title
Iris Dataset EDA Lecture1@ Applied AI Course

description
for more details please visit the following link https://www.appliedaicourse.com/course/applied-ai-course/lessons/introduction-to-iris-dataset-and-2d-scatter-plot-1/ #ArtificialIntelligence,#MachineLearning,#DeepLearning,#DataScience,#NLP,#AI,#ML

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{'title': 'Iris Dataset EDA Lecture1@ Applied AI Course', 'heatmap': [{'end': 1291.666, 'start': 1244.335, 'weight': 0.847}], 'summary': 'Covers simple plotting tools for data analysis, introduces the iris flower dataset with 3 types of flowers and 150 rows, emphasizes the significance of exploratory data analysis, and discusses linear separability in machine learning.', 'chapters': [{'end': 106.015, 'segs': [{'end': 43.026, 'src': 'embed', 'start': 21.9, 'weight': 0, 'content': [{'end': 31.003, 'text': 'from linear algebra and other techniques, so as to understand what a data set is before we go and model and do actual machine learning.', 'start': 21.9, 'duration': 9.103}, {'end': 38.205, 'text': 'But this is extremely important stage for any given problem the first thing that we do is actually exploratory data analysis.', 'start': 31.603, 'duration': 6.602}, {'end': 43.026, 'text': 'This is like it is called exploratory because we do not know anything about the data set when we start.', 'start': 38.705, 'duration': 4.321}], 'summary': 'Exploratory data analysis is crucial for understanding a dataset before modeling, a key stage in the machine learning process.', 'duration': 21.126, 'max_score': 21.9, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FLuqwQgSBDw/pics/FLuqwQgSBDw21900.jpg'}, {'end': 92.072, 'src': 'embed', 'start': 63.117, 'weight': 1, 'content': [{'end': 67.7, 'text': 'The data set that we will use, so this is a very simple, very basic toy data set.', 'start': 63.117, 'duration': 4.583}, {'end': 71.682, 'text': "Actually, it's a real world data set, but I call it TOI because it's so simple.", 'start': 68.3, 'duration': 3.382}, {'end': 78.325, 'text': "It's used in most textbooks, in most courses to introduce the basic concepts because it's very easy to understand.", 'start': 72.242, 'duration': 6.083}, {'end': 84.788, 'text': 'You can think of it as sort of like the hello world of data science.', 'start': 78.805, 'duration': 5.983}, {'end': 87.67, 'text': 'Hello world of data science.', 'start': 86.369, 'duration': 1.301}, {'end': 92.072, 'text': 'Actually, when you learn any new programming language, you write.', 'start': 89.891, 'duration': 2.181}], 'summary': 'A simple, real-world dataset called toi is commonly used as an introduction to basic data science concepts.', 'duration': 28.955, 'max_score': 63.117, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FLuqwQgSBDw/pics/FLuqwQgSBDw63117.jpg'}], 'start': 1.848, 'title': 'Simple plotting tools for data analysis', 'summary': 'Emphasizes the significance of exploratory data analysis before modeling and introduces basic plotting techniques using the introductory example of toi dataset, commonly used in data science.', 'chapters': [{'end': 106.015, 'start': 1.848, 'title': 'Simple plotting tools for data analysis', 'summary': 'Covers the importance of exploratory data analysis in understanding a dataset before modeling, and introduces basic plotting techniques using a simple real-world dataset called toi, often used as an introductory example in data science.', 'duration': 104.167, 'highlights': ['Exploratory data analysis is crucial before modeling, as it helps in understanding the dataset, and is often the first step in any problem. Understanding the importance of exploratory data analysis as the initial step in any problem, before delving into actual modeling.', 'Introducing basic plotting techniques using a simple real-world dataset called TOI, often used as an introductory example in data science. Introduction of basic plotting techniques using a simple real-world dataset known as TOI, which is commonly used as an introductory example in data science.', "The TOI dataset is often referred to as the 'hello world' of data science, due to its simplicity and widespread use in introductory courses. Description of the TOI dataset as the 'hello world' of data science, serving as a simple and widely used example in introductory data science courses."]}], 'duration': 104.167, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FLuqwQgSBDw/pics/FLuqwQgSBDw1848.jpg', 'highlights': ['Exploratory data analysis is crucial before modeling, as it helps in understanding the dataset, and is often the first step in any problem.', "The TOI dataset is often referred to as the 'hello world' of data science, due to its simplicity and widespread use in introductory courses.", 'Introducing basic plotting techniques using a simple real-world dataset called TOI, often used as an introductory example in data science.']}, {'end': 784.862, 'segs': [{'end': 185.995, 'src': 'embed', 'start': 153.072, 'weight': 0, 'content': [{'end': 155.074, 'text': 'The first flower is called Iris setosa.', 'start': 153.072, 'duration': 2.002}, {'end': 157.856, 'text': 'The second type of flower is called Iris versicolor.', 'start': 155.554, 'duration': 2.302}, {'end': 159.698, 'text': 'The third one is called Iris virginica.', 'start': 157.916, 'duration': 1.782}, {'end': 162.06, 'text': 'All three of them belong to the Iris family.', 'start': 160.278, 'duration': 1.782}, {'end': 167.322, 'text': 'Now, if you look at it, these three flowers look more or less similar.', 'start': 162.938, 'duration': 4.384}, {'end': 171.926, 'text': 'So the task that we have, the objective that we have.', 'start': 168.784, 'duration': 3.142}, {'end': 179.353, 'text': "as I've clearly mentioned here, the objective that we have is to classify a flower as belonging to one of the three classes.", 'start': 171.926, 'duration': 7.427}, {'end': 185.995, 'text': "Right. So so, given a new flower, our task here, which is very important when we're doing data analysis,", 'start': 180.314, 'duration': 5.681}], 'summary': 'Objective: classify flowers into 3 classes - iris setosa, iris versicolor, iris virginica.', 'duration': 32.923, 'max_score': 153.072, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FLuqwQgSBDw/pics/FLuqwQgSBDw153072.jpg'}, {'end': 289.021, 'src': 'embed', 'start': 263.098, 'weight': 1, 'content': [{'end': 267.282, 'text': 'And a botanist could say OK, I measure these four variables.', 'start': 263.098, 'duration': 4.184}, {'end': 271.766, 'text': 'I measure things called sepal length, sepal width, petal length and petal width.', 'start': 267.462, 'duration': 4.304}, {'end': 276.93, 'text': 'And based on these four numbers, I know whether it is setosa, versicolor or virginica.', 'start': 272.126, 'duration': 4.804}, {'end': 285.618, 'text': "Now the task that we're trying to do is we are trying to mimic an algorithm, a machine learning algorithm, to do what the botanist knows,", 'start': 277.671, 'duration': 7.947}, {'end': 289.021, 'text': 'what the botanist has learned by studying biology of these plants.', 'start': 285.618, 'duration': 3.403}], 'summary': 'Botanist uses 4 variables to classify plants; we mimic this with machine learning.', 'duration': 25.923, 'max_score': 263.098, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FLuqwQgSBDw/pics/FLuqwQgSBDw263098.jpg'}, {'end': 347.522, 'src': 'embed', 'start': 323.527, 'weight': 2, 'content': [{'end': 332.151, 'text': 'So when a botanist is given a new flower, he would actually look at these four values and he would say whether it is virginica, versicolor, or setosa.', 'start': 323.527, 'duration': 8.624}, {'end': 336.954, 'text': 'Now, since the biologist uses these four variables of these four measurements,', 'start': 332.652, 'duration': 4.302}, {'end': 346.061, 'text': "It's possible that we could use these four measurements and build a simple algorithm which can classify a given new flower into setosa,", 'start': 337.794, 'duration': 8.267}, {'end': 347.522, 'text': 'virginica and versicolor.', 'start': 346.061, 'duration': 1.461}], 'summary': 'Using four measurements, a botanist can classify a new flower into setosa, virginica, or versicolor.', 'duration': 23.995, 'max_score': 323.527, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FLuqwQgSBDw/pics/FLuqwQgSBDw323527.jpg'}, {'end': 492.917, 'src': 'embed', 'start': 445.308, 'weight': 3, 'content': [{'end': 449.172, 'text': "So just let's go and look at various terms that are typically used.", 'start': 445.308, 'duration': 3.864}, {'end': 455.138, 'text': 'So if you go up, what we have here is it can be called a data point or a vector or an observation.', 'start': 449.553, 'duration': 5.585}, {'end': 460.1, 'text': 'So you might ask me, a vector is something that I learned in physics in my 12th grade.', 'start': 455.879, 'duration': 4.221}, {'end': 470.364, 'text': 'So how is that vector related to this? For this discussion, we can just think of a vector as nothing but n-dimensional numerical array.', 'start': 460.5, 'duration': 9.864}, {'end': 478.046, 'text': "It's the simplest way to think of it for now, right? So you can think of it as n-dimensional numerical array.", 'start': 471.064, 'duration': 6.982}, {'end': 480.966, 'text': 'So we have four variables here.', 'start': 479.204, 'duration': 1.762}, {'end': 483.308, 'text': 'So our data set is nothing but the whole table that we are given.', 'start': 481.026, 'duration': 2.282}, {'end': 488.913, 'text': 'Our features are nothing but your sepal length, sepal width, your petal length, and petal width.', 'start': 483.868, 'duration': 5.045}, {'end': 492.917, 'text': "They're also called as variables or input variables or dependent variables.", 'start': 489.493, 'duration': 3.424}], 'summary': 'Discussion on terms like data point, vector, and features with 4 variables.', 'duration': 47.609, 'max_score': 445.308, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FLuqwQgSBDw/pics/FLuqwQgSBDw445308.jpg'}, {'end': 543.666, 'src': 'embed', 'start': 518.133, 'weight': 5, 'content': [{'end': 524.236, 'text': 'And going back to our vector, our vector, of course, we will understand vectors in more mathematical detail when we learn linear algebra.', 'start': 518.133, 'duration': 6.103}, {'end': 526.958, 'text': 'But for now, we can think of a vector as an n-dimensional array.', 'start': 524.597, 'duration': 2.361}, {'end': 532.758, 'text': 'So, going back to our example, here you can think of this as so.', 'start': 527.378, 'duration': 5.38}, {'end': 537.642, 'text': 'you can think of this as a four dimensional array, all right, where?', 'start': 532.758, 'duration': 4.884}, {'end': 543.666, 'text': 'so you can think of this as an array which has four components or four dimensions, right.', 'start': 537.642, 'duration': 6.024}], 'summary': 'Introduction to vectors as n-dimensional arrays in linear algebra.', 'duration': 25.533, 'max_score': 518.133, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FLuqwQgSBDw/pics/FLuqwQgSBDw518133.jpg'}, {'end': 658.445, 'src': 'embed', 'start': 636.057, 'weight': 6, 'content': [{'end': 643.841, 'text': 'Why are we not using other very simple things like shape, size or actually, size is same as sepal lengths and petal widths.', 'start': 636.057, 'duration': 7.784}, {'end': 644.922, 'text': "Why don't we use shape??", 'start': 643.861, 'duration': 1.061}, {'end': 646.863, 'text': "Why don't we use color?", 'start': 644.982, 'duration': 1.881}, {'end': 655.085, 'text': 'So here in this case, the biologist, or the botanist in this specific case, knows what features are important.', 'start': 647.643, 'duration': 7.442}, {'end': 658.445, 'text': 'So domain knowledge is extremely important in machine learning.', 'start': 655.425, 'duration': 3.02}], 'summary': 'Domain knowledge is crucial in machine learning.', 'duration': 22.388, 'max_score': 636.057, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FLuqwQgSBDw/pics/FLuqwQgSBDw636057.jpg'}, {'end': 726.005, 'src': 'embed', 'start': 699.802, 'weight': 7, 'content': [{'end': 704.665, 'text': 'to solve a problem much better than somebody who is just doing number crunching.', 'start': 699.802, 'duration': 4.863}, {'end': 706.626, 'text': 'machine learning is not just number crunching.', 'start': 704.665, 'duration': 1.961}, {'end': 708.288, 'text': "it's about getting insights into data.", 'start': 706.626, 'duration': 1.662}, {'end': 711.601, 'text': "That's a very, very important task that we have to do.", 'start': 709.318, 'duration': 2.283}, {'end': 712.944, 'text': "So let's go and run.", 'start': 712.142, 'duration': 0.802}, {'end': 721.657, 'text': "So my first question is, in this IDIS data set, how many data points are there? So let's go through some simple code.", 'start': 713.564, 'duration': 8.093}, {'end': 722.778, 'text': "Let's be the Sherlock Holmes.", 'start': 721.757, 'duration': 1.021}, {'end': 726.005, 'text': "OK, so I'm just importing a bunch of libraries.", 'start': 723.604, 'duration': 2.401}], 'summary': 'Machine learning provides insights into data. idis dataset data points analyzed.', 'duration': 26.203, 'max_score': 699.802, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FLuqwQgSBDw/pics/FLuqwQgSBDw699802.jpg'}], 'start': 106.015, 'title': 'Iris flower classification', 'summary': 'Introduces the iris flower data set with 3 types of flowers: iris setosa, versicolor, and virginica, and explains the objective of classifying a flower into these categories based on four features: sepal length, sepal width, petal length, and petal width. it also discusses the use of four measurements to classify iris flowers into setosa, virginica, and versicolor, and explores the terms related to data points, vectors, and variables in the context of machine learning. additionally, it introduces the concept of vectors as n-dimensional arrays and emphasizes the importance of domain knowledge in machine learning, highlighting the significance of understanding the features used and the amount of data points in a dataset.', 'chapters': [{'end': 323.407, 'start': 106.015, 'title': 'Iris flower classification', 'summary': 'Introduces the iris flower data set with 3 types of flowers: iris setosa, versicolor, and virginica, and explains the objective of classifying a flower into these categories based on four features: sepal length, sepal width, petal length, and petal width.', 'duration': 217.392, 'highlights': ['The data set comprises three types of flowers: Iris setosa, versicolor, and virginica, collected in 1936. In 1936, this data set was collected from three types of flowers, all belonging to the Iris family.', 'The objective is to classify a given flower into one of the three categories based on sepal length, sepal width, petal length, and petal width. The objective is to classify a given flower into one of the three categories based on sepal length, sepal width, petal length, and petal width.', "The chapter emphasizes the importance of understanding the objective in line with data analysis and mentions the relevance of a botanist's method for classification. It is important that we do our data analysis in line with our objective, and the chapter mentions mimicking an algorithm to do what a botanist has learned by studying the biology of these plants."]}, {'end': 517.553, 'start': 323.527, 'title': 'Iris flower classification', 'summary': 'Discusses the use of four measurements to classify iris flowers into setosa, virginica, and versicolor, and explores the terms related to data points, vectors, and variables in the context of machine learning.', 'duration': 194.026, 'highlights': ['The biologist uses these four variables of these four measurements to classify a given new flower into setosa, virginica, and versicolor. The biologist uses the four measurements to classify iris flowers into setosa, virginica, and versicolor.', 'The data set contains four variables: sepal length, sepal width, petal length, and petal width, which are also referred to as features, input variables, or dependent variables. The data set contains four variables: sepal length, sepal width, petal length, and petal width, referred to as features, input variables, or dependent variables.', 'A data point or vector in this context is an n-dimensional numerical array, and the class label to be predicted is the species of the flower, also known as an output variable, class label, or response label. A data point or vector is an n-dimensional numerical array, and the class label to be predicted is the species of the flower, also known as an output variable, class label, or response label.']}, {'end': 784.862, 'start': 518.133, 'title': 'Understanding vectors and domain knowledge in machine learning', 'summary': 'Introduces the concept of vectors as n-dimensional arrays and emphasizes the importance of domain knowledge in machine learning, highlighting the significance of understanding the features used and the amount of data points in a dataset.', 'duration': 266.729, 'highlights': ['Vectors are introduced as n-dimensional arrays, simplifying the concept for beginners. Vectors are explained as n-dimensional arrays, providing a simpler understanding for beginners before delving into more mathematical detail in linear algebra.', 'Emphasizes the importance of domain knowledge in machine learning, particularly in understanding features used in datasets. The transcript emphasizes the significance of domain knowledge in machine learning, particularly in understanding the relevance and importance of features used in datasets.', "Stresses the value of analyzing the amount of data points in a dataset for investigative purposes. The chapter emphasizes the importance of analyzing the amount of data points in a dataset, highlighting the investigative aspect of understanding the dataset's size and scope."]}], 'duration': 678.847, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FLuqwQgSBDw/pics/FLuqwQgSBDw106015.jpg', 'highlights': ['The data set comprises three types of flowers: Iris setosa, versicolor, and virginica, collected in 1936.', 'The objective is to classify a given flower into one of the three categories based on sepal length, sepal width, petal length, and petal width.', 'The biologist uses the four measurements to classify iris flowers into setosa, virginica, and versicolor.', 'The data set contains four variables: sepal length, sepal width, petal length, and petal width, referred to as features, input variables, or dependent variables.', 'A data point or vector is an n-dimensional numerical array, and the class label to be predicted is the species of the flower, also known as an output variable, class label, or response label.', 'Vectors are explained as n-dimensional arrays, providing a simpler understanding for beginners before delving into more mathematical detail in linear algebra.', 'The transcript emphasizes the significance of domain knowledge in machine learning, particularly in understanding the relevance and importance of features used in datasets.', "The chapter emphasizes the importance of analyzing the amount of data points in a dataset, highlighting the investigative aspect of understanding the dataset's size and scope."]}, {'end': 1234.509, 'segs': [{'end': 810.081, 'src': 'embed', 'start': 784.962, 'weight': 0, 'content': [{'end': 791.966, 'text': 'And now my first question is how many data points and features are there? So I could just use the simple function called print iris.shape.', 'start': 784.962, 'duration': 7.004}, {'end': 796.788, 'text': 'What iris.shape will give me is it will give me the shape of the matrix or the data table.', 'start': 792.566, 'duration': 4.222}, {'end': 802.991, 'text': 'So what this says is that I have a table which is 150 rows.', 'start': 797.308, 'duration': 5.683}, {'end': 806.36, 'text': 'and five columns.', 'start': 805.2, 'duration': 1.16}, {'end': 810.081, 'text': 'So I have 150 rows and one, two.', 'start': 807.921, 'duration': 2.16}], 'summary': 'The dataset contains 150 rows and 5 columns.', 'duration': 25.119, 'max_score': 784.962, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FLuqwQgSBDw/pics/FLuqwQgSBDw784962.jpg'}, {'end': 888.534, 'src': 'embed', 'start': 859.556, 'weight': 1, 'content': [{'end': 861.576, 'text': 'So this is how my table is.', 'start': 859.556, 'duration': 2.02}, {'end': 862.417, 'text': 'And I have 150 rows.', 'start': 861.896, 'duration': 0.521}, {'end': 869.061, 'text': "Now my next question is, since I have three classes, I know this because that's what the data set has.", 'start': 864.298, 'duration': 4.763}, {'end': 871.863, 'text': 'The data set has setosa, versicolor, and virginica.', 'start': 869.181, 'duration': 2.682}, {'end': 877.426, 'text': 'I want to understand how many points are there which belong to the setosa class,', 'start': 872.263, 'duration': 5.163}, {'end': 882.049, 'text': 'how many points belong to the versicolor class and how many points belong to the virginica class.', 'start': 877.426, 'duration': 4.623}, {'end': 884.751, 'text': 'For that, all you have to do is very simple.', 'start': 882.57, 'duration': 2.181}, {'end': 886.532, 'text': "It's very, very straightforward.", 'start': 885.372, 'duration': 1.16}, {'end': 888.534, 'text': 'You just say iris species.', 'start': 887.073, 'duration': 1.461}], 'summary': 'The dataset contains 150 rows with three classes: setosa, versicolor, and virginica. the user wants to know the number of points belonging to each class.', 'duration': 28.978, 'max_score': 859.556, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FLuqwQgSBDw/pics/FLuqwQgSBDw859556.jpg'}, {'end': 1076.897, 'src': 'embed', 'start': 1029.58, 'weight': 2, 'content': [{'end': 1035.406, 'text': "I would say this is almost balanced, because it's not as severely imbalanced as 509,500..", 'start': 1029.58, 'duration': 5.826}, {'end': 1037.428, 'text': 'So this is OK.', 'start': 1035.406, 'duration': 2.022}, {'end': 1043.992, 'text': 'The reason we are specifically calling out imbalanced data sets is because when you have an imbalanced data set,', 'start': 1037.968, 'duration': 6.024}, {'end': 1048.396, 'text': 'we have to do slightly different data analysis as compared to a balanced data set.', 'start': 1043.992, 'duration': 4.404}, {'end': 1051.919, 'text': 'Slightly different, not significantly different, but slightly different.', 'start': 1048.636, 'duration': 3.283}, {'end': 1055.342, 'text': "And it's important to understand whether your data set is balanced or not.", 'start': 1052.539, 'duration': 2.803}, {'end': 1059.505, 'text': "In our case, thankfully, for the iris dataset, it's a very balanced dataset.", 'start': 1055.902, 'duration': 3.603}, {'end': 1066.53, 'text': "Now let's go and understand some very, very interesting, simple plotting tools.", 'start': 1060.265, 'duration': 6.265}, {'end': 1069.792, 'text': "So let's do something called a 2D scatter plot.", 'start': 1067.05, 'duration': 2.742}, {'end': 1073.315, 'text': 'So the code of it is very, very straightforward.', 'start': 1071.133, 'duration': 2.182}, {'end': 1075.296, 'text': 'So you just say iris.plot.', 'start': 1073.835, 'duration': 1.461}, {'end': 1076.897, 'text': "You're doing the scatter plot.", 'start': 1075.776, 'duration': 1.121}], 'summary': 'The iris dataset is balanced, enabling straightforward data analysis and plotting.', 'duration': 47.317, 'max_score': 1029.58, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FLuqwQgSBDw/pics/FLuqwQgSBDw1029579.jpg'}, {'end': 1192.081, 'src': 'embed', 'start': 1144.385, 'weight': 3, 'content': [{'end': 1151.888, 'text': "So for each flower, I take, let's assume the sepal length is 5.5 and the sepal width is 2.1.", 'start': 1144.385, 'duration': 7.503}, {'end': 1157.77, 'text': 'Then I would say, take the sepal length as 5.5 and take the sepal width as 2.1 and put a point here.', 'start': 1151.888, 'duration': 5.882}, {'end': 1160.071, 'text': "That's how I plot my scatter plot.", 'start': 1158.751, 'duration': 1.32}, {'end': 1166.282, 'text': 'It is called a scatter plot because we are scattering all of these points that we have and putting it on a map.', 'start': 1160.431, 'duration': 5.851}, {'end': 1168.303, 'text': "So it's a simple scatter plot.", 'start': 1166.962, 'duration': 1.341}, {'end': 1172.185, 'text': 'Now you might ask, OK, we are doing a scatter plot of only two dimensions.', 'start': 1168.803, 'duration': 3.382}, {'end': 1173.666, 'text': 'What about three dimensions?', 'start': 1172.646, 'duration': 1.02}, {'end': 1174.807, 'text': 'What about four dimensions?', 'start': 1173.706, 'duration': 1.101}, {'end': 1176.508, 'text': 'What about 100 dimensions?', 'start': 1175.287, 'duration': 1.221}, {'end': 1183.754, 'text': 'While in physics you might have heard about the fourth dimension as time in machine learning 100 dimensions is not unheard of.', 'start': 1177.608, 'duration': 6.146}, {'end': 1186.877, 'text': "It's very, very commonly found.", 'start': 1184.574, 'duration': 2.303}, {'end': 1192.081, 'text': "Imagine if I have 100 features, I'm operating in a 100-dimensional space.", 'start': 1187.237, 'duration': 4.844}], 'summary': 'Using scatter plots to visualize data in different dimensions, up to 100 features.', 'duration': 47.696, 'max_score': 1144.385, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FLuqwQgSBDw/pics/FLuqwQgSBDw1144385.jpg'}], 'start': 784.962, 'title': 'Exploring iris dataset', 'summary': 'Explores the iris dataset, revealing it has 150 rows and 5 columns, with 50 data points each for setosa, versicolor, and virginica classes, and discusses balanced vs imbalanced data sets with an example of a hospital dataset having 500 diabetic patients out of 10,000, highlighting the need for different data analysis approaches for imbalanced data sets, and understanding 2d scatter plots emphasizing the visualization and interpretation of the plotted data points.', 'chapters': [{'end': 903.619, 'start': 784.962, 'title': 'Exploring iris dataset', 'summary': 'Explores the iris dataset, revealing it has 150 rows and 5 columns, with 50 data points each for setosa, versicolor, and virginica classes.', 'duration': 118.657, 'highlights': ['The dataset contains 150 rows and 5 columns. The iris dataset consists of 150 rows and 5 columns, representing the shape of the matrix or data table.', 'The dataset has 50 data points for each Setosa, Versicolor, and Virginica classes. It is revealed that there are 50 data points each for Setosa, Versicolor, and Virginica classes, indicating a balanced distribution among the three classes.']}, {'end': 1051.919, 'start': 903.919, 'title': 'Balanced vs imbalanced data sets', 'summary': 'Explains the concept of balanced and imbalanced data sets, with an example of a hospital dataset having 500 diabetic patients out of 10,000, highlighting the need for different data analysis approaches for imbalanced data sets.', 'duration': 148, 'highlights': ['Imbalanced data set defined An example of a hospital dataset with 500 diabetic patients out of 10,000 is provided, illustrating the severe class imbalance and the need for different data analysis approaches.', 'Balanced data set explained A data set where 50 out of 100 data points become Versicolor is mentioned, demonstrating the concept of a balanced data set with almost equal number of data points for each class.', 'Impact of imbalanced data sets The need for slightly different data analysis approaches for imbalanced data sets compared to balanced data sets is emphasized.']}, {'end': 1234.509, 'start': 1052.539, 'title': 'Understanding 2d scatter plots', 'summary': 'Discusses the importance of balanced datasets and explains the process of creating 2d scatter plots using the iris dataset, emphasizing the visualization and interpretation of the plotted data points.', 'duration': 181.97, 'highlights': ['The iris dataset is very balanced with 150 data points and includes features such as sepal length, sepal width, petal length, and petal width. The balanced nature of the iris dataset is emphasized, and the specific features included are highlighted.', 'The process of creating a 2D scatter plot is explained, demonstrating the plotting of sepal length against sepal width to visualize the distribution of data points. The step-by-step process of creating a 2D scatter plot and its purpose is outlined, providing a clear understanding of the visualization technique.', 'The discussion extends to the possibility of working with higher dimensions, such as 100 dimensions, in machine learning and emphasizes the interpretation of each dimension as a feature or variable for problem-solving. The consideration of higher dimensions in machine learning and the interpretation of dimensions as features is highlighted, providing insight into the scalability and complexity of datasets.', 'The potential to color data points based on their class membership is mentioned as a method to enhance the interpretation of the scatter plot. The suggestion to use color to differentiate class membership in the scatter plot is presented as a strategy for improved data interpretation and analysis.']}], 'duration': 449.547, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FLuqwQgSBDw/pics/FLuqwQgSBDw784962.jpg', 'highlights': ['The dataset contains 150 rows and 5 columns. The iris dataset consists of 150 rows and 5 columns, representing the shape of the matrix or data table.', 'The dataset has 50 data points for each Setosa, Versicolor, and Virginica classes. It is revealed that there are 50 data points each for Setosa, Versicolor, and Virginica classes, indicating a balanced distribution among the three classes.', 'The balanced nature of the iris dataset is emphasized, and the specific features included are highlighted.', 'The process of creating a 2D scatter plot is explained, demonstrating the plotting of sepal length against sepal width to visualize the distribution of data points.', 'The suggestion to use color to differentiate class membership in the scatter plot is presented as a strategy for improved data interpretation and analysis.', 'The need for slightly different data analysis approaches for imbalanced data sets compared to balanced data sets is emphasized.', 'The consideration of higher dimensions in machine learning and the interpretation of dimensions as features is highlighted, providing insight into the scalability and complexity of datasets.']}, {'end': 1518.407, 'segs': [{'end': 1291.666, 'src': 'heatmap', 'start': 1244.335, 'weight': 0.847, 'content': [{'end': 1248.716, 'text': 'OK, so C1 gives us very nice tools to plot this thing.', 'start': 1244.335, 'duration': 4.381}, {'end': 1250.617, 'text': "So let's go through the code.", 'start': 1248.936, 'duration': 1.681}, {'end': 1251.597, 'text': "It's very, very simple.", 'start': 1250.737, 'duration': 0.86}, {'end': 1257.619, 'text': "So all that I'm saying here is set a white grid, which basically gives me this grid structure for my plot here.", 'start': 1252.037, 'duration': 5.582}, {'end': 1261.04, 'text': "And all I'm saying here is color them.", 'start': 1258.259, 'duration': 2.781}, {'end': 1267.402, 'text': 'The hue parameter here says by which column in my data set should I color these points.', 'start': 1261.26, 'duration': 6.142}, {'end': 1272.344, 'text': 'So hue equals to species basically means color these points based on what species value they have.', 'start': 1267.622, 'duration': 4.722}, {'end': 1278.931, 'text': 'and plot a scatter plot here with sepal length on x-axis and sepal width on y-axis and add a legend.', 'start': 1272.964, 'duration': 5.967}, {'end': 1280.253, 'text': "We'll see what legends are.", 'start': 1279.272, 'duration': 0.981}, {'end': 1282.663, 'text': 'Legends are very, very important.', 'start': 1280.902, 'duration': 1.761}, {'end': 1286.924, 'text': 'So for a data set, typically, this is what is called a legend.', 'start': 1283.363, 'duration': 3.561}, {'end': 1291.666, 'text': 'For a plot, this is a legend, which says what color is what class label.', 'start': 1287.364, 'duration': 4.302}], 'summary': 'Using c1 tool to plot a scatter plot with sepal length on x-axis and sepal width on y-axis, and adding a legend based on species value.', 'duration': 47.331, 'max_score': 1244.335, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FLuqwQgSBDw/pics/FLuqwQgSBDw1244335.jpg'}, {'end': 1286.924, 'src': 'embed', 'start': 1261.26, 'weight': 0, 'content': [{'end': 1267.402, 'text': 'The hue parameter here says by which column in my data set should I color these points.', 'start': 1261.26, 'duration': 6.142}, {'end': 1272.344, 'text': 'So hue equals to species basically means color these points based on what species value they have.', 'start': 1267.622, 'duration': 4.722}, {'end': 1278.931, 'text': 'and plot a scatter plot here with sepal length on x-axis and sepal width on y-axis and add a legend.', 'start': 1272.964, 'duration': 5.967}, {'end': 1280.253, 'text': "We'll see what legends are.", 'start': 1279.272, 'duration': 0.981}, {'end': 1282.663, 'text': 'Legends are very, very important.', 'start': 1280.902, 'duration': 1.761}, {'end': 1286.924, 'text': 'So for a data set, typically, this is what is called a legend.', 'start': 1283.363, 'duration': 3.561}], 'summary': 'Instruction on using hue parameter to color data points in a scatter plot based on species value with sepal length and width on x-axis and y-axis respectively.', 'duration': 25.664, 'max_score': 1261.26, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FLuqwQgSBDw/pics/FLuqwQgSBDw1261260.jpg'}, {'end': 1342.4, 'src': 'embed', 'start': 1307.932, 'weight': 1, 'content': [{'end': 1309.853, 'text': 'And all of your virginicas are green in color.', 'start': 1307.932, 'duration': 1.921}, {'end': 1312.431, 'text': 'Now, the first immediate takeaway.', 'start': 1310.629, 'duration': 1.802}, {'end': 1314.754, 'text': 'And what is the x-axis here? Seppel length.', 'start': 1313.092, 'duration': 1.662}, {'end': 1316.275, 'text': 'The y-axis here is seppel width.', 'start': 1314.794, 'duration': 1.481}, {'end': 1317.977, 'text': 'The first important takeaway.', 'start': 1316.696, 'duration': 1.281}, {'end': 1319.339, 'text': "Let's never forget our objective.", 'start': 1317.997, 'duration': 1.342}, {'end': 1327.468, 'text': 'What was our objective? Our objective was given a new flower to distinguish whether it is setosa, versicolor, or virginica.', 'start': 1319.619, 'duration': 7.849}, {'end': 1332.009, 'text': 'or virginica.', 'start': 1330.407, 'duration': 1.602}, {'end': 1335.493, 'text': 'So we had a classification problem.', 'start': 1333.731, 'duration': 1.762}, {'end': 1342.4, 'text': 'By looking at it, can you do something? So the first observation I have here is this is a 2D plane.', 'start': 1336.013, 'duration': 6.387}], 'summary': 'Analyzing sepal length and width to classify flowers into setosa, versicolor, or virginica.', 'duration': 34.468, 'max_score': 1307.932, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FLuqwQgSBDw/pics/FLuqwQgSBDw1307932.jpg'}, {'end': 1498.799, 'src': 'embed', 'start': 1472.459, 'weight': 2, 'content': [{'end': 1479.785, 'text': 'But if I can draw a line here and say everything on this side, by drawing a line, if I can separate one class from other classes, that is great.', 'start': 1472.459, 'duration': 7.326}, {'end': 1481.306, 'text': "That's called linear separability.", 'start': 1480.025, 'duration': 1.281}, {'end': 1488.612, 'text': "But I can't linearly separate my versicolor and virginica flowers using these two features.", 'start': 1481.706, 'duration': 6.906}, {'end': 1490.293, 'text': 'Probably I can do it with other features.', 'start': 1488.892, 'duration': 1.401}, {'end': 1490.914, 'text': "I don't know.", 'start': 1490.373, 'duration': 0.541}, {'end': 1494.377, 'text': "Right now, I'm just plotting it with sepal length and sepal width.", 'start': 1491.314, 'duration': 3.063}, {'end': 1498.799, 'text': 'Now, the immediate question I have here is this is called a 2D scatter plot.', 'start': 1495.117, 'duration': 3.682}], 'summary': 'In data analysis, linear separability is important in classifying data, but in this case, the versicolor and virginica flowers cannot be linearly separated using sepal length and sepal width in a 2d scatter plot.', 'duration': 26.34, 'max_score': 1472.459, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FLuqwQgSBDw/pics/FLuqwQgSBDw1472459.jpg'}], 'start': 1235.71, 'title': 'Visualizing data, flower classification, and linear separability', 'summary': 'Covers visualizing data with seaborn library, creating a scatter plot of sepal length versus sepal width, discussing flower classification based on sepal dimensions, and exploring linear separability in machine learning, emphasizing the limitations and possibilities of drawing lines to separate different classes in a 2d scatter plot.', 'chapters': [{'end': 1307.532, 'start': 1235.71, 'title': 'Visualizing data with seaborn library', 'summary': 'Explains how to use the seaborn library to create a scatter plot of sepal length versus sepal width, color-coded by species, demonstrating the importance of legends and grid structure in data visualization.', 'duration': 71.822, 'highlights': ["Using Seaborn's C1 library (SNS) to create scatter plot with setosa, versicolor color coding for sepal length and width, and adding legends for species interpretation.", 'Explanation of setting a white grid structure for the plot and color coding points based on species values.', 'Importance of legends in plots to interpret class labels and significance of grid structure in data visualization.']}, {'end': 1355.665, 'start': 1307.932, 'title': 'Flower classification on 2d plane', 'summary': 'Discusses the classification problem of distinguishing setosa, versicolor, or virginica flowers based on sepal length and width, where the blue points are well separated from the orange and green points, but the orange and green points are not well separated.', 'duration': 47.733, 'highlights': ['The x-axis represents sepal length, and the y-axis represents sepal width, with the objective of distinguishing setosa, versicolor, or virginica flowers.', 'The blue points are well separated from the orange and green points, while the orange and green points are not well separated.']}, {'end': 1518.407, 'start': 1355.665, 'title': 'Linear separability in machine learning', 'summary': 'Explores the concept of linear separability and its relevance in classifying data points, emphasizing the limitations and possibilities of drawing lines to separate different classes in a 2d scatter plot, with a focus on sepal length and sepal width features.', 'duration': 162.742, 'highlights': ['Drawing a line in a 2D space to separate setosa flowers from others using sepal length and sepal width features is a key insight, highlighting the concept of linear separability and its application in machine learning.', 'The limitations of linear separability are illustrated by the inability to draw a single line to separate versicolor and virginica flowers using the same features, emphasizing the need for exploring alternative features for classification.', 'The significance of 2D scatter plots in visualizing class separation and the potential for drawing 3D scatter plots as an extension of this visualization technique is discussed, providing a comprehensive understanding of data representation in machine learning.']}], 'duration': 282.697, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FLuqwQgSBDw/pics/FLuqwQgSBDw1235710.jpg', 'highlights': ["Using Seaborn's C1 library (SNS) to create scatter plot with setosa, versicolor color coding for sepal length and width, and adding legends for species interpretation.", 'The x-axis represents sepal length, and the y-axis represents sepal width, with the objective of distinguishing setosa, versicolor, or virginica flowers.', 'Drawing a line in a 2D space to separate setosa flowers from others using sepal length and sepal width features is a key insight, highlighting the concept of linear separability and its application in machine learning.']}], 'highlights': ['Exploratory data analysis is crucial before modeling, as it helps in understanding the dataset, and is often the first step in any problem.', "The TOI dataset is often referred to as the 'hello world' of data science, due to its simplicity and widespread use in introductory courses.", 'Introducing basic plotting techniques using a simple real-world dataset called TOI, often used as an introductory example in data science.', 'The data set comprises three types of flowers: Iris setosa, versicolor, and virginica, collected in 1936.', 'The objective is to classify a given flower into one of the three categories based on sepal length, sepal width, petal length, and petal width.', 'The biologist uses the four measurements to classify iris flowers into setosa, virginica, and versicolor.', 'The data set contains four variables: sepal length, sepal width, petal length, and petal width, referred to as features, input variables, or dependent variables.', 'A data point or vector is an n-dimensional numerical array, and the class label to be predicted is the species of the flower, also known as an output variable, class label, or response label.', 'The transcript emphasizes the significance of domain knowledge in machine learning, particularly in understanding the relevance and importance of features used in datasets.', "The chapter emphasizes the importance of analyzing the amount of data points in a dataset, highlighting the investigative aspect of understanding the dataset's size and scope.", 'The dataset contains 150 rows and 5 columns. The iris dataset consists of 150 rows and 5 columns, representing the shape of the matrix or data table.', 'The dataset has 50 data points for each Setosa, Versicolor, and Virginica classes. It is revealed that there are 50 data points each for Setosa, Versicolor, and Virginica classes, indicating a balanced distribution among the three classes.', 'The balanced nature of the iris dataset is emphasized, and the specific features included are highlighted.', 'The process of creating a 2D scatter plot is explained, demonstrating the plotting of sepal length against sepal width to visualize the distribution of data points.', 'The suggestion to use color to differentiate class membership in the scatter plot is presented as a strategy for improved data interpretation and analysis.', 'The need for slightly different data analysis approaches for imbalanced data sets compared to balanced data sets is emphasized.', 'The consideration of higher dimensions in machine learning and the interpretation of dimensions as features is highlighted, providing insight into the scalability and complexity of datasets.', "Using Seaborn's C1 library (SNS) to create scatter plot with setosa, versicolor color coding for sepal length and width, and adding legends for species interpretation.", 'The x-axis represents sepal length, and the y-axis represents sepal width, with the objective of distinguishing setosa, versicolor, or virginica flowers.', 'Drawing a line in a 2D space to separate setosa flowers from others using sepal length and sepal width features is a key insight, highlighting the concept of linear separability and its application in machine learning.']}