title
Data Science vs Machine Learning – What’s The Difference? | Data Science Course | Edureka

description
** Python Data Science Training: https://www.edureka.co/data-science-python-certification-course ** In this video on Data Science vs Machine Learning, we’ll be discussing the importance of Data Science and Machine Learning and we’ll compare them based on a few key parameters. The following topics are covered in this session: (00:47)What Is Data Science? (02:32)What Is Machine Learning? (04:06)Fields Of Data Science (05:32)Use Case Python Training Playlist: https://goo.gl/Na1p9G Python Blog Series: https://bit.ly/2RVzcVE PG in Artificial Intelligence and Machine Learning with NIT Warangal : https://www.edureka.co/post-graduate/machine-learning-and-ai Post Graduate Certification in Data Science with IIT Guwahati - https://www.edureka.co/post-graduate/data-science-program (450+ Hrs || 9 Months || 20+ Projects & 100+ Case studies) - - - - - - - - - - - - - - - - - Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain - - - - - - - - - - - - - - - - - #edureka #datascience #machinelearning #datasciencevsmachinelearning How it Works? 1. This is a 5 Week Instructor led Online Course,40 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training, you will be working on a real-time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course Edureka’s Data Science Course on Python helps you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes and Q-Learning. Throughout the Data Science Certification Course, you’ll be solving real life case studies on Media, Healthcare, Social Media, Aviation, HR. During our Python Certification Training, our instructors will help you to: 1. Master the basic and advanced concepts of Python 2. Gain insight into the 'Roles' played by a Machine Learning Engineer 3. Automate data analysis using python 4. Gain expertise in machine learning using Python and build a Real Life Machine Learning application 5. Understand the supervised and unsupervised learning and concepts of Scikit-Learn 6. Explain Time Series and it’s related concepts 7. Perform Text Mining and Sentimental analysis 8. Gain expertise to handle business in the future, living the present 9. Work on a Real Life Project on Big Data Analytics using Python and gain Hands-on Project Experience - - - - - - - - - - - - - - - - - - - Why learn Python? Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built-in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and Dot NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license. Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain. For online Data Science training, please write back to us at sales@edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll-free) for more information.

detail
{'title': 'Data Science vs Machine Learning – What’s The Difference? | Data Science Course | Edureka', 'heatmap': [{'end': 185.677, 'start': 148.314, 'weight': 0.778}, {'end': 310.793, 'start': 267.289, 'weight': 0.843}, {'end': 487.366, 'start': 475.063, 'weight': 0.703}, {'end': 674.03, 'start': 649.383, 'weight': 0.703}, {'end': 707.397, 'start': 674.671, 'weight': 0.81}, {'end': 789.841, 'start': 755.7, 'weight': 0.721}], 'summary': 'Discusses the emergence of data science, exponential data growth, and its role in making smarter business decisions, introduces machine learning as a process of teaching machines using data, and covers data processing, exploration, and the machine learning process, emphasizing the importance of data in driving business growth and providing insights for customers, and discussing the use of machine learning in building recommendation engines.', 'chapters': [{'end': 158.885, 'segs': [{'end': 158.885, 'src': 'embed', 'start': 87.827, 'weight': 0, 'content': [{'end': 95.931, 'text': 'it is estimated that by 2020, 1.7 mb of data will be created every second for every person on earth.', 'start': 87.827, 'duration': 8.104}, {'end': 98.392, 'text': 'can you imagine how much data that is?', 'start': 95.931, 'duration': 2.461}, {'end': 100.873, 'text': 'how are we going to process this much data?', 'start': 98.392, 'duration': 2.481}, {'end': 105.415, 'text': 'not only that, the data generated these days is mostly unstructured or semi-structured.', 'start': 100.873, 'duration': 4.542}, {'end': 110.464, 'text': 'So, in order to process such data, we need more complex and effective algorithms.', 'start': 105.962, 'duration': 4.502}, {'end': 112.505, 'text': 'We need a more advanced field.', 'start': 110.824, 'duration': 1.681}, {'end': 115.006, 'text': 'This is exactly where data science comes in.', 'start': 112.925, 'duration': 2.081}, {'end': 124.589, 'text': 'Data science is all about uncovering findings from data by exploring data at a granular level to mine and understand complex behaviors,', 'start': 115.546, 'duration': 9.043}, {'end': 126.39, 'text': 'trends and inferences in the data.', 'start': 124.589, 'duration': 1.801}, {'end': 132.606, 'text': "It's about surfacing hidden insights that can help enable companies to make smarter business decisions.", 'start': 127.043, 'duration': 5.563}, {'end': 136.288, 'text': "For example, I'm sure all of you have binge watched on Netflix.", 'start': 133.026, 'duration': 3.262}, {'end': 143.051, 'text': 'Now Netflix data mines movie viewing patterns of its users to understand what drives user interest.', 'start': 136.688, 'duration': 6.363}, {'end': 147.754, 'text': 'And then it uses this data to make decisions on which Netflix series to produce.', 'start': 143.492, 'duration': 4.262}, {'end': 155.802, 'text': 'Similarly, there is target now target identifies each customer shopping behaviors by drawing out patterns from their database.', 'start': 148.314, 'duration': 7.488}, {'end': 158.885, 'text': 'All right, this helps them make better marketing decisions.', 'start': 156.022, 'duration': 2.863}], 'summary': 'By 2020, 1.7mb data created/sec/person. data science uncovers insights for smarter decisions.', 'duration': 71.058, 'max_score': 87.827, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0cSFjaXMHpM/pics/0cSFjaXMHpM87827.jpg'}], 'start': 11.522, 'title': 'Data science vs machine learning', 'summary': 'Discusses the emergence of data science, exponential data growth, and the role of data science in making smarter business decisions.', 'chapters': [{'end': 158.885, 'start': 11.522, 'title': 'Data science vs machine learning', 'summary': 'Discusses the emergence of data science, the exponential growth of data, and its impact on the need for advanced algorithms and the role of data science in uncovering hidden insights to make smarter business decisions.', 'duration': 147.363, 'highlights': ['Over 2.5 quintillion bytes of data is created every single day, and by 2020, 1.7 mb of data will be created every second for every person on earth. The exponential growth of data is quantified, with over 2.5 quintillion bytes created daily and the projection of 1.7 mb of data generated every second for every person on earth by 2020.', "Data science is all about uncovering findings from data by exploring data at a granular level to mine and understand complex behaviors, trends and inferences in the data. Data science's focus on uncovering insights from data at a granular level to understand complex behaviors, trends, and inferences is emphasized.", 'Netflix data mines movie viewing patterns of its users to understand what drives user interest and make decisions on which Netflix series to produce. The use case of Netflix data mining to understand user interests and make decisions on producing Netflix series is highlighted.', 'Target identifies customer shopping behaviors by drawing out patterns from their database, aiding in making better marketing decisions. The role of data science in helping Target identify customer shopping behaviors and make better marketing decisions through database pattern analysis is emphasized.']}], 'duration': 147.363, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0cSFjaXMHpM/pics/0cSFjaXMHpM11522.jpg', 'highlights': ['The exponential growth of data is quantified, with over 2.5 quintillion bytes created daily and the projection of 1.7 mb of data generated every second for every person on earth by 2020.', "Data science's focus on uncovering insights from data at a granular level to understand complex behaviors, trends, and inferences is emphasized.", 'The use case of Netflix data mining to understand user interests and make decisions on producing Netflix series is highlighted.', 'The role of data science in helping Target identify customer shopping behaviors and make better marketing decisions through database pattern analysis is emphasized.']}, {'end': 524.897, 'segs': [{'end': 219.123, 'src': 'embed', 'start': 180.474, 'weight': 0, 'content': [{'end': 185.677, 'text': "Let's say that you've enrolled for some skating classes and you have no prior experience of skating.", 'start': 180.474, 'duration': 5.203}, {'end': 190.759, 'text': "So initially, you'd be pretty bad at it because you have no idea about how to skate.", 'start': 186.337, 'duration': 4.422}, {'end': 195.104, 'text': 'But as you observe and pick up more information, you get better at it.', 'start': 191.303, 'duration': 3.801}, {'end': 198.424, 'text': 'Observing is just another way of collecting data.', 'start': 195.704, 'duration': 2.72}, {'end': 203.205, 'text': 'Just like how we humans learn from our observations and our experiences,', 'start': 198.904, 'duration': 4.301}, {'end': 208.086, 'text': 'machines are also capable of learning on their own when they are fed a good amount of data.', 'start': 203.205, 'duration': 4.881}, {'end': 210.487, 'text': 'This is exactly how machine learning works.', 'start': 208.466, 'duration': 2.021}, {'end': 218.168, 'text': "It's the process of getting machines to automatically learn and improve from experience without being explicitly programmed.", 'start': 210.907, 'duration': 7.261}, {'end': 219.123, 'text': 'So guys,', 'start': 218.743, 'duration': 0.38}], 'summary': 'Machine learning enables machines to learn from data and improve autonomously.', 'duration': 38.649, 'max_score': 180.474, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0cSFjaXMHpM/pics/0cSFjaXMHpM180474.jpg'}, {'end': 320.422, 'src': 'heatmap', 'start': 263.126, 'weight': 1, 'content': [{'end': 266.869, 'text': "let's try to understand the different fields which are covered under data science.", 'start': 263.126, 'duration': 3.743}, {'end': 274.015, 'text': 'Now data science covers a wide spectrum of domains including artificial intelligence, machine learning and deep learning.', 'start': 267.289, 'duration': 6.726}, {'end': 283.099, 'text': 'Data science uses various AI machine learning and deep learning methodologies in order to analyze data and then extract useful insights from it.', 'start': 274.511, 'duration': 8.588}, {'end': 286.242, 'text': 'To make things more clear, let me define these terms for you.', 'start': 283.479, 'duration': 2.763}, {'end': 293.649, 'text': 'So artificial intelligence is basically a subset of data science, which lets machines to stimulate human like behavior.', 'start': 286.702, 'duration': 6.947}, {'end': 296.131, 'text': 'Okay, so they try to mimic human like behavior.', 'start': 294.009, 'duration': 2.122}, {'end': 298.273, 'text': 'Machine learning, on the other hand,', 'start': 296.73, 'duration': 1.543}, {'end': 310.793, 'text': 'is a subfield of artificial intelligence which provides machines the ability to learn automatically and then improve from experiences without being explicitly programmed or without any human intervention.', 'start': 298.273, 'duration': 12.52}, {'end': 320.422, 'text': 'Now, deep learning is a part of machine learning that uses various computational measures and algorithms inspired by the structure and function of the brain,', 'start': 311.354, 'duration': 9.068}], 'summary': 'Data science encompasses ai, ml, and deep learning for analyzing and extracting insights from data.', 'duration': 57.296, 'max_score': 263.126, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0cSFjaXMHpM/pics/0cSFjaXMHpM263126.jpg'}, {'end': 432.359, 'src': 'embed', 'start': 404.682, 'weight': 2, 'content': [{'end': 413.287, 'text': 'Now a recommendation system basically filters down a list of choices for each user, based on their browsing history, based on their ratings,', 'start': 404.682, 'duration': 8.605}, {'end': 418.01, 'text': 'based on their profile details, transactional details, card details, and so on.', 'start': 413.287, 'duration': 4.723}, {'end': 423.313, 'text': "Now such a system is used to get useful insights about customers' shopping patterns.", 'start': 418.53, 'duration': 4.783}, {'end': 432.359, 'text': 'It provides every user a particular view of the e-commerce website based on their profile and it allows them to select relevant products.', 'start': 423.914, 'duration': 8.445}], 'summary': 'Recommendation system personalizes user experience based on browsing history, ratings, and profile details.', 'duration': 27.677, 'max_score': 404.682, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0cSFjaXMHpM/pics/0cSFjaXMHpM404682.jpg'}, {'end': 500.069, 'src': 'heatmap', 'start': 475.063, 'weight': 0.703, 'content': [{'end': 483.765, 'text': 'In our case, the objective is to build a recommendation engine that will suggest relevant items to each customer based on the data generated by them.', 'start': 475.063, 'duration': 8.702}, {'end': 487.366, 'text': 'Now the second stage is data acquisition or data gathering.', 'start': 484.125, 'duration': 3.241}, {'end': 492.287, 'text': "So after you define the objectives of your project, it's time to start gathering the data.", 'start': 487.726, 'duration': 4.561}, {'end': 495.068, 'text': 'This process is also known as data mining.', 'start': 492.787, 'duration': 2.281}, {'end': 500.069, 'text': 'Data mining is basically the process of gathering your data from different sources.', 'start': 495.428, 'duration': 4.641}], 'summary': 'Build a recommendation engine for suggesting relevant items to customers, involving data acquisition and data mining.', 'duration': 25.006, 'max_score': 475.063, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0cSFjaXMHpM/pics/0cSFjaXMHpM475063.jpg'}], 'start': 159.465, 'title': 'Understanding machine learning & data science', 'summary': 'Introduces machine learning as a process of teaching machines using data, and its relation to data science. it emphasizes the importance of data in driving business growth and providing insights for customers, and discusses the use of machine learning in building recommendation engines.', 'chapters': [{'end': 203.205, 'start': 159.465, 'title': 'Understanding machine learning', 'summary': 'Provides an introduction to machine learning, explaining it as the process of teaching machines using data without human intervention, likening it to humans learning from observations and experiences.', 'duration': 43.74, 'highlights': ['The idea behind machine learning is teaching machines by feeding them data and letting them learn on their own, without any human intervention.', 'The analogy of learning to skate is used to illustrate how machine learning works, highlighting the process of observing, collecting data, and improving over time.']}, {'end': 524.897, 'start': 203.205, 'title': 'Understanding machine learning & data science', 'summary': 'Explains the process of machine learning, its relation to data science, and the use of machine learning in building recommendation engines, emphasizing the importance of data in driving business growth and providing insights for customers.', 'duration': 321.692, 'highlights': ['Machine learning is the process of machines automatically learning and improving from experience without being explicitly programmed. Machine learning involves machines learning and improving automatically without human intervention.', 'Data science covers a wide spectrum of domains including artificial intelligence, machine learning, and deep learning, using various methodologies to analyze and extract insights from data. Data science encompasses domains like artificial intelligence, machine learning, and deep learning, employing various methodologies for data analysis and insight extraction.', 'Recommendation systems use data from user browsing history, ratings, and profile details to provide relevant product suggestions, contributing to the understanding of customer shopping patterns. Recommendation systems utilize user data to offer relevant product suggestions, contributing to understanding customer shopping patterns.']}], 'duration': 365.432, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0cSFjaXMHpM/pics/0cSFjaXMHpM159465.jpg', 'highlights': ['The analogy of learning to skate is used to illustrate how machine learning works, highlighting the process of observing, collecting data, and improving over time.', 'Data science encompasses domains like artificial intelligence, machine learning, and deep learning, employing various methodologies for data analysis and insight extraction.', 'Recommendation systems utilize user data to offer relevant product suggestions, contributing to understanding customer shopping patterns.', 'Machine learning is the process of machines automatically learning and improving from experience without being explicitly programmed.']}, {'end': 966.842, 'segs': [{'end': 574.154, 'src': 'embed', 'start': 546.48, 'weight': 0, 'content': [{'end': 550.902, 'text': 'Alright, this is where you transform your data into the desired format so that you can read it.', 'start': 546.48, 'duration': 4.422}, {'end': 556.165, 'text': 'Now data cleaning is considered one of the most time-consuming tasks in data science.', 'start': 551.242, 'duration': 4.923}, {'end': 562.888, 'text': 'According to a recent survey, it was found out that about 50 to 80% of the time goes in data cleaning.', 'start': 556.705, 'duration': 6.183}, {'end': 569.612, 'text': "Alright, this can be a very tedious task because you don't know what are the relevant items and what are the missing values.", 'start': 562.908, 'duration': 6.704}, {'end': 574.154, 'text': 'Okay, so you have to remove all the irrelevant items or all of the inconsistent data.', 'start': 569.892, 'duration': 4.262}], 'summary': 'Data cleaning can consume 50-80% of data science time.', 'duration': 27.674, 'max_score': 546.48, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0cSFjaXMHpM/pics/0cSFjaXMHpM546480.jpg'}, {'end': 652.804, 'src': 'embed', 'start': 621.847, 'weight': 1, 'content': [{'end': 624.128, 'text': 'This is how you perform data exploration over here.', 'start': 621.847, 'duration': 2.281}, {'end': 630.73, 'text': 'You need to find patterns or behaviors of a particular customer and such information is used to grow the business.', 'start': 624.368, 'duration': 6.362}, {'end': 637.121, 'text': "So only after you know what a customer likes or dislikes, you'll be able to suggest or recommend something to them.", 'start': 631.34, 'duration': 5.781}, {'end': 641.982, 'text': 'This is exactly how data exploration works when it comes to a recommendation engine.', 'start': 637.741, 'duration': 4.241}, {'end': 648.443, 'text': "You're just going to study the shopping behavior of each customer and then try and suggest relevant items to each customer.", 'start': 642.322, 'duration': 6.121}, {'end': 652.804, 'text': "Now let's move on to the next stage, which is the data modeling stage.", 'start': 649.383, 'duration': 3.421}], 'summary': 'Data exploration identifies customer patterns to drive business growth and inform recommendation engine.', 'duration': 30.957, 'max_score': 621.847, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0cSFjaXMHpM/pics/0cSFjaXMHpM621847.jpg'}, {'end': 679.192, 'src': 'heatmap', 'start': 649.383, 'weight': 0.703, 'content': [{'end': 652.804, 'text': "Now let's move on to the next stage, which is the data modeling stage.", 'start': 649.383, 'duration': 3.421}, {'end': 655.864, 'text': "So guys, there's this one important thing I want to tell you all.", 'start': 653.344, 'duration': 2.52}, {'end': 659.925, 'text': 'There is no actual distinction between data science and machine learning.', 'start': 656.224, 'duration': 3.701}, {'end': 666.888, 'text': 'In fact, machine learning is a method which is used by data science in order to retrieve useful information.', 'start': 660.525, 'duration': 6.363}, {'end': 674.03, 'text': "Alright, so there's no actual distinction between them, but they do have different processes or they do have different steps in the processes.", 'start': 667.308, 'duration': 6.722}, {'end': 679.192, 'text': "So the next stage that I'm going to talk about in the data science life cycle is known as modeling.", 'start': 674.671, 'duration': 4.521}], 'summary': 'Data science and machine learning are intertwined, with distinct processes.', 'duration': 29.809, 'max_score': 649.383, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0cSFjaXMHpM/pics/0cSFjaXMHpM649383.jpg'}, {'end': 707.397, 'src': 'heatmap', 'start': 674.671, 'weight': 0.81, 'content': [{'end': 679.192, 'text': "So the next stage that I'm going to talk about in the data science life cycle is known as modeling.", 'start': 674.671, 'duration': 4.521}, {'end': 683.514, 'text': 'And at this data modeling stage is where you incorporate machine learning.', 'start': 679.693, 'duration': 3.821}, {'end': 688.256, 'text': 'Alright, so basically the entire data modeling stage is the machine learning process.', 'start': 683.754, 'duration': 4.502}, {'end': 691.25, 'text': "Okay, so let's look at the machine learning process.", 'start': 688.846, 'duration': 2.404}, {'end': 696.698, 'text': "So guys, the five stages that I've defined over here are basically the steps in the data modeling phase.", 'start': 692.031, 'duration': 4.667}, {'end': 700.484, 'text': 'Okay, now in the data modeling phase is where machine learning is implemented.', 'start': 697.139, 'duration': 3.345}, {'end': 703.449, 'text': "So let's look at how machine learning works, okay, step by step.", 'start': 700.725, 'duration': 2.724}, {'end': 707.397, 'text': 'So there are five distinctive stages in machine learning.', 'start': 703.996, 'duration': 3.401}], 'summary': 'Data modeling incorporates machine learning in five stages.', 'duration': 32.726, 'max_score': 674.671, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0cSFjaXMHpM/pics/0cSFjaXMHpM674671.jpg'}, {'end': 707.397, 'src': 'embed', 'start': 679.693, 'weight': 2, 'content': [{'end': 683.514, 'text': 'And at this data modeling stage is where you incorporate machine learning.', 'start': 679.693, 'duration': 3.821}, {'end': 688.256, 'text': 'Alright, so basically the entire data modeling stage is the machine learning process.', 'start': 683.754, 'duration': 4.502}, {'end': 691.25, 'text': "Okay, so let's look at the machine learning process.", 'start': 688.846, 'duration': 2.404}, {'end': 696.698, 'text': "So guys, the five stages that I've defined over here are basically the steps in the data modeling phase.", 'start': 692.031, 'duration': 4.667}, {'end': 700.484, 'text': 'Okay, now in the data modeling phase is where machine learning is implemented.', 'start': 697.139, 'duration': 3.345}, {'end': 703.449, 'text': "So let's look at how machine learning works, okay, step by step.", 'start': 700.725, 'duration': 2.724}, {'end': 707.397, 'text': 'So there are five distinctive stages in machine learning.', 'start': 703.996, 'duration': 3.401}], 'summary': 'Data modeling stage incorporates machine learning with five distinctive stages.', 'duration': 27.704, 'max_score': 679.693, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0cSFjaXMHpM/pics/0cSFjaXMHpM679693.jpg'}, {'end': 789.841, 'src': 'heatmap', 'start': 755.7, 'weight': 0.721, 'content': [{'end': 759.265, 'text': 'Now the next stage in machine learning is creating a model.', 'start': 755.7, 'duration': 3.565}, {'end': 761.688, 'text': 'Over here you perform the data splicing.', 'start': 759.725, 'duration': 1.963}, {'end': 765.733, 'text': 'Now data splicing is basically splitting the data set into two sets.', 'start': 762.028, 'duration': 3.705}, {'end': 769.678, 'text': 'One is for training your model and the other is for testing your model.', 'start': 766.354, 'duration': 3.324}, {'end': 773.503, 'text': 'After this you build the model by using the training data set.', 'start': 770.399, 'duration': 3.104}, {'end': 775.429, 'text': 'Now, how do you create these models?', 'start': 774.068, 'duration': 1.361}, {'end': 783.476, 'text': 'These models are nothing but your machine learning algorithms like k-nearest neighbor algorithm, support vector machines, linear regression and so on.', 'start': 775.649, 'duration': 7.827}, {'end': 789.841, 'text': 'So for a problem statement like a recommendation engine, you can make use of clustering and classification algorithms.', 'start': 783.916, 'duration': 5.925}], 'summary': 'In machine learning, data is split into training and testing sets, and models are created using algorithms like k-nearest neighbor and linear regression.', 'duration': 34.141, 'max_score': 755.7, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0cSFjaXMHpM/pics/0cSFjaXMHpM755700.jpg'}, {'end': 851.148, 'src': 'embed', 'start': 813.474, 'weight': 3, 'content': [{'end': 817.976, 'text': 'Also, let me tell you that a large portion of the data set is used for training,', 'start': 813.474, 'duration': 4.502}, {'end': 822.638, 'text': 'so that the model can learn to map the input to the output on a set of varied values.', 'start': 817.976, 'duration': 4.662}, {'end': 825.953, 'text': 'Next step in machine learning is the model testing.', 'start': 823.49, 'duration': 2.463}, {'end': 830.759, 'text': "Now after you've trained the model, it is then evaluated by using the testing data set.", 'start': 826.374, 'duration': 4.385}, {'end': 840.904, 'text': 'At this stage the model is fed new data points and it must predict the outcome by running the new data points on the machine learning model that was built in the earlier stage.', 'start': 831.36, 'duration': 9.544}, {'end': 844.485, 'text': 'Now the last stage is improving the efficiency of the model.', 'start': 841.444, 'duration': 3.041}, {'end': 851.148, 'text': 'So after you create the model and you evaluate it using the testing data, its accuracy is calculated.', 'start': 845.105, 'duration': 6.043}], 'summary': 'Data set is used for training model, then tested and evaluated for accuracy.', 'duration': 37.674, 'max_score': 813.474, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0cSFjaXMHpM/pics/0cSFjaXMHpM813474.jpg'}, {'end': 936.218, 'src': 'embed', 'start': 907.859, 'weight': 5, 'content': [{'end': 912.163, 'text': "Since machine learning is a part of data science, there isn't much comparison between them.", 'start': 907.859, 'duration': 4.304}, {'end': 915.426, 'text': 'They are separate cycles, but they are used together.', 'start': 912.823, 'duration': 2.603}, {'end': 924.675, 'text': 'So machine learning aids data science by providing a suite of algorithms for data modeling, for decision making, or even data preparation.', 'start': 916.006, 'duration': 8.669}, {'end': 926.475, 'text': 'okay. on the other hand,', 'start': 925.055, 'duration': 1.42}, {'end': 936.218, 'text': 'data science what it does is it stitches together a bunch of ideas or a bunch of algorithms drawn from machine learning to create a basic solution.', 'start': 926.475, 'duration': 9.743}], 'summary': 'Machine learning provides algorithms for data modeling and decision making, while data science stitches together algorithms from machine learning to create solutions.', 'duration': 28.359, 'max_score': 907.859, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0cSFjaXMHpM/pics/0cSFjaXMHpM907859.jpg'}], 'start': 525.257, 'title': 'Data processing, exploration, and machine learning process', 'summary': 'Covers data processing, including data acquisition and cleaning which takes 50-80% of the time, emphasizes the importance of data exploration, and discusses the five stages of the machine learning process, highlighting aspects such as model training, testing, and deployment.', 'chapters': [{'end': 641.982, 'start': 525.257, 'title': 'Data processing and exploration', 'summary': 'Discusses the process of data processing, which includes data acquisition, data cleaning, and data exploration, where data cleaning is identified as one of the most time-consuming tasks in data science, taking about 50 to 80% of the time, and the importance of data exploration in understanding user patterns and behaviors for recommendation engines.', 'duration': 116.725, 'highlights': ['Data cleaning is considered one of the most time-consuming tasks in data science, with about 50 to 80% of the time spent on it. The process of data cleaning is highlighted as one of the most time-consuming tasks in data science, taking about 50 to 80% of the time.', 'Data exploration is critical in understanding user patterns and behaviors for recommendation engines. The importance of data exploration in understanding user patterns and behaviors for recommendation engines is emphasized.', "Data acquisition is made easy for users as they don't have to do any extra work since they're already using the application. The ease of data acquisition for users is highlighted, as they don't have to perform any extra work due to their usage of the application."]}, {'end': 793.264, 'start': 642.322, 'title': 'Machine learning process', 'summary': 'Discusses the integration of machine learning in the data modeling stage, emphasizing the five stages in the machine learning process and the iterative nature of data cleaning.', 'duration': 150.942, 'highlights': ['The data modeling stage incorporates machine learning, which involves five distinctive stages: importing data, data cleaning, creating a model, training the model, and making predictions.', 'Data cleaning is highlighted as an iterative process due to the presence of duplicate or missing values, emphasizing the need for repetitive and iterative cleaning to prevent wrongful predictions.', 'The creation of machine learning models involves data splicing, where the data set is split into training and testing sets, and the utilization of algorithms such as k-nearest neighbor, support vector machines, and linear regression for building the models.']}, {'end': 966.842, 'start': 793.925, 'title': 'Machine learning process', 'summary': 'Explains the stages of the machine learning process, including model training, testing, and deployment, with emphasis on data splicing, accuracy improvement, and model validation.', 'duration': 172.917, 'highlights': ['During model training, a large portion of the data set is used for training to ensure the model can learn to map the input to the output on a set of varied values.', 'Model testing involves evaluating the trained model using the testing data set, where the model must predict the outcome by running new data points on the machine learning model.', 'The last stage involves deploying the model into the production environment, validating its performance, and fixing any issues that may arise.', 'Data science and machine learning are interconnected, with machine learning providing algorithms for data modeling and decision making, while data science stitches together algorithms from machine learning to create a basic solution.']}], 'duration': 441.585, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/0cSFjaXMHpM/pics/0cSFjaXMHpM525257.jpg', 'highlights': ['Data cleaning is considered one of the most time-consuming tasks in data science, taking about 50 to 80% of the time.', 'Data exploration is critical in understanding user patterns and behaviors for recommendation engines.', 'The data modeling stage incorporates machine learning, involving five distinctive stages: importing data, data cleaning, creating a model, training the model, and making predictions.', 'During model training, a large portion of the data set is used for training to ensure the model can learn to map the input to the output on a set of varied values.', 'Model testing involves evaluating the trained model using the testing data set, where the model must predict the outcome by running new data points on the machine learning model.', 'Data science and machine learning are interconnected, with machine learning providing algorithms for data modeling and decision making, while data science stitches together algorithms from machine learning to create a basic solution.']}], 'highlights': ['The exponential growth of data is quantified, with over 2.5 quintillion bytes created daily and the projection of 1.7 mb of data generated every second for every person on earth by 2020.', "Data science's focus on uncovering insights from data at a granular level to understand complex behaviors, trends, and inferences is emphasized.", 'The use case of Netflix data mining to understand user interests and make decisions on producing Netflix series is highlighted.', 'The role of data science in helping Target identify customer shopping behaviors and make better marketing decisions through database pattern analysis is emphasized.', 'The analogy of learning to skate is used to illustrate how machine learning works, highlighting the process of observing, collecting data, and improving over time.', 'Data science encompasses domains like artificial intelligence, machine learning, and deep learning, employing various methodologies for data analysis and insight extraction.', 'Recommendation systems utilize user data to offer relevant product suggestions, contributing to understanding customer shopping patterns.', 'Machine learning is the process of machines automatically learning and improving from experience without being explicitly programmed.', 'Data cleaning is considered one of the most time-consuming tasks in data science, taking about 50 to 80% of the time.', 'Data exploration is critical in understanding user patterns and behaviors for recommendation engines.', 'The data modeling stage incorporates machine learning, involving five distinctive stages: importing data, data cleaning, creating a model, training the model, and making predictions.', 'During model training, a large portion of the data set is used for training to ensure the model can learn to map the input to the output on a set of varied values.', 'Model testing involves evaluating the trained model using the testing data set, where the model must predict the outcome by running new data points on the machine learning model.', 'Data science and machine learning are interconnected, with machine learning providing algorithms for data modeling and decision making, while data science stitches together algorithms from machine learning to create a basic solution.']}