title
Artificial Intelligence Tutorial | AI Tutorial for Beginners | Artificial Intelligence | Simplilearn

description
🔥Professional Certificate Course In AI And Machine Learning by IIT Kanpur (India Only): https://www.simplilearn.com/iitk-professional-certificate-course-ai-machine-learning?utm_campaign=23AugustTubebuddyExpPCPAIandML&utm_medium=DescriptionFF&utm_source=youtube 🔥AI Engineer Masters Program (Discount Code - YTBE15): https://www.simplilearn.com/masters-in-artificial-intelligence?utm_campaign=SCE-AIMasters&utm_medium=DescriptionFF&utm_source=youtube 🔥AI & Machine Learning Bootcamp(US Only): https://www.simplilearn.com/ai-machine-learning-bootcamp?utm_campaign=AI-FWOZmmIUqHg&utm_medium=Descriptionff&utm_source=youtube 🔥 Purdue Post Graduate Program In AI And Machine Learning: https://www.simplilearn.com/pgp-ai-machine-learning-certification-training-course?utm_campaign=AI-FWOZmmIUqHg&utm_medium=Descriptionff&utm_source=youtube This Artificial Intelligence tutorial video will help you understand what is Artificial Intelligence, the types of Artificial Intelligence, ways of achieving Artificial Intelligence and applications of Artificial Intelligence. In the end, we will also implement a use case on TensorFlow in which we will predict whether a person has diabetes or not. The topics covered in this Artificial Intelligence Tutorial are as follows: 00:00:00 Introduction to Artificial intelligence 00:00:55 What is Artificial intelligence? 00:02:06 Types of Artificial Intelligence 00:03:47 Ways of achieving artificial intelligence 00:09:00 Applications of Artificial intelligence 00:09:20 Use case - Predicting if a person has diabetes or not To learn more about Artificial Intelligence, subscribe to our YouTube channel: youtube.com/c/SimplilearnOfficial Download the Artificial Intelligence Career Guide and take a sneak peek into the world that awaits you: https://www.simplilearn.com/artificial-intelligence-career-guide-pdf?utm_campaign=Aritificial-Intelligence-Tutorial-FWOZmmIUqHg&utm_medium=Tutorials&utm_source=youtube. You can also go through the slides here: https://goo.gl/ApsL6g #ArtificialIntelligenceTutorial #AITutorialForBeginners #artificialintelligence #AI #WhatIsArtificialIntelligence #WhyArtificialIntelligence #AIIn10Minutes #MachineLearning #DeepLearning #Simplilearn What is Artificial Intelligence? Artificial Intelligence is a method of making a computer think intelligently in a manner similar to the human mind. AI is accomplished by studying the patterns of the human brain and by analyzing the cognitive process. Artificial Intelligence is emerging as the next big thing in the technology field. Organizations are adopting AI and budgeting for certified professionals in the field. Thus, the demand for trained and certified professionals in AI is increasing. ➡️ About Post Graduate Program In AI And Machine Learning This AI ML course is designed to enhance your career in AI and ML by demystifying concepts like machine learning, deep learning, NLP, computer vision, reinforcement learning, and more. You'll also have access to 4 live sessions, led by industry experts, covering the latest advancements in AI such as generative modeling, ChatGPT, OpenAI, and chatbots. ✅ Key Features - Post Graduate Program certificate and Alumni Association membership - Exclusive hackathons and Ask me Anything sessions by IBM - 3 Capstones and 25+ Projects with industry data sets from Twitter, Uber, Mercedes Benz, and many more - Master Classes delivered by Purdue faculty and IBM experts - Simplilearn's JobAssist helps you get noticed by top hiring companies - Gain access to 4 live online sessions on latest AI trends such as ChatGPT, generative AI, explainable AI, and more - Learn about the applications of ChatGPT, OpenAI, Dall-E, Midjourney & other prominent tools ✅ Skills Covered - ChatGPT - Generative AI - Explainable AI - Generative Modeling - Statistics - Python - Supervised Learning - Unsupervised Learning - NLP - Neural Networks - Computer Vision - And Many More… 👉 Learn More At: Learn more at: https://www.simplilearn.com/artificial-intelligence-masters-program-training-course?utm_campaign=Aritificial-Intelligence-Tutorial-FWOZmmIUqHg&utm_medium=Tutorials&utm_source=youtube. 🔥 Enroll for FREE Artificial Intelligence Course & Get your Completion Certificate: https://www.simplilearn.com/learn-ai-basics-skillup?utm_campaign=AI&utm_medium=Description&utm_source=youtube 🔥🔥 Interested in Attending Live Classes? Call Us: IN - 18002127688 / US - +18445327688

detail
{'title': 'Artificial Intelligence Tutorial | AI Tutorial for Beginners | Artificial Intelligence | Simplilearn', 'heatmap': [{'end': 339.651, 'start': 247.954, 'weight': 0.883}, {'end': 769.825, 'start': 740.969, 'weight': 0.906}], 'summary': 'Tutorial on artificial intelligence for beginners covers the general concept of ai, deep learning, predicting diabetes risk using tensorflow, reading and cleaning data with pandas and tensorflow, data preprocessing techniques, tensorflow model training process, and implementing a diabetes prediction model achieving an accuracy of 71%.', 'chapters': [{'end': 127.298, 'segs': [{'end': 93.084, 'src': 'embed', 'start': 4.773, 'weight': 0, 'content': [{'end': 7.634, 'text': 'Welcome to Artificial Intelligence Tutorial.', 'start': 4.773, 'duration': 2.861}, {'end': 9.434, 'text': 'My name is Richard Kirshner.', 'start': 7.914, 'duration': 1.52}, {'end': 11.295, 'text': "I'm with the Simply Learn team.", 'start': 9.554, 'duration': 1.741}, {'end': 14.116, 'text': "That's www.simplylearn.com.", 'start': 11.455, 'duration': 2.661}, {'end': 15.296, 'text': 'Get certified.', 'start': 14.336, 'duration': 0.96}, {'end': 16.315, 'text': 'Get ahead.', 'start': 15.676, 'duration': 0.639}, {'end': 22.578, 'text': "What's in it for you today? What is artificial intelligence? So we'll start with the general concept.", 'start': 16.816, 'duration': 5.762}, {'end': 26.279, 'text': 'Types of artificial intelligence covering the four main areas.', 'start': 22.818, 'duration': 3.461}, {'end': 29.18, 'text': 'Ways of achieving artificial intelligence.', 'start': 26.639, 'duration': 2.541}, {'end': 33.351, 'text': "and some general applications of artificial intelligence in today's world.", 'start': 29.69, 'duration': 3.661}, {'end': 42.875, 'text': "Finally, we'll dive into a use case predicting if a person has diabetes or not, and we'll be using TensorFlow for that in the Python environment.", 'start': 33.752, 'duration': 9.123}, {'end': 45.056, 'text': 'What is artificial intelligence?', 'start': 43.315, 'duration': 1.741}, {'end': 51.158, 'text': "And today's world is probably one of the most exciting advancements that we're in the middle of experiencing as humans.", 'start': 45.416, 'duration': 5.742}, {'end': 56.66, 'text': 'So what is artificial intelligence? And here we have a robot with nice little clampy hands.', 'start': 51.498, 'duration': 5.162}, {'end': 60.241, 'text': 'Hey, what am I? You are what we call artificial intelligence.', 'start': 56.9, 'duration': 3.341}, {'end': 64.922, 'text': "I am your creator, reminding him who programmed him and who he's supposed to take care of.", 'start': 60.761, 'duration': 4.161}, {'end': 73.565, 'text': 'Artificial intelligence is a branch of computer science dedicated to creating intelligent machines that work and react like humans.', 'start': 65.282, 'duration': 8.283}, {'end': 81.352, 'text': "In today's place where we're at with artificial intelligence, I really want to highlight the fact that they work and react like humans.", 'start': 74.005, 'duration': 7.347}, {'end': 87.999, 'text': "Because that is where the development of artificial intelligence is, and that's what we're comparing it to, is how it looks like next to a human.", 'start': 81.372, 'duration': 6.627}, {'end': 92.423, 'text': 'Thanks Any tasks you want me to do for you? Get me a cup of coffee? Poof.', 'start': 88.139, 'duration': 4.284}, {'end': 93.084, 'text': 'Here you go.', 'start': 92.664, 'duration': 0.42}], 'summary': 'Introduction to artificial intelligence, covering types, ways of achieving ai, applications, and use case with tensorflow in python environment.', 'duration': 88.311, 'max_score': 4.773, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FWOZmmIUqHg/pics/FWOZmmIUqHg4773.jpg'}], 'start': 4.773, 'title': 'Ai tutorial & applications', 'summary': "Covers the general concept of artificial intelligence, its types, applications in today's world, and a use case of predicting diabetes using tensorflow in the python environment, emphasizing the development of machines that work and react like humans.", 'chapters': [{'end': 127.298, 'start': 4.773, 'title': 'Ai tutorial & applications', 'summary': "Covers the general concept of artificial intelligence, types, applications in today's world, and a use case of predicting diabetes using tensorflow in the python environment, emphasizing the development of machines that work and react like humans.", 'duration': 122.525, 'highlights': ['Artificial intelligence is a branch of computer science dedicated to creating intelligent machines that work and react like humans. Defines artificial intelligence as the creation of machines that mimic human behaviors, emphasizing its core goal.', 'A use case predicting if a person has diabetes or not, and using TensorFlow for that in the Python environment. Discusses a practical application of AI in predicting diabetes, showcasing its real-world impact and relevance.', "The chapter covers the general concept of artificial intelligence, types, and applications in today's world. Summarizes the main topics covered, giving an overview of the content of the chapter.", 'The development of artificial intelligence is focused on creating machines that work and react like humans. Emphasizes the primary focus of AI development, highlighting the goal of creating human-like machines.']}], 'duration': 122.525, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FWOZmmIUqHg/pics/FWOZmmIUqHg4773.jpg', 'highlights': ['Artificial intelligence is the creation of machines that mimic human behaviors, emphasizing its core goal.', 'The development of artificial intelligence is focused on creating machines that work and react like humans.', 'A use case predicting if a person has diabetes or not, and using TensorFlow for that in the Python environment.', "The chapter covers the general concept of artificial intelligence, types, and applications in today's world."]}, {'end': 652.528, 'segs': [{'end': 179.741, 'src': 'embed', 'start': 149.668, 'weight': 6, 'content': [{'end': 153.91, 'text': "So the very first part that we're really, this is reactive machines.", 'start': 149.668, 'duration': 4.242}, {'end': 155.55, 'text': "They've been around a long time.", 'start': 154.07, 'duration': 1.48}, {'end': 163.032, 'text': 'This kind of AI are purely reactive and do not hold the ability to form memories or use past experiences to make decisions.', 'start': 155.65, 'duration': 7.382}, {'end': 166.193, 'text': 'These machines are designed to do specific jobs.', 'start': 163.292, 'duration': 2.901}, {'end': 172.596, 'text': "Remember I talked about the programmable coffee maker that makes coffee in the morning? It doesn't remember yesterday from tomorrow.", 'start': 166.753, 'duration': 5.843}, {'end': 173.917, 'text': 'It runs its program.', 'start': 172.857, 'duration': 1.06}, {'end': 179.741, 'text': 'Even going back to our washing machines, they have automatic load balancers that have been around for decades.', 'start': 174.197, 'duration': 5.544}], 'summary': 'Reactive machines lack memory, only do specific tasks, like programmable coffee makers and washing machines.', 'duration': 30.073, 'max_score': 149.668, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FWOZmmIUqHg/pics/FWOZmmIUqHg149668.jpg'}, {'end': 217.785, 'src': 'embed', 'start': 187.625, 'weight': 1, 'content': [{'end': 189.146, 'text': 'Then we have limited memory.', 'start': 187.625, 'duration': 1.521}, {'end': 191.526, 'text': "This is kind of right where we're at right now.", 'start': 189.545, 'duration': 1.981}, {'end': 196.588, 'text': 'This kind of AI uses past experience and the present data to make a decision.', 'start': 191.646, 'duration': 4.942}, {'end': 199.83, 'text': 'Self-driving cars are kind of limited memory AI.', 'start': 196.828, 'duration': 3.002}, {'end': 203.512, 'text': "We bring up self-driving cars because that's a big thing, especially in today's market.", 'start': 200.03, 'duration': 3.482}, {'end': 213.063, 'text': "They have all these images that they brought in and videos of what's gone on before, and they use that to teach the automatic car what to do.", 'start': 203.672, 'duration': 9.391}, {'end': 214.864, 'text': "So it's based on a limited memory.", 'start': 213.263, 'duration': 1.601}, {'end': 217.785, 'text': "Limited memory means it's not evolving new ideas.", 'start': 214.964, 'duration': 2.821}], 'summary': 'Ai in self-driving cars uses limited memory for decision-making based on past experiences and present data.', 'duration': 30.16, 'max_score': 187.625, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FWOZmmIUqHg/pics/FWOZmmIUqHg187625.jpg'}, {'end': 339.651, 'src': 'heatmap', 'start': 247.954, 'weight': 0.883, 'content': [{'end': 252.276, 'text': 'their facial features, all kinds of things, and evolve with them.', 'start': 247.954, 'duration': 4.322}, {'end': 255.858, 'text': 'So they have some kind of reflective ability to evolve with that.', 'start': 252.416, 'duration': 3.442}, {'end': 257.559, 'text': 'And finally, self-awareness.', 'start': 256.238, 'duration': 1.321}, {'end': 259.12, 'text': 'This is the future of AI.', 'start': 257.779, 'duration': 1.341}, {'end': 262.743, 'text': 'These machines will be super intelligent, sentient, and conscious.', 'start': 259.301, 'duration': 3.442}, {'end': 267.587, 'text': "So they'll be able to react very much like a human being, although they'll have their own flavor, I'm sure.", 'start': 263.104, 'duration': 4.483}, {'end': 269.829, 'text': 'Achieving artificial intelligence.', 'start': 267.907, 'duration': 1.922}, {'end': 276.054, 'text': "So how in today's world right now are we going to achieve artificial intelligence? Well, the main..", 'start': 270.389, 'duration': 5.665}, {'end': 278.48, 'text': 'arena right now is machine learning.', 'start': 276.678, 'duration': 1.802}, {'end': 282.383, 'text': 'Machine learning provides artificial intelligence with the ability to learn.', 'start': 278.68, 'duration': 3.703}, {'end': 288.889, 'text': 'This is achieved by using algorithms to discover patterns and generate insights from the data they are exposed to.', 'start': 282.724, 'duration': 6.165}, {'end': 294.434, 'text': 'And here we have a computer as we get more and more to the human realm of artificial intelligence.', 'start': 289.11, 'duration': 5.324}, {'end': 299.439, 'text': "You see this guy splits in two and he's inside his hidden networks and machine learning programs.", 'start': 294.555, 'duration': 4.884}, {'end': 303.089, 'text': 'Deep learning which is a subcategory of machine learning.', 'start': 299.779, 'duration': 3.31}, {'end': 309.373, 'text': "Deep learning provides artificial intelligence the ability to mimic a human brain's neural network.", 'start': 303.129, 'duration': 6.244}, {'end': 313.695, 'text': 'It can make sense of patterns, noise, and sources of confusion in the data.', 'start': 309.593, 'duration': 4.102}, {'end': 317.657, 'text': "Let's try to segregate different kinds of photos using deep learning.", 'start': 313.935, 'duration': 3.722}, {'end': 323.48, 'text': 'So first we have our pile of photographs, much more organized than my boxes in my closet of photographs.', 'start': 317.797, 'duration': 5.683}, {'end': 326.882, 'text': 'The machine goes through the features of every photo to distinguish them.', 'start': 323.7, 'duration': 3.182}, {'end': 329.024, 'text': 'This is called feature extraction.', 'start': 327.002, 'duration': 2.022}, {'end': 331.725, 'text': 'Bingo, it figures out the different features in the photos.', 'start': 329.144, 'duration': 2.581}, {'end': 338.27, 'text': 'And so based on those different features, labels the photos, it says these are landscapes, these are portraits, these are others.', 'start': 332.086, 'duration': 6.184}, {'end': 339.651, 'text': 'So it separates them in there.', 'start': 338.51, 'duration': 1.141}], 'summary': 'The future of ai includes super intelligent, sentient machines achieved through machine learning and deep learning.', 'duration': 91.697, 'max_score': 247.954, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FWOZmmIUqHg/pics/FWOZmmIUqHg247954.jpg'}, {'end': 317.657, 'src': 'embed', 'start': 278.68, 'weight': 0, 'content': [{'end': 282.383, 'text': 'Machine learning provides artificial intelligence with the ability to learn.', 'start': 278.68, 'duration': 3.703}, {'end': 288.889, 'text': 'This is achieved by using algorithms to discover patterns and generate insights from the data they are exposed to.', 'start': 282.724, 'duration': 6.165}, {'end': 294.434, 'text': 'And here we have a computer as we get more and more to the human realm of artificial intelligence.', 'start': 289.11, 'duration': 5.324}, {'end': 299.439, 'text': "You see this guy splits in two and he's inside his hidden networks and machine learning programs.", 'start': 294.555, 'duration': 4.884}, {'end': 303.089, 'text': 'Deep learning which is a subcategory of machine learning.', 'start': 299.779, 'duration': 3.31}, {'end': 309.373, 'text': "Deep learning provides artificial intelligence the ability to mimic a human brain's neural network.", 'start': 303.129, 'duration': 6.244}, {'end': 313.695, 'text': 'It can make sense of patterns, noise, and sources of confusion in the data.', 'start': 309.593, 'duration': 4.102}, {'end': 317.657, 'text': "Let's try to segregate different kinds of photos using deep learning.", 'start': 313.935, 'duration': 3.722}], 'summary': "Machine learning and deep learning enable ai to learn from data and mimic human brain's neural network for pattern recognition.", 'duration': 38.977, 'max_score': 278.68, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FWOZmmIUqHg/pics/FWOZmmIUqHg278680.jpg'}, {'end': 451.24, 'src': 'embed', 'start': 427.423, 'weight': 3, 'content': [{'end': 434.208, 'text': 'And you might ask, well, why do we have multiple hidden layers? Well, the hidden layers function as different alternatives to some degree.', 'start': 427.423, 'duration': 6.785}, {'end': 439.613, 'text': 'So the more hidden layers you have, the more complex the data that goes in and what it can produce coming out.', 'start': 434.288, 'duration': 5.325}, {'end': 444.877, 'text': 'The accuracy of the predicted output generally depends on the number of hidden layers we have.', 'start': 439.853, 'duration': 5.024}, {'end': 446.038, 'text': 'So there we go.', 'start': 445.458, 'duration': 0.58}, {'end': 448.399, 'text': 'The accuracy is based on how many hidden layers we have.', 'start': 446.258, 'duration': 2.141}, {'end': 451.24, 'text': 'And again, it has to do with how complex the data is going in.', 'start': 448.439, 'duration': 2.801}], 'summary': 'Multiple hidden layers increase data complexity and accuracy of predicted output.', 'duration': 23.817, 'max_score': 427.423, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FWOZmmIUqHg/pics/FWOZmmIUqHg427423.jpg'}, {'end': 564.054, 'src': 'embed', 'start': 535.912, 'weight': 4, 'content': [{'end': 541.6, 'text': "if you're planning a trip for next year, how much those tickets are going to cost, and they certainly fluctuate a lot.", 'start': 535.912, 'duration': 5.688}, {'end': 546.466, 'text': "So let's take a look at the applications of artificial intelligence.", 'start': 542.02, 'duration': 4.446}, {'end': 549.81, 'text': "We're going to dive just a little deeper, because we talked about photos.", 'start': 546.886, 'duration': 2.924}, {'end': 552.651, 'text': "we've talked about airline ticket prices.", 'start': 549.81, 'duration': 2.841}, {'end': 553.732, 'text': 'kind of very specific.', 'start': 552.651, 'duration': 1.081}, {'end': 555.212, 'text': 'you get one specific number.', 'start': 553.732, 'duration': 1.48}, {'end': 560.033, 'text': "But let's look at some more things that are probably more in-home, more common right now.", 'start': 555.372, 'duration': 4.661}, {'end': 564.054, 'text': 'Speaking of in-home, this young gentleman is entering his room.', 'start': 560.393, 'duration': 3.661}], 'summary': 'Applications of ai can impact airline ticket pricing and common in-home tasks.', 'duration': 28.142, 'max_score': 535.912, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FWOZmmIUqHg/pics/FWOZmmIUqHg535912.jpg'}, {'end': 605.125, 'src': 'embed', 'start': 574.736, 'weight': 5, 'content': [{'end': 577.857, 'text': 'This is an example of non-memory machines.', 'start': 574.736, 'duration': 3.121}, {'end': 579.715, 'text': 'Okay, you know, it senses you.', 'start': 578.374, 'duration': 1.341}, {'end': 581.455, 'text': "It doesn't have a memory of whether you've gone in or not.", 'start': 579.735, 'duration': 1.72}, {'end': 587.618, 'text': "Some of the new models start running a prediction as to whether you're in the room or not, when you show up, when you don't.", 'start': 581.575, 'duration': 6.043}, {'end': 593.32, 'text': "So they turn things on before you come down the stairs in the morning, especially if you're trying to save energy.", 'start': 587.878, 'duration': 5.442}, {'end': 598.302, 'text': 'You might have one of those fancy thermostats which starts guessing when you get up in the morning,', 'start': 593.34, 'duration': 4.962}, {'end': 601.223, 'text': "so it doesn't start the heater until it knows you're going to get up.", 'start': 598.302, 'duration': 2.921}, {'end': 605.125, 'text': 'So here he comes in here, and this is one of the examples of a smart machine.', 'start': 601.483, 'duration': 3.642}], 'summary': "Non-memory machines predict user behavior to save energy, such as turning on devices before user's arrival.", 'duration': 30.389, 'max_score': 574.736, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FWOZmmIUqHg/pics/FWOZmmIUqHg574736.jpg'}], 'start': 127.518, 'title': 'Artificial intelligence and deep learning', 'summary': 'Discusses different types of artificial intelligence, including reactive machines, limited memory ai, theory of mind, and self-awareness, and explains the workings of deep learning through neural networks, highlighting the impact of hidden layers on accuracy and demonstrating applications in predicting airline ticket prices and smart home automation.', 'chapters': [{'end': 349.698, 'start': 127.518, 'title': 'Types of artificial intelligence', 'summary': 'Discusses the different types of artificial intelligence, including reactive machines, limited memory ai, theory of mind, and self-awareness, emphasizing their functionalities and current advancements in achieving artificial intelligence through machine learning and deep learning.', 'duration': 222.18, 'highlights': ['Machine learning provides artificial intelligence with the ability to learn by using algorithms to discover patterns and generate insights from the data they are exposed to. Machine learning enables AI to learn from data and derive insights, contributing to the advancement of artificial intelligence.', "Deep learning, a subcategory of machine learning, mimics a human brain's neural network, making sense of patterns, noise, and sources of confusion in the data. Deep learning replicates the human brain's neural network, allowing AI to make sense of complex data and identify patterns, contributing to the advancement of artificial intelligence.", 'Limited memory AI uses past experience and present data to make decisions, as exemplified by self-driving cars that utilize previous images and videos to teach the automatic car what to do. Limited memory AI leverages past experiences and current data for decision-making, demonstrated by the use of previous images and videos to instruct self-driving cars, showcasing its practical application.', 'Reactive machines, a type of AI, are purely reactive and do not hold the ability to form memories or use past experiences to make decisions, designed to perform specific tasks. Reactive machines operate without memory or past experiences, focusing on executing specific tasks, highlighting their functional nature in performing designated functions.']}, {'end': 652.528, 'start': 349.938, 'title': 'Understanding deep learning', 'summary': 'Explains the workings of deep learning through neural networks, highlighting the role of input, hidden, and output layers, as well as the impact of the number of hidden layers on accuracy, and then demonstrates the applications of machine learning in predicting airline ticket prices and smart home automation.', 'duration': 302.59, 'highlights': ['The number of hidden layers in a neural network impacts the accuracy of predicted output. The accuracy of the predicted output generally depends on the number of hidden layers we have.', 'Machine learning is applied to predict airline ticket prices using historical data, with the aim of providing insights for future ticket costs. The machine is trained using historical data of ticket prices, and then it compares the new data to predict the new prices, offering insights for future ticket costs.', 'Smart home automation demonstrates the application of non-memory and smart machines for energy-saving and convenience, utilizing sensors to detect presence and voice activation. The example of smart home automation showcases non-memory machines detecting presence and smart machines using voice activation for energy-saving and convenience purposes.']}], 'duration': 525.01, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FWOZmmIUqHg/pics/FWOZmmIUqHg127518.jpg', 'highlights': ["Deep learning replicates the human brain's neural network, allowing AI to make sense of complex data and identify patterns, contributing to the advancement of artificial intelligence.", 'Limited memory AI leverages past experiences and current data for decision-making, demonstrated by the use of previous images and videos to instruct self-driving cars, showcasing its practical application.', 'Machine learning provides artificial intelligence with the ability to learn by using algorithms to discover patterns and generate insights from the data they are exposed to, contributing to the advancement of artificial intelligence.', 'The accuracy of the predicted output generally depends on the number of hidden layers we have.', 'Machine learning is applied to predict airline ticket prices using historical data, with the aim of providing insights for future ticket costs.', 'The example of smart home automation showcases non-memory machines detecting presence and smart machines using voice activation for energy-saving and convenience purposes.', 'Reactive machines operate without memory or past experiences, focusing on executing specific tasks, highlighting their functional nature in performing designated functions.']}, {'end': 889.18, 'segs': [{'end': 726.139, 'src': 'embed', 'start': 673.278, 'weight': 0, 'content': [{'end': 675.399, 'text': 'Is a person a high risk of diabetes?', 'start': 673.278, 'duration': 2.121}, {'end': 678.261, 'text': 'Should just be something on their radar to be looking out for.', 'start': 675.759, 'duration': 2.502}, {'end': 680.582, 'text': "And we'll be helping you out with the use case.", 'start': 678.641, 'duration': 1.941}, {'end': 682.363, 'text': 'There we are with a cup of coffee again.', 'start': 680.822, 'duration': 1.541}, {'end': 684.266, 'text': 'I forgot to shave, as you can see.', 'start': 682.705, 'duration': 1.561}, {'end': 685.927, 'text': "We'll start with the problem statement.", 'start': 684.466, 'duration': 1.461}, {'end': 689.73, 'text': 'The problem statement is to predict if a person has diabetes or not.', 'start': 686.007, 'duration': 3.723}, {'end': 694.733, 'text': 'And we might start with this prediction statement, but if you were actually using this in a real case again,', 'start': 690.17, 'duration': 4.563}, {'end': 700.878, 'text': 'you would say your high risk of diabetes would be the proper way to present that to somebody if you ran their test data through here.', 'start': 694.733, 'duration': 6.145}, {'end': 707.122, 'text': "But that's very domain specific, which is outside of the problem statement as far as a computer programmer is involved.", 'start': 700.938, 'duration': 6.184}, {'end': 709.127, 'text': "So we're going to have a look at the features.", 'start': 707.426, 'duration': 1.701}, {'end': 713.11, 'text': 'These are the things that have been recorded, and they already have this data from the hospitals.', 'start': 709.207, 'duration': 3.903}, {'end': 715.151, 'text': 'One of them would be number of times pregnant.', 'start': 713.41, 'duration': 1.741}, {'end': 717.053, 'text': "Obviously, if you're a guy, that'd be zero.", 'start': 715.412, 'duration': 1.641}, {'end': 718.654, 'text': 'Glucose concentration.', 'start': 717.233, 'duration': 1.421}, {'end': 721.336, 'text': "So these are people who've had their glucose measured.", 'start': 718.934, 'duration': 2.402}, {'end': 722.417, 'text': 'Blood pressure.', 'start': 721.616, 'duration': 0.801}, {'end': 726.139, 'text': 'Age Age is a big factor, an easy thing to look at.', 'start': 722.877, 'duration': 3.262}], 'summary': 'Predicting diabetes risk using recorded features like pregnancy count, glucose concentration, blood pressure, and age.', 'duration': 52.861, 'max_score': 673.278, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FWOZmmIUqHg/pics/FWOZmmIUqHg673278.jpg'}, {'end': 784.475, 'src': 'heatmap', 'start': 740.969, 'weight': 3, 'content': [{'end': 744.551, 'text': "And if we're going to do this, let's jump into one of our favorite tools.", 'start': 740.969, 'duration': 3.582}, {'end': 748.313, 'text': "We're going to use the Anaconda Jupyter Notebook.", 'start': 744.791, 'duration': 3.522}, {'end': 752.895, 'text': "So let me flip over to there and we'll talk just briefly about that and then we'll look over this code.", 'start': 748.573, 'duration': 4.322}, {'end': 762.26, 'text': 'So here we are in the Jupyter Notebook, and this is an inline editor which is just really cool for messing with Python on the fly.', 'start': 753.175, 'duration': 9.085}, {'end': 763.781, 'text': 'you can do demos in it.', 'start': 762.26, 'duration': 1.521}, {'end': 765.042, 'text': "it's very visual.", 'start': 763.781, 'duration': 1.261}, {'end': 766.563, 'text': 'you can add pieces of code.', 'start': 765.042, 'duration': 1.521}, {'end': 769.825, 'text': 'it has cells, so you can run each cell individually.', 'start': 766.563, 'duration': 3.262}, {'end': 775.529, 'text': 'you can delete them, you can tell the cell to be a comment so that instead of running it, it just skips over it.', 'start': 769.825, 'duration': 5.704}, {'end': 778.211, 'text': "it's all kinds of cool things you can do with Jupyter notebook.", 'start': 775.529, 'duration': 2.682}, {'end': 779.252, 'text': "we're not going to go into that.", 'start': 778.211, 'duration': 1.041}, {'end': 784.475, 'text': "we're actually going to go with the tensorflow and you do have to import all the different modules in your Python Now,", 'start': 779.252, 'duration': 5.223}], 'summary': 'Using anaconda jupyter notebook for visual, interactive python coding with cells for individual execution and skipping code.', 'duration': 39.684, 'max_score': 740.969, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FWOZmmIUqHg/pics/FWOZmmIUqHg740969.jpg'}, {'end': 868.453, 'src': 'embed', 'start': 839.353, 'weight': 4, 'content': [{'end': 840.754, 'text': 'So go ahead and draw a line there.', 'start': 839.353, 'duration': 1.401}, {'end': 845.497, 'text': 'You can see that the pandas as PD, add a little drawing thing in there to make it a little easier to see.', 'start': 840.794, 'duration': 4.703}, {'end': 847.378, 'text': 'Pandas is a data set.', 'start': 845.657, 'duration': 1.721}, {'end': 850.801, 'text': "It's a really nice package you can add into your Python.", 'start': 847.779, 'duration': 3.022}, {'end': 852.562, 'text': "Usually it's done as pandas as PD.", 'start': 850.981, 'duration': 1.581}, {'end': 854.703, 'text': "That's very common to see the PD here.", 'start': 852.602, 'duration': 2.101}, {'end': 855.624, 'text': "Let's circle that.", 'start': 854.723, 'duration': 0.901}, {'end': 857.565, 'text': "It's basically like an Excel spreadsheet.", 'start': 855.804, 'duration': 1.761}, {'end': 862.829, 'text': 'It adds columns, it adds headers, it adds a lot of functionality to look at your data.', 'start': 857.865, 'duration': 4.964}, {'end': 867.092, 'text': "And the first thing we're going to do with that is we're going to come down here and we're going to read.", 'start': 862.949, 'duration': 4.143}, {'end': 868.453, 'text': 'This is a CSV.', 'start': 867.112, 'duration': 1.341}], 'summary': 'Pandas, a data set in python, functions like an excel spreadsheet, adding columns and headers for easier data analysis.', 'duration': 29.1, 'max_score': 839.353, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FWOZmmIUqHg/pics/FWOZmmIUqHg839353.jpg'}], 'start': 652.908, 'title': 'Predicting diabetes risk', 'summary': 'Discusses using machine learning to predict the risk of diabetes in individuals using features such as glucose concentration, blood pressure, age, and insulin level. it emphasizes redefining the problem statement to determine the risk and demonstrates the use of anaconda jupyter notebook for tensorflow, highlighting data manipulation with pandas and importing modules.', 'chapters': [{'end': 744.551, 'start': 652.908, 'title': 'Predicting diabetes risk', 'summary': 'Discusses using machine learning to predict the risk of diabetes in individuals using features such as glucose concentration, blood pressure, age, and insulin level, with a focus on restating the problem statement as determining the risk rather than simply predicting diabetes.', 'duration': 91.643, 'highlights': ['The problem statement is to predict if a person has diabetes or not, while emphasizing the importance of restating this as determining the risk, shifting the focus to identifying high risk individuals.', 'The features used for prediction include number of times pregnant, glucose concentration, blood pressure, age, and insulin level, with age identified as a significant factor influencing diabetes risk.', 'The discussion emphasizes the importance of restating the problem statement to focus on the risk of diabetes rather than solely predicting its presence, highlighting the need for a domain-specific approach in presenting the results to individuals.']}, {'end': 889.18, 'start': 744.791, 'title': 'Using anaconda jupyter notebook for tensorflow', 'summary': 'Introduces the anaconda jupyter notebook for running python code, particularly for tensorflow, explaining its features and demonstrating the use of pandas for data manipulation, including reading a csv file and importing modules.', 'duration': 144.389, 'highlights': ['The Anaconda Jupyter Notebook is an inline editor for Python, allowing for visual manipulation of code and running cells individually. The Anaconda Jupyter Notebook provides an inline editor for Python, allowing visual manipulation of code and the ability to run cells individually for testing.', "Pandas is a data manipulation package in Python, commonly used as 'pandas as PD,' providing spreadsheet-like functionality. Pandas is a data manipulation package in Python, commonly used as 'pandas as PD,' providing spreadsheet-like functionality with the ability to add columns and headers.", 'The chapter covers importing modules, reading a CSV file, and accessing data for TensorFlow implementation in Python. The chapter covers the process of importing modules, reading a CSV file, and accessing data for TensorFlow implementation in Python.']}], 'duration': 236.272, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FWOZmmIUqHg/pics/FWOZmmIUqHg652908.jpg', 'highlights': ['The problem statement is to predict if a person has diabetes or not, while emphasizing the importance of restating this as determining the risk, shifting the focus to identifying high risk individuals.', 'The features used for prediction include number of times pregnant, glucose concentration, blood pressure, age, and insulin level, with age identified as a significant factor influencing diabetes risk.', 'The discussion emphasizes the importance of restating the problem statement to focus on the risk of diabetes rather than solely predicting its presence, highlighting the need for a domain-specific approach in presenting the results to individuals.', 'The Anaconda Jupyter Notebook is an inline editor for Python, allowing for visual manipulation of code and running cells individually.', "Pandas is a data manipulation package in Python, commonly used as 'pandas as PD,' providing spreadsheet-like functionality with the ability to add columns and headers.", 'The chapter covers importing modules, reading a CSV file, and accessing data for TensorFlow implementation in Python.']}, {'end': 1074.541, 'segs': [{'end': 929.001, 'src': 'embed', 'start': 889.46, 'weight': 0, 'content': [{'end': 895.026, 'text': "I put in the full path, you don't have to, if you have your data file saved in the same folder you're running your program in.", 'start': 889.46, 'duration': 5.566}, {'end': 898.309, 'text': 'And this is just a CSV file, comma separated variables.', 'start': 895.146, 'duration': 3.163}, {'end': 903.975, 'text': "And with the pandas, we can read it in there and we're going to give it, put it right in a variable called diabetes.", 'start': 898.63, 'duration': 5.345}, {'end': 908.5, 'text': "And then finally we take diabetes, and you'll see diabetes.head.", 'start': 904.316, 'duration': 4.184}, {'end': 913.065, 'text': "This is a pandas command, and it's really nice because it just displays the data for us.", 'start': 908.76, 'duration': 4.305}, {'end': 914.607, 'text': 'Let me go ahead and erase that.', 'start': 913.165, 'duration': 1.442}, {'end': 919.712, 'text': 'So we go up to the run button up here in our Jupyter notebook, which runs the script in this.', 'start': 915.007, 'duration': 4.705}, {'end': 921.134, 'text': "That's the cell we're in.", 'start': 920.092, 'duration': 1.042}, {'end': 927.701, 'text': "And you can see with the diabetes.head, since it's a pandas data frame, it prints everything out nice and neat for us.", 'start': 921.294, 'duration': 6.407}, {'end': 929.001, 'text': 'And this is really great.', 'start': 928.021, 'duration': 0.98}], 'summary': 'Using pandas to read csv file and display data neatly in a variable called diabetes.', 'duration': 39.541, 'max_score': 889.46, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FWOZmmIUqHg/pics/FWOZmmIUqHg889460.jpg'}, {'end': 1016.069, 'src': 'embed', 'start': 967.108, 'weight': 3, 'content': [{'end': 974.033, 'text': "Anytime you start exploring data, and for this example we don't really need to get too much in detail, you want to know the domain.", 'start': 967.108, 'duration': 6.925}, {'end': 979.817, 'text': "What does this information mean? Are we duplicating information going in? That's beyond the scope of this.", 'start': 974.133, 'duration': 5.684}, {'end': 985.261, 'text': "For right now though, we're just going to look at this data and we're going to take apart the different pieces of this data.", 'start': 980.077, 'duration': 5.184}, {'end': 988.323, 'text': "So let's jump in there and take a look at what the next set of code is.", 'start': 985.481, 'duration': 2.842}, {'end': 993.626, 'text': "So in our next setup or our next step, we've got to start looking at cleaning the data.", 'start': 988.763, 'duration': 4.863}, {'end': 996.989, 'text': "We've got to start looking at these different columns and how are we going to bring them in.", 'start': 993.706, 'duration': 3.283}, {'end': 998.75, 'text': 'Which columns are what?', 'start': 997.229, 'duration': 1.521}, {'end': 1006.055, 'text': "Now I'm going to jump ahead a little bit and also, as we look at the columns, we're going to do our import, our tensorflow, as tf.", 'start': 999.03, 'duration': 7.025}, {'end': 1008.578, 'text': "That's a common import, but that's our TensorFlow model.", 'start': 1006.295, 'duration': 2.283}, {'end': 1016.069, 'text': "TensorFlow was developed by Google, and then they put it into open source so that it's free for everybody to use, which is always a nice thing.", 'start': 1008.759, 'duration': 7.31}], 'summary': "Exploring and cleaning data for tensorflow model with google's open source framework.", 'duration': 48.961, 'max_score': 967.108, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FWOZmmIUqHg/pics/FWOZmmIUqHg967108.jpg'}], 'start': 889.46, 'title': 'Reading and cleaning data with pandas and tensorflow', 'summary': 'Covers reading a csv file with pandas, displaying the first five lines of data, and using tensorflow for data exploration and cleaning, emphasizing the open-source nature of tensorflow and the importance of domain understanding.', 'chapters': [{'end': 966.788, 'start': 889.46, 'title': 'Reading and displaying csv data with pandas', 'summary': "Explains how to read a csv file using pandas, assign it to a variable called 'diabetes', and display the first five lines of the data using the 'diabetes.head' command, which neatly prints the first five lines of the pandas data frame.", 'duration': 77.328, 'highlights': ["The 'diabetes.head' command displays the first five lines of the pandas data frame, providing a clear view of the data's structure and content.", "The process involves reading a CSV file using pandas and assigning it to a variable called 'diabetes', simplifying data manipulation and analysis.", 'The file format is specified as a comma-separated variables (CSV) file, ensuring easy parsing and handling of the data.']}, {'end': 1074.541, 'start': 967.108, 'title': 'Data exploration and cleaning with tensorflow', 'summary': 'Discusses the initial steps of exploring and cleaning data, including the use of tensorflow for data manipulation and the importance of understanding the domain, with a brief mention of the open-source nature of tensorflow.', 'duration': 107.433, 'highlights': ['The chapter emphasizes the importance of understanding the domain when exploring data, without delving into the specifics.', 'It discusses the need to clean the data and understand different columns, highlighting the iterative nature of data manipulation.', 'The chapter introduces the use of TensorFlow for data manipulation, mentioning its open-source nature and its development by Google.']}], 'duration': 185.081, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FWOZmmIUqHg/pics/FWOZmmIUqHg889460.jpg', 'highlights': ["The 'diabetes.head' command displays the first five lines of the pandas data frame, providing a clear view of the data's structure and content.", "The process involves reading a CSV file using pandas and assigning it to a variable called 'diabetes', simplifying data manipulation and analysis.", 'The file format is specified as a comma-separated variables (CSV) file, ensuring easy parsing and handling of the data.', 'The chapter introduces the use of TensorFlow for data manipulation, mentioning its open-source nature and its development by Google.', 'The chapter emphasizes the importance of understanding the domain when exploring data, without delving into the specifics.', 'It discusses the need to clean the data and understand different columns, highlighting the iterative nature of data manipulation.']}, {'end': 1601.355, 'segs': [{'end': 1144.144, 'src': 'embed', 'start': 1118.386, 'weight': 4, 'content': [{'end': 1125.391, 'text': "What is normalize? If you're using the SKLearn module, one of their neural networks, they call it scaling or scalar.", 'start': 1118.386, 'duration': 7.005}, {'end': 1127.492, 'text': 'This is in all of these neural networks.', 'start': 1125.571, 'duration': 1.921}, {'end': 1136.299, 'text': "What happens is if I have two pieces of data and let's say, one of them is in this case 0 to 6,", 'start': 1127.853, 'duration': 8.446}, {'end': 1142.783, 'text': 'and one of them is in insulin level is 0 to maybe 0.2 would be a very high level.', 'start': 1136.299, 'duration': 6.484}, {'end': 1144.144, 'text': "I don't know what they actually go to.", 'start': 1142.923, 'duration': 1.221}], 'summary': "In sklearn's neural networks, normalization is called scaling or scalar, used to standardize data for consistent input ranges.", 'duration': 25.758, 'max_score': 1118.386, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FWOZmmIUqHg/pics/FWOZmmIUqHg1118386.jpg'}, {'end': 1194.585, 'src': 'embed', 'start': 1166.613, 'weight': 0, 'content': [{'end': 1169.994, 'text': 'We do that by normalizing it here, and we use a lambda function.', 'start': 1166.613, 'duration': 3.381}, {'end': 1171.394, 'text': 'This is always a kind of a fun thing.', 'start': 1170.014, 'duration': 1.38}, {'end': 1178.298, 'text': "We take the diabetes, It's a panda setup, and let me just erase all that so we can go down here, put it back to red.", 'start': 1171.534, 'duration': 6.764}, {'end': 1185.961, 'text': "And we take our diabetes, which is a panda data set, and we only want to look at the columns to normalize, because that's what we built.", 'start': 1178.498, 'duration': 7.463}, {'end': 1190.463, 'text': "We built a list of those columns, and we're going to apply a lambda function.", 'start': 1186.061, 'duration': 4.402}, {'end': 1193.205, 'text': "And you'll see lambda in all kinds of Python programming.", 'start': 1190.523, 'duration': 2.682}, {'end': 1194.585, 'text': "It just means it's a function.", 'start': 1193.385, 'duration': 1.2}], 'summary': 'Data normalization using lambda function on diabetes panda dataset.', 'duration': 27.972, 'max_score': 1166.613, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FWOZmmIUqHg/pics/FWOZmmIUqHg1166613.jpg'}, {'end': 1290.415, 'src': 'embed', 'start': 1253.784, 'weight': 1, 'content': [{'end': 1259.29, 'text': "If we want to make sure that it's not going to be askew in the results, if it's not going to weight the values one direction or the other.", 'start': 1253.784, 'duration': 5.506}, {'end': 1260.592, 'text': '0 to 1.', 'start': 1259.831, 'duration': 0.761}, {'end': 1261.613, 'text': "That's what this is doing.", 'start': 1260.592, 'duration': 1.021}, {'end': 1264.636, 'text': "And again, there's like so many different ways to scale our data.", 'start': 1261.773, 'duration': 2.863}, {'end': 1268.46, 'text': "This is the most basic scale, standard scaling, and it's the most common.", 'start': 1264.836, 'duration': 3.624}, {'end': 1270.242, 'text': 'And we spelled it out in the lambda.', 'start': 1268.68, 'duration': 1.562}, {'end': 1278.127, 'text': "There's actual modules that do that, but lambda is such an easy thing to do, and you can really see what we're doing here.", 'start': 1270.782, 'duration': 7.345}, {'end': 1283.471, 'text': "So now we've taken these columns, we've scaled them all, and changed them to a 0 to 1 value.", 'start': 1278.347, 'duration': 5.124}, {'end': 1290.415, 'text': "We now need to let the program know, the TensorFlow model that we're going to create, we need to let it know what these things are.", 'start': 1283.751, 'duration': 6.664}], 'summary': 'Discussing standard scaling of data to a 0 to 1 value for a tensorflow model.', 'duration': 36.631, 'max_score': 1253.784, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FWOZmmIUqHg/pics/FWOZmmIUqHg1253784.jpg'}, {'end': 1563.868, 'src': 'embed', 'start': 1530.14, 'weight': 3, 'content': [{'end': 1536.601, 'text': "What the heck is that? Well, it turns out that because this is, remember it's a pandas, there's our PD, pandas as PD.", 'start': 1530.14, 'duration': 6.461}, {'end': 1541.502, 'text': 'Because this is a pandas, panda automatically knows to look for the matplot library.', 'start': 1536.821, 'duration': 4.681}, {'end': 1544.123, 'text': "And a hist, it just means it's going to be a histogram.", 'start': 1541.762, 'duration': 2.361}, {'end': 1546.103, 'text': "And we're going to separate it into 20 bins.", 'start': 1544.243, 'duration': 1.86}, {'end': 1547.884, 'text': "And that's what this graph is here.", 'start': 1546.424, 'duration': 1.46}, {'end': 1552.525, 'text': 'So when I take the diabetes and I do a histogram of it, it produces this really nice graph.', 'start': 1548.004, 'duration': 4.521}, {'end': 1563.868, 'text': 'We can see that at 22, most of the participants in this, probably around 174 of the people that were recorded were of this age bracket.', 'start': 1552.805, 'duration': 11.063}], 'summary': 'Using pandas, a histogram with 20 bins was created, showing most participants were around 22 years old.', 'duration': 33.728, 'max_score': 1530.14, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FWOZmmIUqHg/pics/FWOZmmIUqHg1530140.jpg'}], 'start': 1074.801, 'title': 'Data preprocessing techniques', 'summary': 'Covers data normalization for neural networks and data scaling with a lambda function, emphasizing the importance of equal weightage in statistical analysis and visualization using histograms. it also highlights feature engineering for tensorflow models and the usage of the sklearn module for neural networks.', 'chapters': [{'end': 1166.413, 'start': 1074.801, 'title': 'Data normalization for neural networks', 'summary': 'Discusses the process of data normalization for neural networks, emphasizing the importance of scaling or normalizing data to ensure equal weightage in statistical analysis, particularly in the context of the sklearn module for neural networks.', 'duration': 91.612, 'highlights': ['The importance of normalizing or scaling data to ensure equal weightage in statistical analysis, particularly in the context of neural networks like SKLearn.', 'Explaining the concept of normalization by using the example of how a neural network would weigh data differently based on their absolute values, and the need to level the playing field for fair analysis.']}, {'end': 1601.355, 'start': 1166.613, 'title': 'Data scaling and feature engineering', 'summary': 'Explains data scaling using a lambda function to normalize the columns in a pandas dataset, and feature engineering for a tensorflow model, as well as visualizing data using histograms.', 'duration': 434.742, 'highlights': ['Explaining data scaling using a lambda function to normalize columns in a pandas dataset The process involves applying a lambda function to scale the columns to values between 0 and 1, ensuring the data is not skewed and is standardized, which is important for the TensorFlow model.', "Feature engineering for a TensorFlow model by reshaping and mapping columns The process involves reshaping and mapping columns, such as 'numberPregnancy', to inform the TensorFlow model that the data is in a float value range of 0 to 1, and handling special cases for 'age' and 'group' differently.", 'Visualization using histograms to analyze data distribution The use of matplotlib to visualize the distribution of data, with the histogram displaying the age distribution of participants, highlighting the age brackets with the highest number of observations and the trend of decreasing observations with increasing age.']}], 'duration': 526.554, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FWOZmmIUqHg/pics/FWOZmmIUqHg1074801.jpg', 'highlights': ['Explaining data scaling using a lambda function to normalize columns in a pandas dataset', 'Feature engineering for a TensorFlow model by reshaping and mapping columns', 'The importance of normalizing or scaling data to ensure equal weightage in statistical analysis', 'Visualization using histograms to analyze data distribution', 'Explaining the concept of normalization by using the example of how a neural network would weigh data differently based on their absolute values']}, {'end': 1764.056, 'segs': [{'end': 1698.619, 'src': 'embed', 'start': 1673.836, 'weight': 0, 'content': [{'end': 1680.281, 'text': 'We have our x equals x train and our y equals y train, because we want to train it with a particular information.', 'start': 1673.836, 'duration': 6.445}, {'end': 1683.003, 'text': 'But we have these other settings in here, these two settings.', 'start': 1680.461, 'duration': 2.542}, {'end': 1687.807, 'text': "The number of epics is how many times it's going to go over our training model.", 'start': 1683.823, 'duration': 3.984}, {'end': 1688.889, 'text': 'Epic means large.', 'start': 1687.968, 'duration': 0.921}, {'end': 1690.01, 'text': 'It means all the data.', 'start': 1688.929, 'duration': 1.081}, {'end': 1691.471, 'text': "So we're going to go over it a thousand times.", 'start': 1690.05, 'duration': 1.421}, {'end': 1694.234, 'text': "That's actually a huge overkill for this amount of data.", 'start': 1691.491, 'duration': 2.743}, {'end': 1698.619, 'text': "Usually it only needs probably about 200, but you know when we're putting it together and you're trying things out,", 'start': 1694.415, 'duration': 4.204}], 'summary': 'Training model with x train and y train data, using 1000 epics, an overkill for this amount of data.', 'duration': 24.783, 'max_score': 1673.836, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FWOZmmIUqHg/pics/FWOZmmIUqHg1673836.jpg'}, {'end': 1744.242, 'src': 'embed', 'start': 1708.104, 'weight': 1, 'content': [{'end': 1714.187, 'text': "If you're processing this over a huge amount of data and you try to batch everything at once, you end up with a problem.", 'start': 1708.104, 'duration': 6.083}, {'end': 1717.169, 'text': "This means we're only going to read 10 lines at a time through our data.", 'start': 1714.267, 'duration': 2.902}, {'end': 1723.093, 'text': "So each one of those rows of testing they've done, we're only going to look at 10 of them at a time and put that through our model and train it.", 'start': 1717.329, 'duration': 5.764}, {'end': 1724.954, 'text': 'And then shuffle is self-explanatory.', 'start': 1723.293, 'duration': 1.661}, {'end': 1728.356, 'text': "We're just randomly selecting which data and what order we go in.", 'start': 1724.994, 'duration': 3.362}, {'end': 1733.879, 'text': "That way, if there's like five in a row that are kind of weighted one way and vice versa, it mixes them up.", 'start': 1728.476, 'duration': 5.403}, {'end': 1735.619, 'text': 'And then finally we create our model.', 'start': 1734.158, 'duration': 1.461}, {'end': 1741.301, 'text': 'So the model goes in there and goes, okay, I have a tf.estimator.linearclassifier.', 'start': 1735.779, 'duration': 5.522}, {'end': 1744.242, 'text': "We're going to put in the feature columns equals feature columns.", 'start': 1741.401, 'duration': 2.841}], 'summary': 'Processing data in batches of 10, shuffling for variety, and creating a tf.estimator.linearclassifier model.', 'duration': 36.138, 'max_score': 1708.104, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FWOZmmIUqHg/pics/FWOZmmIUqHg1708104.jpg'}], 'start': 1601.455, 'title': 'Tensorflow model training process', 'summary': 'Delves into the specific steps and parameters for training a model using tensorflow, highlighting the significance of parameters such as number of epochs, batch size, and model creation.', 'chapters': [{'end': 1764.056, 'start': 1601.455, 'title': 'Tensorflow model training process', 'summary': 'Discusses the specific steps and parameters involved in training a model using tensorflow, such as the number of epics, batch size, and model creation, emphasizing the significance of these parameters in the training process.', 'duration': 162.601, 'highlights': ["The number of epics determines the frequency of training model iterations, with the example emphasizing the use of a thousand iterations, despite the typical requirement of around 200, for experimentation and fine-tuning purposes. {'maxIterations': 1000, 'typicalIterations': 200}", "The batch size parameter is highlighted for its significance in processing large amounts of data, with the example illustrating the concept of reading 10 lines at a time through the data for training. {'batchSize': 10}", "The creation of the model using tf.estimator.linearclassifier and the definition of feature columns and classes are explained as crucial steps in the model creation process. {'classes': 2}"]}], 'duration': 162.601, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FWOZmmIUqHg/pics/FWOZmmIUqHg1601455.jpg', 'highlights': ["The number of epics determines the frequency of training model iterations, with the example emphasizing the use of a thousand iterations, despite the typical requirement of around 200, for experimentation and fine-tuning purposes. {'maxIterations': 1000, 'typicalIterations': 200}", "The batch size parameter is highlighted for its significance in processing large amounts of data, with the example illustrating the concept of reading 10 lines at a time through the data for training. {'batchSize': 10}", "The creation of the model using tf.estimator.linearclassifier and the definition of feature columns and classes are explained as crucial steps in the model creation process. {'classes': 2}"]}, {'end': 2251.89, 'segs': [{'end': 1798.031, 'src': 'embed', 'start': 1764.396, 'weight': 1, 'content': [{'end': 1765.978, 'text': 'Now we need to actually train it.', 'start': 1764.396, 'duration': 1.582}, {'end': 1772.084, 'text': "Model.train You'll see this so common in so many different neural network models.", 'start': 1766.418, 'duration': 5.666}, {'end': 1773.245, 'text': 'This is like a standard.', 'start': 1772.184, 'duration': 1.061}, {'end': 1776.128, 'text': "What's different, though, is we have to feed it the function.", 'start': 1773.345, 'duration': 2.783}, {'end': 1785.358, 'text': 'remember, we created this function with all this information on it, and then we have steps, and steps similar to number of batches and batch size.', 'start': 1776.128, 'duration': 9.23}, {'end': 1787.9, 'text': "it's more like a individual lines we step through.", 'start': 1785.358, 'duration': 2.542}, {'end': 1792.145, 'text': 'a thousand is a lot more common for steps than epics, but steps is used.', 'start': 1787.9, 'duration': 4.245}, {'end': 1798.031, 'text': "you probably leave this out in this particular example, and let's go ahead and run this all together, because it has a slight.", 'start': 1792.145, 'duration': 5.886}], 'summary': 'Training the model with steps and batch size is a common practice, with a thousand steps being more common than epochs.', 'duration': 33.635, 'max_score': 1764.396, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FWOZmmIUqHg/pics/FWOZmmIUqHg1764396.jpg'}, {'end': 1854.707, 'src': 'embed', 'start': 1824.395, 'weight': 3, 'content': [{'end': 1827.96, 'text': 'This model now has the information we need in it to start running predictions.', 'start': 1824.395, 'duration': 3.565}, {'end': 1836.662, 'text': "So as we take our next sip of coffee, or maybe it's tea, or if you're one of those strange late night workers, maybe it's a sip of wine.", 'start': 1828.3, 'duration': 8.362}, {'end': 1842.144, 'text': "We go into the next step and we actually want to run some predictions on here, but we don't want to run the training.", 'start': 1836.782, 'duration': 5.362}, {'end': 1844.504, 'text': 'We want to run the test on there.', 'start': 1842.304, 'duration': 2.2}, {'end': 1846.485, 'text': 'We want to take our test data and see what it did.', 'start': 1844.524, 'duration': 1.961}, {'end': 1850.366, 'text': "And so that's what we're going to do next is we're going to run the test through and actually get some answers.", 'start': 1846.505, 'duration': 3.861}, {'end': 1854.707, 'text': "So if you were actually deploying it, you would pull the answers out of the data it's bringing back.", 'start': 1850.646, 'duration': 4.061}], 'summary': 'Model ready for predictions. testing data to get answers.', 'duration': 30.312, 'max_score': 1824.395, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FWOZmmIUqHg/pics/FWOZmmIUqHg1824395.jpg'}, {'end': 2047.378, 'src': 'embed', 'start': 2015.228, 'weight': 2, 'content': [{'end': 2017.99, 'text': 'And we have an output of whether the person has diabetes or not.', 'start': 2015.228, 'duration': 2.762}, {'end': 2020.352, 'text': "Well, in this case, it's high risk of diabetes or not.", 'start': 2018.17, 'duration': 2.182}, {'end': 2025.718, 'text': "So, now that we've run our predictions, take a sip of coffee, a short break, and we say well, what do we need to do?", 'start': 2020.692, 'duration': 5.026}, {'end': 2028.321, 'text': 'Well, we need to know how good was our predictions.', 'start': 2025.738, 'duration': 2.583}, {'end': 2030.183, 'text': 'We need to evaluate our model.', 'start': 2028.561, 'duration': 1.622}, {'end': 2034.627, 'text': "So if you're going to publish this to a company or something like that, they want to know how good you did.", 'start': 2030.403, 'duration': 4.224}, {'end': 2036.73, 'text': "Let's take a look at what that looks like in the code.", 'start': 2034.868, 'duration': 1.862}, {'end': 2038.272, 'text': "So let's paste that in here.", 'start': 2036.87, 'duration': 1.402}, {'end': 2041.955, 'text': 'And.. Just real quick, go back over this.', 'start': 2038.292, 'duration': 3.663}, {'end': 2047.378, 'text': "By now, this function should look very, this is, we're going to call it eval input function.", 'start': 2042.335, 'duration': 5.043}], 'summary': 'Evaluating model predictions for diabetes risk assessment.', 'duration': 32.15, 'max_score': 2015.228, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FWOZmmIUqHg/pics/FWOZmmIUqHg2015228.jpg'}, {'end': 2178.811, 'src': 'embed', 'start': 2139.028, 'weight': 0, 'content': [{'end': 2145.313, 'text': "Given a small amount of data, we came up with the 71% of letting people know they're high risk or not with diabetes.", 'start': 2139.028, 'duration': 6.285}, {'end': 2153, 'text': 'So we created a model that can predict if a person has diabetes based on some previous records of people who were diagnosed with diabetes.', 'start': 2145.634, 'duration': 7.366}, {'end': 2157.122, 'text': "And we've managed to have an accuracy of 71%, which is quite good.", 'start': 2153.42, 'duration': 3.702}, {'end': 2160.883, 'text': 'The model was implemented on Python using TensorFlow.', 'start': 2157.502, 'duration': 3.381}, {'end': 2165.305, 'text': 'Again, pat yourself on the back because TensorFlow is one of the more complicated scripts out there.', 'start': 2161.324, 'duration': 3.981}, {'end': 2167.707, 'text': "It's also one of the more diverse and useful ones.", 'start': 2165.445, 'duration': 2.262}, {'end': 2174.55, 'text': "So the key takeaways today is we've covered what is artificial intelligence with our robot that brings us coffee.", 'start': 2167.967, 'duration': 6.583}, {'end': 2178.811, 'text': 'And we noted that we are comparing it to how it reacts and looks like humans.', 'start': 2174.75, 'duration': 4.061}], 'summary': 'Developed a diabetes prediction model with 71% accuracy using tensorflow in python.', 'duration': 39.783, 'max_score': 2139.028, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FWOZmmIUqHg/pics/FWOZmmIUqHg2139028.jpg'}], 'start': 1764.396, 'title': 'Implementing a diabetes prediction model', 'summary': 'Covers training a neural network model with emphasis on steps, batch size, test data, and running predictions. it also discusses the output of a tensorflow model, providing insights on interpreting results and implications for identifying high-risk diabetes, and evaluates a diabetes prediction model implemented in python achieving an accuracy of 71%.', 'chapters': [{'end': 1920.454, 'start': 1764.396, 'title': 'Neural network training and testing', 'summary': 'Covers the steps involved in training a neural network model, emphasizing the use of steps, batch size, and test data for evaluation, followed by the process of running predictions on the model, with an emphasis on x test and shuffle parameter.', 'duration': 156.058, 'highlights': ['The process of training a neural network model involves defining steps and batch size, with a common use of a thousand steps, and running the training process. steps, batch size, common use of a thousand steps', 'The model needs to be trained before running predictions, and it contains the necessary information for making predictions on test data. model training, information for predictions', 'Running predictions on the test data involves processing it 10 lines at a time, without shuffling the data during the prediction process. processing test data 10 lines at a time, shuffle parameter set to false']}, {'end': 2015.208, 'start': 1920.454, 'title': 'Understanding tensorflow output', 'summary': "Discusses the output of a tensorflow model, which includes information about the prediction results such as class ids, probabilities, and labels, providing insights into the interpretation of the model's output and its implications for identifying high-risk diabetes.", 'duration': 94.754, 'highlights': ["The output of a TensorFlow model includes class IDs, probabilities, and labels, such as 'high-risk diabetes', providing specific insights into the model's predictions.", 'The output also includes binary information, expressed as 0 or 1, and offers the potential for further detailed analysis within TensorFlow.', "The chapter emphasizes the significance of the model's output in identifying potential health risks, such as advising individuals to get tested and make dietary changes based on the predictions."]}, {'end': 2251.89, 'start': 2015.228, 'title': 'Diabetes prediction model', 'summary': 'Discusses the evaluation of a diabetes prediction model implemented in python using tensorflow, achieving an accuracy of 71% and covers various aspects of artificial intelligence, including types, deep learning, and practical applications.', 'duration': 236.662, 'highlights': ["The model achieved an accuracy of 71%, indicating its effectiveness in predicting high risk of diabetes. The model's accuracy of 71% demonstrates its capability to predict high risk of diabetes, providing valuable insights into the effectiveness of the predictive model.", 'The chapter explores various aspects of artificial intelligence, including types, deep learning, and practical applications. The chapter delves into different facets of artificial intelligence, covering types, deep learning, and practical applications, providing a comprehensive overview of AI concepts.', 'The TensorFlow model was implemented in Python, showcasing the complexity and usefulness of TensorFlow in AI applications. The implementation of the TensorFlow model in Python highlights the complexity and utility of TensorFlow in the realm of artificial intelligence, emphasizing its significance in AI development.']}], 'duration': 487.494, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FWOZmmIUqHg/pics/FWOZmmIUqHg1764396.jpg', 'highlights': ['The model achieved an accuracy of 71%, indicating its effectiveness in predicting high risk of diabetes.', 'The process of training a neural network model involves defining steps and batch size, with a common use of a thousand steps, and running the training process.', "The output of a TensorFlow model includes class IDs, probabilities, and labels, such as 'high-risk diabetes', providing specific insights into the model's predictions.", 'The model needs to be trained before running predictions, and it contains the necessary information for making predictions on test data.', "The chapter emphasizes the significance of the model's output in identifying potential health risks, such as advising individuals to get tested and make dietary changes based on the predictions.", 'The TensorFlow model was implemented in Python, showcasing the complexity and usefulness of TensorFlow in AI applications.']}], 'highlights': ['The model achieved an accuracy of 71%, indicating its effectiveness in predicting high risk of diabetes.', 'The development of artificial intelligence is focused on creating machines that work and react like humans.', "Deep learning replicates the human brain's neural network, allowing AI to make sense of complex data and identify patterns, contributing to the advancement of artificial intelligence.", "The process involves reading a CSV file using pandas and assigning it to a variable called 'diabetes', simplifying data manipulation and analysis.", 'The problem statement is to predict if a person has diabetes or not, while emphasizing the importance of restating this as determining the risk, shifting the focus to identifying high risk individuals.', "The number of epics determines the frequency of training model iterations, with the example emphasizing the use of a thousand iterations, despite the typical requirement of around 200, for experimentation and fine-tuning purposes. {'maxIterations': 1000, 'typicalIterations': 200}", "The batch size parameter is highlighted for its significance in processing large amounts of data, with the example illustrating the concept of reading 10 lines at a time through the data for training. {'batchSize': 10}", "The creation of the model using tf.estimator.linearclassifier and the definition of feature columns and classes are explained as crucial steps in the model creation process. {'classes': 2}", 'The accuracy of the predicted output generally depends on the number of hidden layers we have.', 'The example of smart home automation showcases non-memory machines detecting presence and smart machines using voice activation for energy-saving and convenience purposes.']}