'so this is what more fitting now, if it have a look at the types of fitting which are available.', 'start': 2148.567, 'duration': 5.087}, {'end': 2155.597, 'text': 'so first of all, machine learning algorithm.', 'start': 2153.654, 'duration': 1.943}, {'end': 2158.822, 'text': 'first attempt to solve the problem of under fitting, that is,', 'start': 2155.597, 'duration': 3.225}, {'end': 2164.63, 'text': 'of taking a line that does not approximate the data well and making it approximate to the data better.', 'start': 2158.822, 'duration': 5.808}, {'end': 2173.179, 'text': 'So machine does not know where to stop in order to solve the problem and it can go ahead from appropriate to overfit model sometimes.', 'start': 2165.237, 'duration': 7.942}, {'end': 2179.501, 'text': 'When we say a model overfits a dataset, we mean that it may have a low error rate for training data,', 'start': 2173.619, 'duration': 5.882}, {'end': 2183.902, 'text': 'but it may not generalize well to the overall population of the data we are interested in.', 'start': 2179.501, 'duration': 4.401}, {'end': 2188.234, 'text': 'so we have under fit, appropriate and over fit.', 'start': 2184.551, 'duration': 3.683}, {'end': 2189.535, 'text': 'these are the types of fitting.', 'start': 2188.234, 'duration': 1.301}, {'end': 2195.639, 'text': 'now, guys, this was linear regression, which is a type of supervised learning algorithm in machine learning.', 'start': 2189.535, 'duration': 6.104}, {'end': 2200.123, 'text': "so next, what we're gonna do is understand the need for logistic regression.", 'start': 2195.639, 'duration': 4.484}], 'summary': 'Fitting a model means learning the relationship between predictors and outcomes to predict future values. linear regression is a type of supervised learning algorithm in machine learning.', 'duration': 76.874, 'max_score': 2123.249, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A2123249.jpg'}, {'end': 2206.828, 'src': 'embed', 'start': 2179.501, 'weight': 1, 'content': [{'end': 2183.902, 'text': 'but it may not generalize well to the overall population of the data we are interested in.', 'start': 2179.501, 'duration': 4.401}, {'end': 2188.234, 'text': 'so we have under fit, appropriate and over fit.', 'start': 2184.551, 'duration': 3.683}, {'end': 2189.535, 'text': 'these are the types of fitting.', 'start': 2188.234, 'duration': 1.301}, {'end': 2195.639, 'text': 'now, guys, this was linear regression, which is a type of supervised learning algorithm in machine learning.', 'start': 2189.535, 'duration': 6.104}, {'end': 2200.123, 'text': "so next, what we're gonna do is understand the need for logistic regression.", 'start': 2195.639, 'duration': 4.484}, {'end': 2206.828, 'text': "So let's consider a use case, as, in political elections are being contested in our country,", 'start': 2200.823, 'duration': 6.005}], 'summary': 'Linear regression explained, next is logistic regression for political elections.', 'duration': 27.327, 'max_score': 2179.501, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A2179501.jpg'}, {'end': 2415.756, 'src': 'heatmap', 'start': 2318.524, 'weight': 2, 'content': [{'end': 2323.846, 'text': 'Now the logistic integration code is also called a sigmoid curve or the S curve.', 'start': 2318.524, 'duration': 5.322}, {'end': 2330.548, 'text': 'The sigmoid function converts any value from minus infinity to infinity to the discrete value 0 or 1.', 'start': 2324.206, 'duration': 6.342}, {'end': 2335.91, 'text': "Now how to decide whether the value is 0 or 1 from this curve? So let's take an example.", 'start': 2330.548, 'duration': 5.362}, {'end': 2338.051, 'text': 'What we do is provide a threshold value.', 'start': 2336.13, 'duration': 1.921}, {'end': 2341.154, 'text': 'we set it, we decide the output from that function.', 'start': 2338.551, 'duration': 2.603}, {'end': 2345.318, 'text': "so let's take an example with the threshold value of 0.4.", 'start': 2341.154, 'duration': 4.164}, {'end': 2352.386, 'text': 'so any value above 0.4 will be rounded off to 1 and anyone below 0.4 will be reduced to 0.', 'start': 2345.318, 'duration': 7.068}, {'end': 2355.409, 'text': 'so similarly we have polynomial regression also.', 'start': 2352.386, 'duration': 3.023}, {'end': 2360.915, 'text': 'so when we have nonlinear data which cannot be predicted with a linear model, we switch to the polynomial regression.', 'start': 2355.409, 'duration': 5.506}, {'end': 2363.376, 'text': 'now such a scenario is shown in the below graph.', 'start': 2361.375, 'duration': 2.001}, {'end': 2369.478, 'text': 'so, as you can see here, we have the equation y equals 3x cubed plus 4x squared minus 5x plus 2..', 'start': 2363.376, 'duration': 6.102}, {'end': 2370.339, 'text': 'now here.', 'start': 2369.478, 'duration': 0.861}, {'end': 2376.501, 'text': 'we cannot perform this linearly, so we need polynomial regression to solve these kind of problems.', 'start': 2370.339, 'duration': 6.162}, {'end': 2381.943, 'text': 'now, when we talk about logistic regression, there is an important term, which is decision tree,', 'start': 2376.501, 'duration': 5.442}, {'end': 2386.165, 'text': 'and this is one of the most used algorithms in supervised learning.', 'start': 2381.943, 'duration': 4.222}, {'end': 2388.566, 'text': "now let's understand what exactly is a decision tree.", 'start': 2386.165, 'duration': 2.401}, {'end': 2395.12, 'text': 'So our decision tree is a tree like structure, in which internal load represent tests on an attribute.', 'start': 2389.295, 'duration': 5.825}, {'end': 2401.705, 'text': 'Now each attribute represents outcome of test and each leaf node represents the class label,', 'start': 2395.48, 'duration': 6.225}, {'end': 2404.848, 'text': 'which is the decision taken after computing all attributes.', 'start': 2401.705, 'duration': 3.143}, {'end': 2412.013, 'text': 'apart from root to the leaf, represents classification rules and a decision tree is made from our data.', 'start': 2405.388, 'duration': 6.625}, {'end': 2415.756, 'text': 'by analyzing the variables from the decision tree now, from the tree,', 'start': 2412.013, 'duration': 3.743}], 'summary': 'Logistic integration uses sigmoid curve for binary classification, and decision tree is a widely used algorithm in supervised learning.', 'duration': 97.232, 'max_score': 2318.524, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A2318524.jpg'}, {'end': 2388.566, 'src': 'embed', 'start': 2363.376, 'weight': 3, 'content': [{'end': 2369.478, 'text': 'so, as you can see here, we have the equation y equals 3x cubed plus 4x squared minus 5x plus 2..', 'start': 2363.376, 'duration': 6.102}, {'end': 2370.339, 'text': 'now here.', 'start': 2369.478, 'duration': 0.861}, {'end': 2376.501, 'text': 'we cannot perform this linearly, so we need polynomial regression to solve these kind of problems.', 'start': 2370.339, 'duration': 6.162}, {'end': 2381.943, 'text': 'now, when we talk about logistic regression, there is an important term, which is decision tree,', 'start': 2376.501, 'duration': 5.442}, {'end': 2386.165, 'text': 'and this is one of the most used algorithms in supervised learning.', 'start': 2381.943, 'duration': 4.222}, {'end': 2388.566, 'text': "now let's understand what exactly is a decision tree.", 'start': 2386.165, 'duration': 2.401}], 'summary': 'Polynomial regression needed for y=3x^3+4x^2-5x+2. decision tree is important in logistic regression.', 'duration': 25.19, 'max_score': 2363.376, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A2363376.jpg'}], 'start': 1967.043, 'title': 'Using sklearn for data set splitting and regression models', 'summary': 'Discusses using sklearn to split a data set into 67% training size and 33% test size, and explains the process and applications of linear regression, logistic regression, polynomial regression, and decision trees in supervised learning, including the need, working principles, and use cases.', 'chapters': [{'end': 2018.282, 'start': 1967.043, 'title': 'Using sklearn for data set splitting', 'summary': 'Discusses using the train test split function from sklearn.crossvalidation to split a data set into 67% training size and 33% test size, which can be customized based on the use case and data at hand.', 'duration': 51.239, 'highlights': ['The train test split function from sklearn.crossvalidation is used to split the data into 67% training size and 33% test size. This method splits the data set into 67% training size and 33% test size, which can be customized based on the use case and data at hand.', 'The test size is set to 0.33, which determines the percentage of data allocated for testing. Setting the test size to 0.33 allocates 33% of the data for testing, leaving 67% for training.']}, {'end': 2472.827, 'start': 2018.282, 'title': 'Linear & logistic regression, polynomial regression, and decision trees', 'summary': 'Explains the process and applications of linear regression, logistic regression, polynomial regression, and decision trees in supervised learning, including the need, working principles, and use cases, with a focus on understanding the fitting types in machine learning, and the sigmoid curve in logistic regression.', 'duration': 454.545, 'highlights': ['Linear regression model created and fitted to training data to predict future values, with the scatter plot used to assess model accuracy and check the fitting types: overfit, appropriate, and underfit.', 'Logistic regression detailed with focus on the sigmoid curve, threshold values, and applications in binary classification, using an example of cancer diagnosis to illustrate its use case.', "Explanation of polynomial regression to address nonlinear data and decision tree's structure and implementation for classification rules and decision making based on attributes."]}], 'duration': 505.784, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A1967043.jpg', 'highlights': ['The train test split function from sklearn.crossvalidation is used to split the data into 67% training size and 33% test size.', 'Linear regression model created and fitted to training data to predict future values, with the scatter plot used to assess model accuracy and check the fitting types: overfit, appropriate, and underfit.', 'Logistic regression detailed with focus on the sigmoid curve, threshold values, and applications in binary classification, using an example of cancer diagnosis to illustrate its use case.', "Explanation of polynomial regression to address nonlinear data and decision tree's structure and implementation for classification rules and decision making based on attributes.", 'The test size is set to 0.33, which determines the percentage of data allocated for testing.']}, {'end': 3095.244, 'segs': [{'end': 2527.432, 'src': 'embed', 'start': 2497.138, 'weight': 1, 'content': [{'end': 2499.499, 'text': 'so the final decision tree looks like this.', 'start': 2497.138, 'duration': 2.361}, {'end': 2504.541, 'text': 'so first of all, we check if the outlook is sunny, overcast or rain.', 'start': 2499.499, 'duration': 5.042}, {'end': 2506.981, 'text': "if it's overcast, we will play.", 'start': 2504.541, 'duration': 2.44}, {'end': 2509.402, 'text': "if it's sunny, we then again check the humidity.", 'start': 2506.981, 'duration': 2.421}, {'end': 2511.783, 'text': 'if the humidity is high, we will not play.', 'start': 2509.862, 'duration': 1.921}, {'end': 2513.985, 'text': 'if the humidity is normal, we will play.', 'start': 2511.783, 'duration': 2.202}, {'end': 2522.349, 'text': 'when again, in the case of rainy, we check the wind, if the wind is weak, the play will go on, and similarly, if the wind is strong,', 'start': 2513.985, 'duration': 8.364}, {'end': 2523.85, 'text': 'the play must stop.', 'start': 2522.349, 'duration': 1.501}, {'end': 2527.432, 'text': 'so this is how exactly a decision tree works.', 'start': 2523.85, 'duration': 3.582}], 'summary': 'Final decision tree: outlook (sunny, overcast, rain) -> humidity (high, normal) -> play; rain -> wind (weak, strong) -> play', 'duration': 30.294, 'max_score': 2497.138, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A2497138.jpg'}, {'end': 2940.299, 'src': 'embed', 'start': 2912.797, 'weight': 0, 'content': [{'end': 2915.939, 'text': 'So the accuracy here we get is 0.91..', 'start': 2912.797, 'duration': 3.142}, {'end': 2921.583, 'text': "Then again, what we need to do, this was logistic regression, normal logistic regression, we're going to use classifier.", 'start': 2915.939, 'duration': 5.644}, {'end': 2928.914, 'text': "So we're going to create a decision tree classifier with random state given as 0.", 'start': 2921.823, 'duration': 7.091}, {'end': 2933.856, 'text': "Now, what next we're going to do is create the cross-validation score, which is the CLF.", 'start': 2928.914, 'duration': 4.942}, {'end': 2940.299, 'text': 'We take the model, we take the train X, train Y and CV equals 10, the cross-validation score.', 'start': 2934.336, 'duration': 5.963}], 'summary': 'Achieved 91% accuracy using logistic regression, creating decision tree classifier with random state 0, and cross-validation score of 10.', 'duration': 27.502, 'max_score': 2912.797, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A2912797.jpg'}, {'end': 3059.367, 'src': 'embed', 'start': 3027.814, 'weight': 2, 'content': [{'end': 3033.046, 'text': "So let's understand random falls with the help of the hurricanes and typhoons dataset.", 'start': 3027.814, 'duration': 5.232}, {'end': 3038.772, 'text': 'So we have the data about hurricanes and typhoons from 1851 to 2014.', 'start': 3033.467, 'duration': 5.305}, {'end': 3044.396, 'text': 'And the data comprises of location, wind, the pressure of tropical cyclones in the Pacific Ocean.', 'start': 3038.772, 'duration': 5.624}, {'end': 3052.462, 'text': 'Based on the data, we have to classify the storms into hurricanes, typhoons and the subcategories as for the two predefined classes mentioned.', 'start': 3044.656, 'duration': 7.806}, {'end': 3059.367, 'text': 'So the predefined classes are TD, tropical cyclone of tropical depression intensity, which is less than 34 knots.', 'start': 3053.002, 'duration': 6.365}], 'summary': 'Analyze hurricanes and typhoons data to classify storms into predefined categories based on location, wind, and pressure.', 'duration': 31.553, 'max_score': 3027.814, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A3027814.jpg'}], 'start': 2472.827, 'title': 'Implementing decision tree and logistic regression', 'summary': 'Covers decision tree implementation with an example related to weather conditions and logistic regression using a cancer dataset for predicting benign or malignant cancer. it also includes data analysis, preprocessing, visualization, and the implementation of logistic regression, decision tree classifier, and random forest, with logistic regression achieving an accuracy of 0.91.', 'chapters': [{'end': 2566.623, 'start': 2472.827, 'title': 'Decision tree and logistic regression', 'summary': 'Explains the concept of decision tree using an example of playing based on weather conditions and later discusses the implementation of logistic regression using a cancer dataset, with the goal of predicting benign or malignant cancer.', 'duration': 93.796, 'highlights': ['The final decision tree is based on weather conditions, where decisions are made based on outlook, humidity, and wind, determining whether to play or not. The decision tree is based on three main conditions: outlook, humidity, and wind, to determine the decision of playing or not, showcasing the process of decision-making based on weather conditions.', 'Logistic regression implementation involves using a cancer dataset with the goal of predicting whether the cancer is benign or malignant. The implementation of logistic regression involves using a cancer dataset to predict whether the cancer is benign or malignant, demonstrating the practical application of logistic regression in healthcare.']}, {'end': 3095.244, 'start': 2567.472, 'title': 'Data analysis with logistic regression and decision tree', 'summary': 'Covers importing necessary libraries for data analysis, data preprocessing using pandas, visualizing data using seaborn and matplotlib, and implementing logistic regression, decision tree classifier, and random forest for model accuracy check and usage scenarios, with logistic regression achieving an accuracy of 0.91.', 'duration': 527.772, 'highlights': ['Implemented logistic regression and achieved an accuracy of 0.91 The logistic regression model was implemented and achieved an accuracy of 0.91, indicating its effectiveness in the given dataset.', 'Explained the process of using decision tree classifier and cross-validation score The chapter provided a detailed explanation of the process of using decision tree classifier and cross-validation score to check the accuracy of the model.', 'Described the concept and advantages of random forest in data analysis The chapter described the concept of random forest as an ensemble classifier, its advantages over decision tree models, and its efficiency in handling large datasets and identifying important variables in classification.', 'Illustrated the application of random forest with a weather data example The chapter illustrated the application of random forest with a weather data example, demonstrating its usage in classifying storms based on predefined classes and the data attributes.']}], 'duration': 622.417, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A2472827.jpg', 'highlights': ['Logistic regression achieved an accuracy of 0.91 in cancer dataset', 'Decision tree based on weather conditions showcased decision-making process', 'Random forest illustrated with weather data example for classifying storms', 'Explanation of using decision tree classifier and cross-validation score']}, {'end': 4109.055, 'segs': [{'end': 3120.283, 'src': 'embed', 'start': 3095.505, 'weight': 3, 'content': [{'end': 3102.851, 'text': 'So as you can see, this is the data in which we have the ID, name, date, event, status, length, longitude, maximum when, minimum when.', 'start': 3095.505, 'duration': 7.346}, {'end': 3104.673, 'text': 'There are so many variables.', 'start': 3103.292, 'duration': 1.381}, {'end': 3107.976, 'text': "So let's start with importing the pandas.", 'start': 3105.034, 'duration': 2.942}, {'end': 3110.178, 'text': 'Then again, we import the matplotlib.', 'start': 3108.197, 'duration': 1.981}, {'end': 3113.502, 'text': "Then we're going to use the aggregate method in matplotlib.", 'start': 3110.639, 'duration': 2.863}, {'end': 3120.283, 'text': "we're going to use the matplotlib inline, which is used for plotting interactive graph, and i like it most for plots.", 'start': 3114.202, 'duration': 6.081}], 'summary': 'The data includes id, name, date, event, status, length, longitude, maximum, and minimum; utilizing pandas and matplotlib for aggregation and interactive graph plotting.', 'duration': 24.778, 'max_score': 3095.505, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A3095505.jpg'}, {'end': 3165.297, 'src': 'embed', 'start': 3135.107, 'weight': 1, 'content': [{'end': 3138.968, 'text': 'we have to import metrics for checking the accuracy,', 'start': 3135.107, 'duration': 3.861}, {'end': 3145.948, 'text': 'then we have to import sklearn and then again from sklearn we have to import tree from sklearn.ensemble.', 'start': 3138.968, 'duration': 6.98}, {'end': 3150.43, 'text': "we're going to import the random forest classifier from sklearn.metrics.", 'start': 3145.948, 'duration': 4.482}, {'end': 3158.734, 'text': "we're going to import confusion matrix so as to check the accuracy, and from sklearn.metrics we're going to also import the accuracy score.", 'start': 3150.43, 'duration': 8.304}, {'end': 3165.297, 'text': "so let's import random and let's read the data set and print the first six rows of the data sets.", 'start': 3158.734, 'duration': 6.563}], 'summary': 'Import metrics and classifiers from sklearn for accuracy check.', 'duration': 30.19, 'max_score': 3135.107, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A3135107.jpg'}, {'end': 3453.868, 'src': 'embed', 'start': 3407.933, 'weight': 2, 'content': [{'end': 3418.818, 'text': 'So, as I mentioned earlier, usually random forest gives a better output or creates a better model than the decision tree classifier because,', 'start': 3407.933, 'duration': 10.885}, {'end': 3421.92, 'text': 'as I mentioned earlier, it combines the result from different models.', 'start': 3418.818, 'duration': 3.102}, {'end': 3422.2, 'text': 'you know.', 'start': 3421.92, 'duration': 0.28}, {'end': 3429.524, 'text': 'So the final decision is based upon the majority of votes and is usually higher than the decision tree models.', 'start': 3422.76, 'duration': 6.764}, {'end': 3435.854, 'text': "So guys, let's move ahead with our naive bias algorithm and let's see what exactly is naive bias.", 'start': 3430.467, 'duration': 5.387}, {'end': 3440.861, 'text': 'So naive bias is a simple but surprisingly powerful algorithm for predictive modeling.', 'start': 3435.894, 'duration': 4.967}, {'end': 3448.386, 'text': 'Now it is a classification technique based on the Bayes theorem with an assumption of independence among predictors.', 'start': 3441.443, 'duration': 6.943}, {'end': 3451.947, 'text': 'It comprises of two parts which are the naive and the bias.', 'start': 3449.026, 'duration': 2.921}, {'end': 3453.868, 'text': 'So, in simple terms,', 'start': 3452.427, 'duration': 1.441}], 'summary': 'Random forest outperforms decision tree by combining results from different models, achieving higher accuracy.', 'duration': 45.935, 'max_score': 3407.933, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A3407933.jpg'}, {'end': 4063.041, 'src': 'embed', 'start': 4038.489, 'weight': 0, 'content': [{'end': 4046.313, 'text': "which is the classifier predict x test, and we're going to compare the y underscore predict with the y underscore test to see the accuracies for that.", 'start': 4038.489, 'duration': 7.824}, {'end': 4050.694, 'text': "so for that we're gonna import sklearn.metrics.", 'start': 4047.052, 'duration': 3.642}, {'end': 4052.915, 'text': "we're gonna import the accuracy score.", 'start': 4050.694, 'duration': 2.221}, {'end': 4054.977, 'text': "now let's compare both of these.", 'start': 4052.915, 'duration': 2.062}, {'end': 4059.459, 'text': 'so the accuracy, what we get is 95.54 percent.', 'start': 4054.977, 'duration': 4.482}, {'end': 4063.041, 'text': 'now another way is to get a confusion matrix build.', 'start': 4059.459, 'duration': 3.582}], 'summary': 'Using sklearn.metrics, the classifier predicted x test with 95.54% accuracy and built a confusion matrix.', 'duration': 24.552, 'max_score': 4038.489, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A4038489.jpg'}], 'start': 3095.505, 'title': 'Predictive modeling and data analysis', 'summary': 'Covers data analysis using pandas and matplotlib, implementing machine learning models with scikit-learn, achieving a 95.66% accuracy with random forest classifier, and discusses naive bayes algorithm achieving 95.54% accuracy.', 'chapters': [{'end': 3227.045, 'start': 3095.505, 'title': 'Data analysis with pandas and matplotlib', 'summary': 'Covers the process of importing data using pandas and matplotlib, performing data preprocessing, and implementing machine learning models from scikit-learn for accuracy evaluation, including the use of random forest classifier and confusion matrix, with a total of 22 columns in the dataset.', 'duration': 131.54, 'highlights': ['The chapter covers the process of importing data using pandas and matplotlib, performing data preprocessing, and implementing machine learning models from scikit-learn for accuracy evaluation, including the use of random forest classifier and confusion matrix, with a total of 22 columns in the dataset.', 'The dataset contains 22 columns including ID, name, date, event, status, length, longitude, maximum when, and minimum when.', 'The process involves importing pandas and matplotlib, using the aggregate method, plotting interactive graphs, importing model selection for train test split, and importing metrics for accuracy evaluation.', 'The chapter also involves importing scikit-learn, using tree from sklearn.ensemble, importing random forest classifier from sklearn.metrics, and importing confusion matrix and accuracy score from sklearn.metrics.', 'The data preprocessing includes converting a column to categorical data, dropping unnecessary columns such as latitude, longitude, ID, name, date, and time, and obtaining the frequency of different typhoons.']}, {'end': 4109.055, 'start': 3227.045, 'title': 'Predictive modeling with random forest and naive bayes', 'summary': "Discusses the process of data splitting, model training, and prediction using a random forest classifier and decision tree classifier, achieving 95.66% and 95.57% accuracy respectively, followed by a detailed explanation of the naive bayes algorithm, its working, and industrial use cases, concluding with an implementation example achieving 95.54% accuracy using scikit-learn's naive bayes model.", 'duration': 882.01, 'highlights': ['Discussion on random forest and decision tree classifiers The chapter discusses the process of data splitting, model training, and prediction using a random forest classifier and decision tree classifier, achieving 95.66% and 95.57% accuracy, respectively.', 'Detailed explanation of the Naive Bayes algorithm The chapter provides a detailed explanation of the Naive Bayes algorithm, its working, and industrial use cases, highlighting its simplicity and usefulness for large datasets.', "Implementation example achieving 95.54% accuracy using scikit-learn's Naive Bayes model An example of implementing the Naive Bayes classifier using scikit-learn, achieving an accuracy of 95.54% and generating a classification report."]}], 'duration': 1013.55, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A3095505.jpg', 'highlights': ['Covers data analysis using pandas and matplotlib, implementing machine learning models with scikit-learn, achieving a 95.66% accuracy with random forest classifier, and discusses naive bayes algorithm achieving 95.54% accuracy.', 'The chapter covers the process of importing data using pandas and matplotlib, performing data preprocessing, and implementing machine learning models from scikit-learn for accuracy evaluation, including the use of random forest classifier and confusion matrix, with a total of 22 columns in the dataset.', 'Discussion on random forest and decision tree classifiers The chapter discusses the process of data splitting, model training, and prediction using a random forest classifier and decision tree classifier, achieving 95.66% and 95.57% accuracy, respectively.', 'The dataset contains 22 columns including ID, name, date, event, status, length, longitude, maximum when, and minimum when.', 'Detailed explanation of the Naive Bayes algorithm The chapter provides a detailed explanation of the Naive Bayes algorithm, its working, and industrial use cases, highlighting its simplicity and usefulness for large datasets.', "Implementation example achieving 95.54% accuracy using scikit-learn's Naive Bayes model An example of implementing the Naive Bayes classifier using scikit-learn, achieving an accuracy of 95.54% and generating a classification report."]}, {'end': 5157.132, 'segs': [{'end': 4133.453, 'src': 'embed', 'start': 4109.055, 'weight': 0, 'content': [{'end': 4114.88, 'text': 'So, guys, this is how you exactly use the Gaussian Envy or the Naive Bayes classifier on it,', 'start': 4109.055, 'duration': 5.825}, {'end': 4124.265, 'text': 'and all of these types of algorithms which are present in the supervised or unsupervised or reinforcement learning are all present in the scikit-learn library.', 'start': 4114.88, 'duration': 9.385}, {'end': 4131.071, 'text': "so Once again I say sklearn is a very important library when you're dealing with machine learning, because you do not have to code any algorithm,", 'start': 4124.265, 'duration': 6.806}, {'end': 4132.192, 'text': 'hard code any algorithm.', 'start': 4131.071, 'duration': 1.121}, {'end': 4133.453, 'text': 'Every algorithm is present.', 'start': 4132.252, 'duration': 1.201}], 'summary': 'Scikit-learn offers a variety of algorithms for supervised, unsupervised, and reinforcement learning, making it a crucial library for machine learning.', 'duration': 24.398, 'max_score': 4109.055, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A4109055.jpg'}, {'end': 4196.643, 'src': 'embed', 'start': 4173.952, 'weight': 1, 'content': [{'end': 4181.496, 'text': 'so, example, we can cluster different bikes based upon the speed limit, their acceleration or the average that they are giving.', 'start': 4173.952, 'duration': 7.544}, {'end': 4191.92, 'text': 'so unsupervised learning is a type of machine learning algorithm used to draw inferences from data sets consisting of input data without labeled responses.', 'start': 4181.496, 'duration': 10.424}, {'end': 4196.643, 'text': 'so if you have a look at the workflow or the process flow of unsupervised learning,', 'start': 4191.92, 'duration': 4.723}], 'summary': 'Unsupervised learning clusters bikes based on speed, acceleration, and average without labeled responses.', 'duration': 22.691, 'max_score': 4173.952, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A4173952.jpg'}, {'end': 4321.865, 'src': 'embed', 'start': 4293.547, 'weight': 2, 'content': [{'end': 4301.692, 'text': 'so that is why clustering is used in the industry and if you have a look at the various use cases of clustering in the industry, so first of all,', 'start': 4293.547, 'duration': 8.145}, {'end': 4310.116, 'text': "it's being used in marketing, so discovering distinct groups in customer databases, such as customers who make a lot of long distance calls,", 'start': 4301.692, 'duration': 8.424}, {'end': 4313.258, 'text': 'customers who use internet more than calls.', 'start': 4310.116, 'duration': 3.142}, {'end': 4321.865, 'text': "they're also using insurance companies for like identifying groups of corporation insurance policy holders with high average claim rate, farmers,", 'start': 4313.258, 'duration': 8.607}], 'summary': 'Clustering is used in marketing to identify distinct customer groups based on usage patterns, such as long distance calls and internet usage.', 'duration': 28.318, 'max_score': 4293.547, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A4293547.jpg'}, {'end': 4389.357, 'src': 'embed', 'start': 4359.959, 'weight': 3, 'content': [{'end': 4362.982, 'text': 'So an example of this is the K-mean clustering.', 'start': 4359.959, 'duration': 3.023}, {'end': 4366.504, 'text': 'So K-mean clustering does this exclusive kind of clustering.', 'start': 4363.022, 'duration': 3.482}, {'end': 4369.326, 'text': 'So secondly we have overlapping clustering.', 'start': 4366.864, 'duration': 2.462}, {'end': 4371.688, 'text': 'So it is also known as soft clusters.', 'start': 4369.386, 'duration': 2.302}, {'end': 4379.234, 'text': 'In this an item can belong to multiple clusters as its degree of association with each cluster is shown.', 'start': 4372.088, 'duration': 7.146}, {'end': 4386.736, 'text': 'and for example, we have fuzzy or the C means clustering, which is being used for overlapping clustering.', 'start': 4380.034, 'duration': 6.702}, {'end': 4389.357, 'text': 'and finally we have the hierarchical clustering.', 'start': 4386.736, 'duration': 2.621}], 'summary': 'The transcript discusses k-mean, overlapping, and hierarchical clustering techniques.', 'duration': 29.398, 'max_score': 4359.959, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A4359959.jpg'}, {'end': 4428.811, 'src': 'heatmap', 'start': 4324.729, 'weight': 4, 'content': [{'end': 4331.64, 'text': 'they are used in seismic studies and define probable areas of oil or gas exploration based on seismic data,', 'start': 4324.729, 'duration': 6.911}, {'end': 4336.526, 'text': "and they're also used in the recommendation of movies, if you'd say.", 'start': 4331.64, 'duration': 4.886}, {'end': 4339.127, 'text': "they're also used in Flickr photos.", 'start': 4336.526, 'duration': 2.601}, {'end': 4343.41, 'text': "they're also used by Amazon for recommending the product which category it lies in.", 'start': 4339.127, 'duration': 4.283}, {'end': 4346.871, 'text': 'so, basically, if we talk about clustering, there are three types of clustering.', 'start': 4343.41, 'duration': 3.461}, {'end': 4350.774, 'text': 'so first of all, we have the exclusive clustering, which is the hard clustering.', 'start': 4346.871, 'duration': 3.903}, {'end': 4359.899, 'text': 'so here an item belongs exclusively to one cluster, not several clusters, and The data point belong exclusively to one cluster.', 'start': 4350.774, 'duration': 9.125}, {'end': 4362.982, 'text': 'So an example of this is the K-mean clustering.', 'start': 4359.959, 'duration': 3.023}, {'end': 4366.504, 'text': 'So K-mean clustering does this exclusive kind of clustering.', 'start': 4363.022, 'duration': 3.482}, {'end': 4369.326, 'text': 'So secondly we have overlapping clustering.', 'start': 4366.864, 'duration': 2.462}, {'end': 4371.688, 'text': 'So it is also known as soft clusters.', 'start': 4369.386, 'duration': 2.302}, {'end': 4379.234, 'text': 'In this an item can belong to multiple clusters as its degree of association with each cluster is shown.', 'start': 4372.088, 'duration': 7.146}, {'end': 4386.736, 'text': 'and for example, we have fuzzy or the C means clustering, which is being used for overlapping clustering.', 'start': 4380.034, 'duration': 6.702}, {'end': 4389.357, 'text': 'and finally we have the hierarchical clustering.', 'start': 4386.736, 'duration': 2.621}, {'end': 4396.979, 'text': 'so when two clusters have a parent-child relationship or a tree-like structure, then it is known as hierarchical cluster.', 'start': 4389.357, 'duration': 7.622}, {'end': 4402.32, 'text': 'so as you can see here from the example, we have a parent-child kind of relationship in the cluster given here.', 'start': 4396.979, 'duration': 5.341}, {'end': 4406.285, 'text': "So let's understand what exactly is k-means clustering?", 'start': 4403.104, 'duration': 3.181}, {'end': 4412.767, 'text': 'So k-means clustering is an algorithm whose main goal is to group similar elements of data points into a cluster.', 'start': 4406.505, 'duration': 6.262}, {'end': 4419.028, 'text': 'And it is the process by which objects are classified into a predefined number of groups,', 'start': 4413.507, 'duration': 5.521}, {'end': 4428.811, 'text': 'so that they are as much dissimilar as possible from one group to another group, but as much as similar or possible within each group.', 'start': 4419.028, 'duration': 9.783}], 'summary': 'Clustering is used in various domains for recommendation and classification. there are 3 types: exclusive (e.g. k-mean), overlapping (e.g. c means), and hierarchical clustering.', 'duration': 25.707, 'max_score': 4324.729, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A4324729.jpg'}, {'end': 4623.614, 'src': 'embed', 'start': 4595.994, 'weight': 7, 'content': [{'end': 4599.616, 'text': 'they should be smaller, so the distortion is also smaller.', 'start': 4595.994, 'duration': 3.622}, {'end': 4606.4, 'text': 'now the idea of the ELBO method is to choose the key at which the SSE decreases abruptly.', 'start': 4599.616, 'duration': 6.784}, {'end': 4614.888, 'text': 'so, for example here, if we have a look at the figure given here, we see that the best number of cluster is at the elbow.', 'start': 4606.4, 'duration': 8.488}, {'end': 4619.051, 'text': 'so as you can see here, the graph here changes abruptly after the number four.', 'start': 4614.888, 'duration': 4.163}, {'end': 4623.614, 'text': "so for this particular example, we're going to use four as a number of cluster.", 'start': 4619.051, 'duration': 4.563}], 'summary': 'Elbo method finds the best number of clusters at the elbow, with an example choosing four clusters.', 'duration': 27.62, 'max_score': 4595.994, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A4595994.jpg'}, {'end': 4692.164, 'src': 'embed', 'start': 4660.61, 'weight': 5, 'content': [{'end': 4663.411, 'text': 'now, k-means is not exactly a very good method.', 'start': 4660.61, 'duration': 2.801}, {'end': 4666.633, 'text': "so let's understand the pros and cons of k-means clustering.", 'start': 4663.411, 'duration': 3.222}, {'end': 4669.434, 'text': 'we know that k-means is simple and understandable.', 'start': 4666.633, 'duration': 2.801}, {'end': 4674.656, 'text': 'everyone learns it at the first go the items automatically assigned to the clusters.', 'start': 4669.434, 'duration': 5.222}, {'end': 4676.597, 'text': 'now, if we have a look at the cons.', 'start': 4674.656, 'duration': 1.941}, {'end': 4679.438, 'text': 'so first of all one needs to define the number of clusters.', 'start': 4676.597, 'duration': 2.841}, {'end': 4681.779, 'text': 'this is a very heavy task as us.', 'start': 4679.878, 'duration': 1.901}, {'end': 4687.421, 'text': 'if we have three, four or if we have 10 categories and if we do not know what the number of clusters are going to be,', 'start': 4681.779, 'duration': 5.642}, {'end': 4692.164, 'text': "it's very difficult for anyone to you know to guess the number of clusters.", 'start': 4687.421, 'duration': 4.743}], 'summary': 'K-means clustering: simple, but requires defining number of clusters; a challenging task with 3, 4, or 10 categories.', 'duration': 31.554, 'max_score': 4660.61, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A4660610.jpg'}, {'end': 4768.411, 'src': 'embed', 'start': 4743.374, 'weight': 6, 'content': [{'end': 4748.66, 'text': "So what we're going to do is now use k-means clustering for the movie data set.", 'start': 4743.374, 'duration': 5.286}, {'end': 4753.045, 'text': 'So we have to find out the number of clusters and divide it accordingly.', 'start': 4748.74, 'duration': 4.305}, {'end': 4761.187, 'text': 'so the use case is that, first of all, we have a data set of 5000 movies and what we want to do is group them,', 'start': 4753.943, 'duration': 7.244}, {'end': 4766.23, 'text': 'group the movies into clusters based on the facebook likes.', 'start': 4761.187, 'duration': 5.043}, {'end': 4768.411, 'text': "so, guys, let's have a look at the demo here.", 'start': 4766.23, 'duration': 2.181}], 'summary': 'Using k-means clustering to group 5000 movies based on facebook likes.', 'duration': 25.037, 'max_score': 4743.374, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A4743374.jpg'}, {'end': 4929.685, 'src': 'embed', 'start': 4903.072, 'weight': 8, 'content': [{'end': 4911.256, 'text': "see, that's exactly what I was going to say is that initially the main challenge in k-means clustering is to define the number of centers which are the k.", 'start': 4903.072, 'duration': 8.184}, {'end': 4920.64, 'text': 'so as you can see here that the third center and the zeroth cluster, the third cluster and the zeroth cluster are very, very close to each other.', 'start': 4911.256, 'duration': 9.384}, {'end': 4921.361, 'text': 'so, guys,', 'start': 4920.64, 'duration': 0.721}, {'end': 4929.685, 'text': 'it probably could have been in one another cluster and the another disadvantage was that we do not exactly know how the points are to be arranged.', 'start': 4921.361, 'duration': 8.324}], 'summary': 'Initial challenge in k-means clustering is defining number of centers, with points close to each other and uncertainty in point arrangement.', 'duration': 26.613, 'max_score': 4903.072, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A4903072.jpg'}, {'end': 5057.684, 'src': 'embed', 'start': 5013.4, 'weight': 9, 'content': [{'end': 5017.044, 'text': 'So first of all, it allows a data point to be in multiple clusters.', 'start': 5013.4, 'duration': 3.644}, {'end': 5017.645, 'text': "That's a pro.", 'start': 5017.084, 'duration': 0.561}, {'end': 5020.708, 'text': "It's a more neutral representation of the behavior of genes.", 'start': 5017.945, 'duration': 2.763}, {'end': 5023.651, 'text': 'Genes usually are involved in multiple functions.', 'start': 5021.269, 'duration': 2.382}, {'end': 5028.176, 'text': "So it is a very good type of clustering when we're talking about genes.", 'start': 5024.152, 'duration': 4.024}, {'end': 5036.426, 'text': 'First of all, and again, if we talk about the cons, again, we have to define C, which is the number of clusters, same as K.', 'start': 5028.92, 'duration': 7.506}, {'end': 5039.189, 'text': 'Next, we need to determine the membership cutoff value also.', 'start': 5036.426, 'duration': 2.763}, {'end': 5043.312, 'text': "So that takes a lot of time and it's time consuming.", 'start': 5039.269, 'duration': 4.043}, {'end': 5047.135, 'text': 'And the clusters are sensitive to initial assignment of centroid.', 'start': 5043.592, 'duration': 3.543}, {'end': 5055.002, 'text': 'So a slight change or deviation from the centroid is going to result in a very different kind of, you know,', 'start': 5047.175, 'duration': 7.827}, {'end': 5057.684, 'text': 'a funny kind of output we get from the fuzzy C means.', 'start': 5055.002, 'duration': 2.682}], 'summary': 'Fuzzy c means clustering allows data points in multiple clusters, suitable for genes, but time-consuming and sensitive to initial centroid assignment.', 'duration': 44.284, 'max_score': 5013.4, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A5013400.jpg'}, {'end': 5094.461, 'src': 'embed', 'start': 5074.418, 'weight': 11, 'content': [{'end': 5085.688, 'text': 'So a hierarchical clustering is an alternative approach which builds a hierarchy from the bottom up or the top to bottom and does not require to specify the number of clusters beforehand.', 'start': 5074.418, 'duration': 11.27}, {'end': 5088.018, 'text': 'Now the algorithm works as in.', 'start': 5086.337, 'duration': 1.681}, {'end': 5094.461, 'text': 'first of all, we put each data point in its own cluster, identify the closest two cluster and combine them into one more cluster.', 'start': 5088.018, 'duration': 6.443}], 'summary': 'Hierarchical clustering builds hierarchy without predefined cluster count.', 'duration': 20.043, 'max_score': 5074.418, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A5074418.jpg'}], 'start': 4109.055, 'title': 'K-means clustering for movie data', 'summary': 'Covers scikit-learn library for machine learning algorithms, unsupervised learning, including clustering with examples and use cases in marketing, insurance, seismic studies, and recommendation systems. it discusses k-means clustering methodology, the elbow method, and pros and cons of k-means clustering, and applies k-means clustering to a movie dataset, highlighting challenges, advantages, and disadvantages.', 'chapters': [{'end': 4339.127, 'start': 4109.055, 'title': 'Supervised and unsupervised learning', 'summary': 'Covers the importance of scikit-learn library for various machine learning algorithms, the workflow and applications of unsupervised learning, focusing on the process of clustering, with examples and use cases in marketing, insurance, seismic studies, and recommendation systems.', 'duration': 230.072, 'highlights': ['The scikit-learn library is vital for machine learning as it contains various supervised, unsupervised, and reinforcement learning algorithms, eliminating the need to hard code algorithms. The scikit-learn library encompasses various supervised, unsupervised, and reinforcement learning algorithms, streamlining the process by eliminating the need to hard code any algorithm.', 'Unsupervised learning helps cluster unstructured and unlabeled data into classes based on statistical properties, with applications such as clustering bikes based on speed limit, acceleration, and average. Unsupervised learning aids in clustering unstructured and unlabeled data based on statistical properties, illustrated by clustering bikes based on speed limit, acceleration, and average.', 'Clustering is the process of dividing data sets into groups of similar data points, used in various industries for applications like marketing, insurance, seismic studies, and recommendation systems. Clustering involves grouping similar data points and finds applications in diverse industries such as marketing, insurance, seismic studies, and recommendation systems.']}, {'end': 4742.744, 'start': 4339.127, 'title': 'K-means clustering: overview and methodology', 'summary': 'Discusses the three types of clustering - exclusive, overlapping, and hierarchical clustering, with a detailed focus on k-means clustering methodology and the elbow method for determining the number of clusters. it also presents the pros and cons of k-means clustering, highlighting its simplicity and limitations in handling noisy data and outliers.', 'duration': 403.617, 'highlights': ['The chapter discusses the three types of clustering - exclusive, overlapping, and hierarchical clustering It outlines the three types of clustering: exclusive (hard clustering), overlapping (soft clustering), and hierarchical clustering, providing examples like K-mean clustering and C means clustering.', 'Detailed focus on k-means clustering methodology and the elbow method for determining the number of clusters It explains the k-means clustering algorithm, emphasizing the process of grouping similar data points into clusters and the iterative steps involved in finding centroids and assigning data points. Additionally, it introduces the elbow method for determining the optimal number of clusters based on the sum squared error (SSE) and abrupt changes in the SSE-K plot.', 'Presentation of the pros and cons of k-means clustering It highlights the simplicity and automatic assignment of items to clusters as pros of k-means clustering, while addressing the challenges of defining the number of clusters, forcing items into clusters, and its limitations in handling noisy data and outliers as cons.']}, {'end': 5157.132, 'start': 4743.374, 'title': 'K-means clustering for movie data', 'summary': 'Discusses using k-means clustering to group a dataset of 5000 movies based on facebook likes, demonstrating the process of importing, fitting, and plotting the clusters with a focus on the challenges, advantages, and disadvantages of k-means, fuzzy c-means, and hierarchical clustering.', 'duration': 413.758, 'highlights': ['K-means clustering is used to group a dataset of 5000 movies based on Facebook likes. The dataset consists of 5000 movies, and the goal is to group them into clusters based on Facebook likes.', 'The number of clusters is determined using the elbow method with a provided number of clusters as five. The number of clusters is chosen as five, and the elbow method is used to determine the optimal number of clusters.', 'Challenges of k-means clustering include defining the number of centers and difficulty in forcing data into clusters. Challenges of k-means clustering include defining the number of centers and the difficulty in forcing data into clusters, where the third and zeroth clusters are found to be very close.', 'Fuzzy C-means clustering allows data points to belong to multiple clusters, with pros including more neutral representation of gene behavior and cons such as the need to define the number of clusters and sensitivity to initial centroid assignment. Fuzzy C-means clustering allows data points to belong to multiple clusters, providing a more neutral representation of gene behavior. However, it also requires defining the number of clusters and is sensitive to the initial centroid assignment.', 'Hierarchical clustering builds a hierarchy without specifying the number of clusters beforehand, but has disadvantages including irreversible cluster combination and becoming slow with large datasets. Hierarchical clustering builds a hierarchy without specifying the number of clusters beforehand, but it has disadvantages including irreversible cluster combination and becoming slow with large datasets.']}], 'duration': 1048.077, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A4109055.jpg', 'highlights': ['The scikit-learn library contains various supervised, unsupervised, and reinforcement learning algorithms, streamlining the process by eliminating the need to hard code any algorithm.', 'Unsupervised learning aids in clustering unstructured and unlabeled data based on statistical properties, illustrated by clustering bikes based on speed limit, acceleration, and average.', 'Clustering involves grouping similar data points and finds applications in diverse industries such as marketing, insurance, seismic studies, and recommendation systems.', 'It outlines the three types of clustering: exclusive (hard clustering), overlapping (soft clustering), and hierarchical clustering, providing examples like K-mean clustering and C means clustering.', 'It explains the k-means clustering algorithm, emphasizing the process of grouping similar data points into clusters and the iterative steps involved in finding centroids and assigning data points.', 'It highlights the simplicity and automatic assignment of items to clusters as pros of k-means clustering, while addressing the challenges of defining the number of clusters, forcing items into clusters, and its limitations in handling noisy data and outliers as cons.', 'The dataset consists of 5000 movies, and the goal is to group them into clusters based on Facebook likes.', 'The number of clusters is chosen as five, and the elbow method is used to determine the optimal number of clusters.', 'Challenges of k-means clustering include defining the number of centers and the difficulty in forcing data into clusters, where the third and zeroth clusters are found to be very close.', 'Fuzzy C-means clustering allows data points to belong to multiple clusters, providing a more neutral representation of gene behavior.', 'However, it also requires defining the number of clusters and is sensitive to the initial centroid assignment.', 'Hierarchical clustering builds a hierarchy without specifying the number of clusters beforehand, but it has disadvantages including irreversible cluster combination and becoming slow with large datasets.']}, {'end': 5837.628, 'segs': [{'end': 5236.72, 'src': 'embed', 'start': 5177.299, 'weight': 0, 'content': [{'end': 5182.863, 'text': 'Now it works by looking for combination of items that occur together frequently in the transactions.', 'start': 5177.299, 'duration': 5.564}, {'end': 5188.967, 'text': 'To put it in another way, it allows retailers to identify the relationships between the items that the people buy.', 'start': 5183.363, 'duration': 5.604}, {'end': 5192.65, 'text': 'For example, people who buy bread also tend to buy butter.', 'start': 5189.328, 'duration': 3.322}, {'end': 5202.936, 'text': 'The marketing team at the retail stores should target customers who buy bread and butter and provide them an offer so that they buy a third item like an egg.', 'start': 5193.231, 'duration': 9.705}, {'end': 5211.02, 'text': 'So if a customer buys bread and butter and sees a discount or an offer on eggs, he will be encouraged to spend more money and buy the eggs.', 'start': 5203.196, 'duration': 7.824}, {'end': 5214.081, 'text': 'Now, this is what market basket analysis is all about.', 'start': 5211.44, 'duration': 2.641}, {'end': 5220.264, 'text': 'Now to find the association between the two items and make predictions about what the customers will buy.', 'start': 5214.461, 'duration': 5.803}, {'end': 5224.966, 'text': 'there are two algorithms, which are the association rule mining and the a priori algorithm.', 'start': 5220.264, 'duration': 4.702}, {'end': 5228.756, 'text': "so let's discuss each of these algorithm with an example.", 'start': 5225.575, 'duration': 3.181}, {'end': 5236.72, 'text': "first of all, if we have a look at the association rule mining now, it's a technique that shows how items are associated to each other.", 'start': 5228.756, 'duration': 7.964}], 'summary': 'Market basket analysis identifies item associations to drive targeted promotions and increase sales.', 'duration': 59.421, 'max_score': 5177.299, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A5177299.jpg'}, {'end': 5286.322, 'src': 'embed', 'start': 5257.133, 'weight': 1, 'content': [{'end': 5264.943, 'text': 'now there are three common ways to measure a particular association, because we have to find these rules on the basis of some statistics right.', 'start': 5257.133, 'duration': 7.81}, {'end': 5267.225, 'text': 'so what we do is use support,', 'start': 5264.943, 'duration': 2.282}, {'end': 5276.493, 'text': 'confidence and lift now these three common ways and the measures to have a look at the association rule mining and know exactly how good is that rule.', 'start': 5267.225, 'duration': 9.268}, {'end': 5278.335, 'text': 'so first of all, we have support.', 'start': 5276.493, 'duration': 1.842}, {'end': 5286.322, 'text': "so support gives the fraction of the transaction which contains an item a and b, so it's basically the frequency of the item in the whole item set,", 'start': 5278.335, 'duration': 7.987}], 'summary': 'Measuring association using support, confidence, and lift.', 'duration': 29.189, 'max_score': 5257.133, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A5257133.jpg'}, {'end': 5736.93, 'src': 'embed', 'start': 5711.701, 'weight': 4, 'content': [{'end': 5717.683, 'text': "So now let's learn how association rules are used in market basket analysis problems.", 'start': 5711.701, 'duration': 5.982}, {'end': 5725.066, 'text': "So what we'll do is we'll be using the online transactional data of a retail store for generating association rules.", 'start': 5717.743, 'duration': 7.323}, {'end': 5734.129, 'text': 'So first of all, what you need to do is import Pandas, MLX, D&D libraries from the imported and read the data.', 'start': 5725.466, 'duration': 8.663}, {'end': 5736.93, 'text': "So first of all, what we're going to do is read the data.", 'start': 5734.209, 'duration': 2.721}], 'summary': 'Using association rules for market basket analysis with retail transaction data.', 'duration': 25.229, 'max_score': 5711.701, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A5711701.jpg'}], 'start': 5157.132, 'title': 'Retail market basket analysis', 'summary': 'Covers market basket analysis in retail, focusing on identifying item associations to increase sales, and discusses association rule mining and a priori algorithm with measures like support, confidence, and lift.', 'chapters': [{'end': 5214.081, 'start': 5157.132, 'title': 'Market basket analysis in retail', 'summary': 'Discusses the concept of market basket analysis, a key technique used by large retailers to identify item associations, allowing them to target specific customers and increase sales by offering relevant discounts or offers.', 'duration': 56.949, 'highlights': ['Market basket analysis is a key technique used by large retailers to uncover association between items, allowing them to identify the relationships between the items that the people buy.', 'It works by looking for combinations of items that occur together frequently in transactions, enabling retailers to target customers who buy specific items and provide them with relevant offers, ultimately encouraging increased spending.', 'For example, if a customer buys bread and butter, and sees a discount or an offer on eggs, they will be encouraged to spend more money and buy the eggs, demonstrating the practical application of market basket analysis in increasing sales.']}, {'end': 5837.628, 'start': 5214.461, 'title': 'Association rule mining & a priori algorithm', 'summary': 'Discusses association rule mining and a priori algorithm, explaining measures like support, confidence, and lift, and provides an example of their application to market basket analysis using transaction data.', 'duration': 623.167, 'highlights': ['Association Rule Mining is a technique that shows how items are associated to each other, measured using support, confidence, and lift. Customers who purchase bread have a 60% likelihood of also purchasing jam, and customers who purchase a laptop are more likely to purchase laptop bags. Support gives the fraction of the transaction which contains an item a and b, confidence gives how often the item A and B occur together, and lift indicates the strength of the rule over the random co-occurrence of a and b.', 'A Priori Algorithm uses frequent item sets to generate association rules based on the concept that a subset of a frequent item set must also be a frequent item set. The algorithm is used to generate association rules and is based on the concept that a subset of a frequent item set must also be a frequent item set. It involves creating item sets of different sizes, calculating support values, and generating subsets of frequent item sets.', 'Application of association rules to market basket analysis using transactional data to generate association rules for retail store transactions. The transcript discusses how association rules are used in market basket analysis problems, using online transactional data of a retail store to generate association rules. It involves importing libraries, data cleanup, and consolidating items into one transaction per row.']}], 'duration': 680.496, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A5157132.jpg', 'highlights': ['Market basket analysis uncovers item associations to increase sales.', 'Association Rule Mining measures support, confidence, and lift.', 'Market basket analysis targets customers with relevant offers to increase spending.', 'A Priori Algorithm generates association rules based on frequent item sets.', 'Association rules are applied to market basket analysis using transactional data.']}, {'end': 7080.881, 'segs': [{'end': 5859.886, 'src': 'embed', 'start': 5837.628, 'weight': 3, 'content': [{'end': 5848.277, 'text': 'so now that we have structured the data properly, so the next step is to generate the frequent item set that has support of at least 7%.', 'start': 5837.628, 'duration': 10.649}, {'end': 5851.639, 'text': 'now this number is chosen so that you can get close enough.', 'start': 5848.277, 'duration': 3.362}, {'end': 5857.124, 'text': "now what we're going to do is generate the rules with the corresponding support, confidence and lift.", 'start': 5851.639, 'duration': 5.485}, {'end': 5859.886, 'text': 'so we have given the minimum support as 0.7.', 'start': 5857.124, 'duration': 2.762}], 'summary': 'Generate frequent item set with at least 7% support and minimum support of 0.7.', 'duration': 22.258, 'max_score': 5837.628, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A5837628.jpg'}, {'end': 5911.79, 'src': 'embed', 'start': 5883.657, 'weight': 1, 'content': [{'end': 5893.085, 'text': 'If we filter the data frame using the standard Pandas code for large lift 6 and high confidence 0.8, this is what the output is going to look like.', 'start': 5883.657, 'duration': 9.428}, {'end': 5898.219, 'text': 'These are 1, 2, 3, 4, 5, 6, 7, 8.', 'start': 5893.818, 'duration': 4.401}, {'end': 5904.425, 'text': 'So as you can see here, we have the 8 rules, which are the final rules, which are given by the association rule mining.', 'start': 5898.221, 'duration': 6.204}, {'end': 5911.79, 'text': 'And this is how all the industries or any of these we talk about large retailers.', 'start': 5905.086, 'duration': 6.704}], 'summary': 'After filtering with large lift (6) and high confidence (0.8), 8 final rules are obtained using association rule mining.', 'duration': 28.133, 'max_score': 5883.657, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A5883657.jpg'}, {'end': 5985.169, 'src': 'embed', 'start': 5954.893, 'weight': 0, 'content': [{'end': 5957.034, 'text': 'So what reinforcement learning is?', 'start': 5954.893, 'duration': 2.141}, {'end': 5969.942, 'text': "it's a type of machine learning where an agent is put in an environment and it learns to behave in this environment by performing certain actions and observing the rewards which it gets from those actions.", 'start': 5957.034, 'duration': 12.908}, {'end': 5977.946, 'text': 'so reinforcement learning is all about taking an appropriate action in order to maximize the reward in the particular situation and in supervised learning.', 'start': 5970.382, 'duration': 7.564}, {'end': 5985.169, 'text': 'the training data comprises of input and the expected output, so the model is trained with the expected output itself,', 'start': 5977.946, 'duration': 7.223}], 'summary': 'Reinforcement learning involves learning through actions and rewards to maximize outcomes.', 'duration': 30.276, 'max_score': 5954.893, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A5954893.jpg'}, {'end': 6453.29, 'src': 'embed', 'start': 6431.765, 'weight': 4, 'content': [{'end': 6441.689, 'text': "So guys let's understand Q learning algorithm which is one of the most used reinforcement learning algorithm with the help of an example.", 'start': 6431.765, 'duration': 9.924}, {'end': 6448.747, 'text': 'so we have five rooms in a building connected by doors and each room is numbered from zero through four.', 'start': 6442.364, 'duration': 6.383}, {'end': 6453.29, 'text': 'the outside of the building can be thought of as one big room, which is the room number five.', 'start': 6448.747, 'duration': 4.543}], 'summary': 'Q learning is a popular reinforcement learning algorithm explained with a 5-room building example.', 'duration': 21.525, 'max_score': 6431.765, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A6431765.jpg'}, {'end': 6908.425, 'src': 'embed', 'start': 6879.62, 'weight': 2, 'content': [{'end': 6885.921, 'text': 'So if the agent learns more through further iterations, it will finally reach convergence value in Q matrix.', 'start': 6879.62, 'duration': 6.301}, {'end': 6894.422, 'text': 'So the Q matrix can then be normalized, that is, converted to percentage, by dividing all the non-zeros entities by the highest number,', 'start': 6886.661, 'duration': 7.761}, {'end': 6895.803, 'text': 'which is 500 in this case.', 'start': 6894.422, 'duration': 1.381}, {'end': 6902.824, 'text': 'So once the matrix Q gets close enough to the state of convergence, agent has learned the most optimal path to the goal state.', 'start': 6896.283, 'duration': 6.541}, {'end': 6908.425, 'text': "So what we're going to do next is divide it by 5, which is the maximum here.", 'start': 6903.304, 'duration': 5.121}], 'summary': 'Agent reaches convergence in q matrix, normalized to 500, then optimized path found', 'duration': 28.805, 'max_score': 6879.62, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A6879620.jpg'}], 'start': 5837.628, 'title': 'Association rule mining, reinforcement learning, and q learning', 'summary': 'Covers association rule mining with a support of at least 7% resulting in 8 final rules, reinforcement learning basics, exploration and exploitation, the q learning algorithm using a building with five rooms as an example, and implementing the q-learning algorithm in python over 10,000 iterations to find the optimal path.', 'chapters': [{'end': 5911.79, 'start': 5837.628, 'title': 'Association rule mining process', 'summary': 'Explains the process of generating frequent item sets with a support of at least 7%, and then deriving rules with corresponding support, confidence, and lift, ultimately resulting in 8 final rules with a high lift value and confidence of at least 0.8.', 'duration': 74.162, 'highlights': ['The process involves generating frequent item sets with a support of at least 7% and then deriving rules with corresponding support, confidence, and lift. This step ensures that the rules are based on statistically significant patterns in the data.', 'The final output consists of 8 rules, which are derived from the association rule mining process. This indicates the practical application of the mining process, resulting in actionable rules for decision-making.', 'The rules have a high lift value and confidence of at least 0.8, indicating their significance and reliability. This highlights the quality and reliability of the derived rules, ensuring their practical applicability in real-world scenarios.']}, {'end': 6207.116, 'start': 5911.79, 'title': 'Reinforcement learning basics', 'summary': 'Covers unsupervised learning, including association rule mining and clustering, and then delves into reinforcement learning, explaining its definition, components, and the concept of reward maximization with examples and formulae.', 'duration': 295.326, 'highlights': ['Reinforcement learning is a type of machine learning where an agent learns to behave in an environment by performing actions and observing rewards, with the goal of maximizing the reward in a given situation. Reinforcement learning involves an agent learning from trial and error, with no expected output, and aims to maximize rewards in a given environment.', 'Key components of reinforcement learning include the agent, environment, actions, state, reward, policy, value, and action value, each playing a crucial role in the learning process. Reinforcement learning involves various key components such as the agent, environment, actions, state, reward, policy, value, and action value, each contributing to the learning process.', 'The concept of reward maximization is central to reinforcement learning, with the agent being trained to take actions that lead to maximum rewards, and the cumulative rewards at a particular time being represented as GT equals RT plus 1, RT plus 2, and so on. Reinforcement learning focuses on reward maximization, training the agent to take actions that yield maximum rewards, with the cumulative rewards at a particular time being crucial in the learning process.', 'Discounting of rewards in reinforcement learning is achieved through the use of a discount factor, gamma, where a smaller gamma results in larger discounting, and the cumulative discounted reward is calculated using the formula GT summation of k 0 to infinity gamma to the power k RTk RT plus k plus 1. Discounting of rewards in reinforcement learning involves using a discount factor, gamma, to calculate the cumulative discounted reward, with a smaller gamma leading to larger discounting, and the specific formula GT summation of k 0 to infinity gamma to the power k RTk RT plus k plus 1 being employed.']}, {'end': 6431.765, 'start': 6207.116, 'title': 'Reinforcement learning: exploration and exploitation', 'summary': 'Discusses the concepts of exploration and exploitation in reinforcement learning, including the k-armed bandit problem, epsilon greedy algorithm, markov decision process, and an example of finding the shortest path, aiming to maximize rewards and optimize policies.', 'duration': 224.649, 'highlights': ["The agent must take up an action A to transition from the start state to end state S, receiving the reward R for each action taken, defining the policy Pi and the rewards collected define the value V. The key concept of the agent's action, transition, reward, policy, and value in reinforcement learning.", "The epsilon greedy algorithm exploits the best known option with probability 1-epsilon and exploits the worst known option with probability epsilon/2. Explanation of the epsilon greedy algorithm's approach to balancing exploration and exploitation.", 'Markov decision process (MDP) is a mathematical approach used to map a solution in reinforcement learning, involving parameters such as set of actions A, set of states S, reward R, policy pi, and value V. Introduction to Markov decision process (MDP) and its key parameters in reinforcement learning.', 'Example of finding the shortest path between nodes A and D, considering the costs represented by each edge and the traversal policy. Illustration of applying reinforcement learning concepts to find the shortest path with minimum cost.']}, {'end': 6856.373, 'start': 6431.765, 'title': 'Understanding q learning algorithm', 'summary': 'Explains the q learning algorithm using a building with five rooms as an example, associating reward values to each door, and calculating the q matrix to learn through experience, with a detailed walkthrough of the q-learning algorithm, achieving the maximum reward points.', 'duration': 424.608, 'highlights': ['The Q-learning algorithm is explained using a building with five rooms as an example, associating reward values to each door, and calculating the Q matrix to learn through experience.', "The Q matrix is calculated using the formula Q(s, a) = R(s, a) + ฮณ * max(Q(s', a')), where ฮณ is the learning parameter set to 0.8, and the initial state is room number 1.", 'The Q-learning algorithm involves nine steps, including setting the gamma parameter, initializing the Q matrix, selecting a random initial state, and updating the Q matrix based on the calculated Q values.', 'The Q-learning algorithm involves selecting a random initial state, calculating the maximum Q value for the next state based on all possible actions, and updating the Q matrix based on the calculated Q values.', 'The Python code for Q learning involves importing numpy, initializing the Q matrix, defining available actions in the state, and choosing the next action within the available actions.']}, {'end': 7080.881, 'start': 6857.02, 'title': 'Q-learning algorithm in python', 'summary': 'Explains the q-learning algorithm, updating the q matrix over 10,000 iterations, normalizing the q matrix, and finding the optimal path, resulting in the selected path of 2, 3, 1, and 5 with rewards and maximum values through reinforcement learning.', 'duration': 223.861, 'highlights': ['The Q matrix is trained over 10,000 iterations to reach convergence, with non-zero entities being normalized to percentages, resulting in the optimal path of 2, 3, 1, and 5 being selected. Q matrix training over 10,000 iterations, normalization of non-zero entities, optimal path selected', 'The Q matrix can be normalized by dividing all non-zero entities by the highest number (500), and once the matrix Q gets close enough to the state of convergence, the agent has learned the most optimal path to the goal state. Normalization of Q matrix, agent learning optimal path', "The output given by the Q learning algorithm is the selected path of 2, 3, 1, and 5, indicating the agent's successful learning of the most optimal paths. Output of Q learning algorithm, selected path indicating successful learning", 'Reinforcement learning algorithm aims to find the optimal solution using the path, action, rewards, and challenges, with the main goal being to maximize reward and value through the environment. Goal of reinforcement learning algorithm, maximizing reward and value']}], 'duration': 1243.253, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/b2q5OFtxm6A/pics/b2q5OFtxm6A5837628.jpg', 'highlights': ['Reinforcement learning involves an agent learning from trial and error, with no expected output, and aims to maximize rewards in a given environment.', 'The final output consists of 8 rules, which are derived from the association rule mining process. This indicates the practical application of the mining process, resulting in actionable rules for decision-making.', 'The Q matrix is trained over 10,000 iterations to reach convergence, with non-zero entities being normalized to percentages, resulting in the optimal path of 2, 3, 1, and 5 being selected.', 'The process involves generating frequent item sets with a support of at least 7% and then deriving rules with corresponding support, confidence, and lift. This step ensures that the rules are based on statistically significant patterns in the data.', 'The Q-learning algorithm is explained using a building with five rooms as an example, associating reward values to each door, and calculating the Q matrix to learn through experience.']}], 'highlights': ['The daily data generation has reached 2.5 quintillion bytes, emphasizing the need for machine learning in analyzing and predicting patterns in the growing data.', "35% of Amazon's revenue is generated by product recommendations.", 'Uber saw a 26% accuracy improvement in delivery and pickup with machine learning implementation.', "Python's extensive libraries and diverse application make it preferred over R for machine learning.", 'The scikit-learn library contains various supervised, unsupervised, and reinforcement learning algorithms, streamlining the process by eliminating the need to hard code any algorithm.', 'Reinforcement learning involves an agent learning from trial and error, with no expected output, and aims to maximize rewards in a given environment.', 'Market basket analysis uncovers item associations to increase sales.', 'The final output consists of 8 rules, which are derived from the association rule mining process. This indicates the practical application of the mining process, resulting in actionable rules for decision-making.']}