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Live Day 4- Advance Statistics With Python In Data Science

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{'title': 'Live Day 4- Advance Statistics With Python In Data Science', 'heatmap': [{'end': 941.892, 'start': 767.442, 'weight': 0.759}, {'end': 3018.426, 'start': 2957.423, 'weight': 0.715}, {'end': 3913.689, 'start': 3784.014, 'weight': 0.817}, {'end': 4279.755, 'start': 4167.475, 'weight': 1}, {'end': 4832.776, 'start': 4657.691, 'weight': 0.763}], 'summary': 'Covers advanced statistics in python for data science, including iqr implementation, probability, permutation, combination, outlier detection using z score, visualization, probability in machine learning, conditional events, permutation & combination, combinations formula, p-value, significance value, and confidence intervals, with practical examples and python implementation.', 'chapters': [{'end': 352.922, 'segs': [{'end': 225.527, 'src': 'embed', 'start': 129.152, 'weight': 0, 'content': [{'end': 131.995, 'text': "Saurav Kedar yesterday's cricket problem.", 'start': 129.152, 'duration': 2.843}, {'end': 142.141, 'text': 'the team like second wherever the standard deviation is more that that team have probably won, you know, based on the final proportion.', 'start': 131.995, 'duration': 10.146}, {'end': 144.982, 'text': "Okay So let's start today.", 'start': 142.861, 'duration': 2.121}, {'end': 146.263, 'text': 'What all things we are going to do.', 'start': 145.042, 'duration': 1.221}, {'end': 150.766, 'text': 'First of all, we are going to implement this IQR using Python.', 'start': 146.323, 'duration': 4.443}, {'end': 157.65, 'text': 'Okay The second topic we are going to discuss about is probability.', 'start': 152.487, 'duration': 5.163}, {'end': 165.482, 'text': 'The third thing that we are going to discuss about is something called as permutation and combination.', 'start': 159.437, 'duration': 6.045}, {'end': 178.153, 'text': 'Once we finish this up, the fourth thing that we are going to discuss about is something called as confidence intervals.', 'start': 169.886, 'duration': 8.267}, {'end': 196.381, 'text': 'okay. so in confidence intervals, then probably, if we get time, we will cover up p value and then we will start with hypothesis testing.', 'start': 182.56, 'duration': 13.821}, {'end': 202.907, 'text': 'so these all things we are going to cover today, And please make sure that you keep your laptop ready.', 'start': 196.381, 'duration': 6.526}, {'end': 207.531, 'text': 'You also write code parallelly so that you can practice all the things.', 'start': 202.927, 'duration': 4.604}, {'end': 217.28, 'text': 'And with respect to all the materials that is uploaded in the community course, which is basically given in the description of this particular video.', 'start': 208.552, 'duration': 8.728}, {'end': 219.261, 'text': 'So go ahead over there.', 'start': 218.14, 'duration': 1.121}, {'end': 225.527, 'text': 'OK And probably you can just have a look at it.', 'start': 219.802, 'duration': 5.725}], 'summary': "Today's session covers iqr, probability, permutation, combination, confidence intervals, p value, and hypothesis testing.", 'duration': 96.375, 'max_score': 129.152, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xTLfLtqGsyg/pics/xTLfLtqGsyg129151.jpg'}], 'start': 25.916, 'title': 'Data analysis and statistics', 'summary': 'Covers data analysis and statistics, including iqr implementation using python, probability, permutation and combination, confidence intervals, p-value, and hypothesis testing.', 'chapters': [{'end': 352.922, 'start': 25.916, 'title': 'Data analysis and statistics session', 'summary': 'Covers a data analysis and statistics session including topics such as iqr implementation using python, probability, permutation and combination, confidence intervals, p-value, and hypothesis testing.', 'duration': 327.006, 'highlights': ['The session covers topics such as IQR implementation using Python, probability, permutation and combination, confidence intervals, p-value, and hypothesis testing. This marks the key topics covered in the session, providing a comprehensive overview of the subjects to be discussed.', 'Discussion on implementing IQR using Python. The session includes the practical implementation of IQR using Python, allowing participants to gain hands-on experience in data analysis.', 'Emphasis on practicing coding parallel to the session and utilizing the materials provided in the community course. Participants are encouraged to practice coding parallel to the session and make use of the materials provided in the community course for a more interactive learning experience.']}], 'duration': 327.006, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xTLfLtqGsyg/pics/xTLfLtqGsyg25916.jpg', 'highlights': ['Covers data analysis and statistics, including IQR implementation using Python, probability, permutation and combination, confidence intervals, p-value, and hypothesis testing.', 'Discussion on implementing IQR using Python. The session includes the practical implementation of IQR using Python, allowing participants to gain hands-on experience in data analysis.', 'Emphasis on practicing coding parallel to the session and utilizing the materials provided in the community course. Participants are encouraged to practice coding parallel to the session and make use of the materials provided in the community course for a more interactive learning experience.']}, {'end': 952.039, 'segs': [{'end': 442.948, 'src': 'embed', 'start': 407.453, 'weight': 0, 'content': [{'end': 412.756, 'text': 'Okay Now using Z score, how do you find out some outliers? Now let me just go and explain you over here.', 'start': 407.453, 'duration': 5.303}, {'end': 415.757, 'text': "Let's say that you know about normal distribution.", 'start': 413.496, 'duration': 2.261}, {'end': 419.338, 'text': 'Till now you have discussed, we have discussed so many things in normal distribution.', 'start': 415.897, 'duration': 3.441}, {'end': 421.299, 'text': 'We know that this is the mean.', 'start': 419.398, 'duration': 1.901}, {'end': 422.219, 'text': 'first standard deviation.', 'start': 421.299, 'duration': 0.92}, {'end': 423.239, 'text': 'second standard deviation.', 'start': 422.219, 'duration': 1.02}, {'end': 425.14, 'text': 'third standard deviation first.', 'start': 423.239, 'duration': 1.901}, {'end': 427.181, 'text': 'second and third standard deviation to the left.', 'start': 425.14, 'duration': 2.041}, {'end': 429.982, 'text': 'You know that 68% of data, 95% of data and 99.7% of data.', 'start': 427.301, 'duration': 2.681}, {'end': 442.948, 'text': 'Can I consider that during some of the scenarios, if my data is normally distributed after the third standard deviation,', 'start': 434.503, 'duration': 8.445}], 'summary': 'Using z score to identify outliers in normal distribution with 68%, 95%, and 99.7% data coverage.', 'duration': 35.495, 'max_score': 407.453, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xTLfLtqGsyg/pics/xTLfLtqGsyg407453.jpg'}, {'end': 534.302, 'src': 'embed', 'start': 506.202, 'weight': 2, 'content': [{'end': 511.643, 'text': 'how many data set or data points actually fall within the third standard deviation?', 'start': 506.202, 'duration': 5.441}, {'end': 518.304, 'text': "So here I'm actually going to create a function which says define detect underscore outliers.", 'start': 512.102, 'duration': 6.202}, {'end': 519.605, 'text': 'So this will be my function.', 'start': 518.465, 'duration': 1.14}, {'end': 521.905, 'text': "And here I'm going to give my data.", 'start': 520.365, 'duration': 1.54}, {'end': 525.96, 'text': 'Okay Now the first thing that I will create a threshold.', 'start': 522.706, 'duration': 3.254}, {'end': 528.641, 'text': 'My threshold will basically be 3 standard deviation.', 'start': 526.04, 'duration': 2.601}, {'end': 534.302, 'text': 'Anything that falls away from the 3 standard deviation, I will basically be able to do it.', 'start': 529.541, 'duration': 4.761}], 'summary': 'Creating a function to detect outliers beyond 3 standard deviations.', 'duration': 28.1, 'max_score': 506.202, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xTLfLtqGsyg/pics/xTLfLtqGsyg506202.jpg'}, {'end': 624.835, 'src': 'embed', 'start': 594.254, 'weight': 3, 'content': [{'end': 601.778, 'text': 'Of that specific data, I will be able to get the standard deviation, right? So I have got my mean and standard deviation.', 'start': 594.254, 'duration': 7.524}, {'end': 607.439, 'text': 'Now for each and every points inside my data set, I will just apply the z-score formula.', 'start': 602.952, 'duration': 4.487}, {'end': 617.029, 'text': "So I'll say for i in data, okay, I can say z-score is equal to I.", 'start': 607.98, 'duration': 9.049}, {'end': 618.49, 'text': 'I is my X of I points right?', 'start': 617.029, 'duration': 1.461}, {'end': 622.713, 'text': "I'll say X, I minus mean right?", 'start': 619.23, 'duration': 3.483}, {'end': 624.835, 'text': 'Divided by standard deviation.', 'start': 623.314, 'duration': 1.521}], 'summary': 'Calculate z-scores for each data point using mean and standard deviation.', 'duration': 30.581, 'max_score': 594.254, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xTLfLtqGsyg/pics/xTLfLtqGsyg594254.jpg'}, {'end': 941.892, 'src': 'heatmap', 'start': 767.442, 'weight': 0.759, 'content': [{'end': 778.296, 'text': 'threshold three basically means this this defines our third standard deviation right below, like beyond third standard deviation.', 'start': 767.442, 'duration': 10.854}, {'end': 781.038, 'text': 'i can basically say that this actually falls on.', 'start': 778.296, 'duration': 2.742}, {'end': 790.687, 'text': 'okay, if you want to probably go and check how this distribution is, so i can write plot dot hist on a specific data set.', 'start': 781.038, 'duration': 9.649}, {'end': 796.432, 'text': 'okay, plt is not defined why.', 'start': 790.687, 'duration': 5.745}, {'end': 797.633, 'text': 'okay, this should be plt.', 'start': 796.432, 'duration': 1.201}, {'end': 805.669, 'text': "It's okay whether it is normally distributed or not, but I am actually trying to see this.", 'start': 799.386, 'duration': 6.283}, {'end': 810.251, 'text': 'Okay There are some definite outliers.', 'start': 808.13, 'duration': 2.121}, {'end': 812.112, 'text': "Okay But it's okay.", 'start': 810.591, 'duration': 1.521}, {'end': 815.134, 'text': "Let's see that whether we will be able to do this or not.", 'start': 812.352, 'duration': 2.782}, {'end': 827.986, 'text': 'What is which arg passed as change data set data in per loop? It is simple, right guys? This function everybody understood or not? Oh, sorry.', 'start': 816.374, 'duration': 11.612}, {'end': 828.927, 'text': 'This should be data.', 'start': 828.166, 'duration': 0.761}, {'end': 831.889, 'text': "This data I'm actually passing over here.", 'start': 830.168, 'duration': 1.721}, {'end': 833.37, 'text': 'Fine Perfect.', 'start': 832.109, 'duration': 1.261}, {'end': 836.371, 'text': 'I think everybody is saying it.', 'start': 833.99, 'duration': 2.381}, {'end': 837.412, 'text': "That's fine.", 'start': 836.391, 'duration': 1.021}, {'end': 841.915, 'text': 'Okay See, threshold.', 'start': 838.933, 'duration': 2.982}, {'end': 844.416, 'text': 'Threshold here is my third standard deviation.', 'start': 842.035, 'duration': 2.381}, {'end': 847.098, 'text': 'Third standard deviation.', 'start': 846.017, 'duration': 1.081}, {'end': 852.241, 'text': 'Okay If you want the data set, I can paste this entirely and give in the chat.', 'start': 847.318, 'duration': 4.923}, {'end': 859.781, 'text': 'So this is my chat with respect to the dataset.', 'start': 855.558, 'duration': 4.223}, {'end': 861.963, 'text': "I've already given it to you all.", 'start': 860.582, 'duration': 1.381}, {'end': 866.546, 'text': "Okay So now let's go and execute it.", 'start': 863.164, 'duration': 3.382}, {'end': 868.268, 'text': "Now I've executed this.", 'start': 867.047, 'duration': 1.221}, {'end': 876.494, 'text': "Now what I'm actually going to do over here, I'm just going to call detect underscore outliers.", 'start': 869.108, 'duration': 7.386}, {'end': 881.114, 'text': 'and I am going to call this specific dataset.', 'start': 878.351, 'duration': 2.763}, {'end': 886.599, 'text': 'The dataset, nb.abs, nb.abs basically means nb.absolute.', 'start': 881.854, 'duration': 4.745}, {'end': 888.721, 'text': 'Okay? Absolute function.', 'start': 887.179, 'duration': 1.542}, {'end': 894.025, 'text': 'Now once I execute it, here you will be seeing that it will be returning these three outliers.', 'start': 889.721, 'duration': 4.304}, {'end': 899.781, 'text': 'Are these my outliers or not guys? Yes? The for loop is very simple.', 'start': 894.586, 'duration': 5.195}, {'end': 909.488, 'text': 'For i in data, I am finding for every data which is in the form of list, all the z score and I am comparing if the z score is greater than 3 or not.', 'start': 899.821, 'duration': 9.667}, {'end': 912.69, 'text': 'If it is greater than 3, I am considering it as an outlier.', 'start': 910.028, 'duration': 2.662}, {'end': 915.072, 'text': 'Here you can see all the outliers are there.', 'start': 913.151, 'duration': 1.921}, {'end': 921.28, 'text': 'Okay, outliers means a big number.', 'start': 916.213, 'duration': 5.067}, {'end': 923.561, 'text': 'right, if you have not attended the previous session.', 'start': 921.28, 'duration': 2.281}, {'end': 927.404, 'text': 'guys, see, if you have not attended the previous session, you can drop off.', 'start': 923.561, 'duration': 3.843}, {'end': 929.585, 'text': "okay, because you'll not be able to understand.", 'start': 927.404, 'duration': 2.181}, {'end': 941.892, 'text': 'this is a seven days live session, right, if you are not able to understand the previous one, yeah, this appa is really.', 'start': 929.585, 'duration': 12.307}], 'summary': 'Detecting outliers using third standard deviation and z-scores, identifying three outliers in the dataset.', 'duration': 174.45, 'max_score': 767.442, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xTLfLtqGsyg/pics/xTLfLtqGsyg767442.jpg'}, {'end': 921.28, 'src': 'embed', 'start': 894.586, 'weight': 1, 'content': [{'end': 899.781, 'text': 'Are these my outliers or not guys? Yes? The for loop is very simple.', 'start': 894.586, 'duration': 5.195}, {'end': 909.488, 'text': 'For i in data, I am finding for every data which is in the form of list, all the z score and I am comparing if the z score is greater than 3 or not.', 'start': 899.821, 'duration': 9.667}, {'end': 912.69, 'text': 'If it is greater than 3, I am considering it as an outlier.', 'start': 910.028, 'duration': 2.662}, {'end': 915.072, 'text': 'Here you can see all the outliers are there.', 'start': 913.151, 'duration': 1.921}, {'end': 921.28, 'text': 'Okay, outliers means a big number.', 'start': 916.213, 'duration': 5.067}], 'summary': 'For loop checks z score for outliers, considering z score > 3 as outlier.', 'duration': 26.694, 'max_score': 894.586, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xTLfLtqGsyg/pics/xTLfLtqGsyg894586.jpg'}], 'start': 352.922, 'title': 'Outlier detection using z score', 'summary': 'Discusses the process of detecting outliers in a data set using z score and standard deviation, emphasizing that data points beyond the third standard deviation can be considered as outliers. it also covers the implementation of the z-score formula in python to detect outliers, resulting in the identification of three outliers.', 'chapters': [{'end': 528.641, 'start': 352.922, 'title': 'Outlier detection using z score', 'summary': 'Discusses the process of detecting outliers in a data set using z score and standard deviation, emphasizing that data points beyond the third standard deviation can be considered as outliers.', 'duration': 175.719, 'highlights': ['The chapter emphasizes the process of detecting outliers in a data set using Z score and standard deviation, noting that data points beyond the third standard deviation can be considered as outliers.', 'The discussion revolves around creating a list of outliers by implementing a function to detect data points beyond the third standard deviation using Z score.', 'The speaker highlights the threshold of 3 standard deviations as a criterion for identifying outliers in the data set.']}, {'end': 952.039, 'start': 529.541, 'title': 'Detecting outliers using z-score', 'summary': 'Covers the implementation of the z-score formula in python to detect outliers, with a threshold of 3 standard deviations, resulting in the identification of three outliers.', 'duration': 422.498, 'highlights': ['The z-score formula is implemented in Python to detect outliers, with a threshold of 3 standard deviations, resulting in the identification of three outliers.', 'The process involves computing the mean and standard deviation of the dataset, then applying the z-score formula to each data point to determine the number of standard deviations it is away from the mean.', 'The outliers are identified by comparing the z-score to the threshold of 3, and the identified outliers are returned as the result of the function call.']}], 'duration': 599.117, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xTLfLtqGsyg/pics/xTLfLtqGsyg352922.jpg', 'highlights': ['The chapter emphasizes the process of detecting outliers using Z score and standard deviation.', 'The z-score formula is implemented in Python to detect outliers, resulting in the identification of three outliers.', 'The discussion revolves around creating a list of outliers by implementing a function to detect data points beyond the third standard deviation using Z score.', 'The process involves computing the mean and standard deviation of the dataset, then applying the z-score formula to each data point.', 'The speaker highlights the threshold of 3 standard deviations as a criterion for identifying outliers in the data set.', 'The outliers are identified by comparing the z-score to the threshold of 3.']}, {'end': 1553.505, 'segs': [{'end': 1126.559, 'src': 'embed', 'start': 1097.558, 'weight': 0, 'content': [{'end': 1103.366, 'text': 'right, this is the formula to basically find out the lower fence, then find the upper fence.', 'start': 1097.558, 'duration': 5.808}, {'end': 1113.336, 'text': 'Here I will basically be using Q3 plus 1.5 multiplied by IQR.', 'start': 1105.834, 'duration': 7.502}, {'end': 1117.937, 'text': 'Okay So these are the steps that we are probably going to do.', 'start': 1114.196, 'duration': 3.741}, {'end': 1126.559, 'text': 'Okay So these are my steps that I am actually going to plan for and based on the steps I will be implementing it.', 'start': 1119.057, 'duration': 7.502}], 'summary': 'Formula to find lower and upper fence using q3 and iqr.', 'duration': 29.001, 'max_score': 1097.558, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xTLfLtqGsyg/pics/xTLfLtqGsyg1097558.jpg'}, {'end': 1283.39, 'src': 'embed', 'start': 1249.617, 'weight': 1, 'content': [{'end': 1253.779, 'text': "Now once we have this, now let's go ahead and compute the lower fence and the higher fence.", 'start': 1249.617, 'duration': 4.162}, {'end': 1259.262, 'text': 'Now in order to compute the lower fence and the higher fence, here I am just going to write the comment.', 'start': 1254.559, 'duration': 4.703}, {'end': 1265.965, 'text': 'Find the lower fence and higher fence.', 'start': 1259.962, 'duration': 6.003}, {'end': 1280.668, 'text': 'Okay The lower fence, is equal to Q1, right, minus 1.5 multiplied by IQR, right.', 'start': 1267.066, 'duration': 13.602}, {'end': 1283.39, 'text': 'And before that, I need to compute the IQR.', 'start': 1281.389, 'duration': 2.001}], 'summary': 'Compute lower and higher fences for data analysis.', 'duration': 33.773, 'max_score': 1249.617, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xTLfLtqGsyg/pics/xTLfLtqGsyg1249617.jpg'}, {'end': 1488.197, 'src': 'embed', 'start': 1452.781, 'weight': 2, 'content': [{'end': 1460.385, 'text': 'This same outlier we found out with the help of multiple things, right? And here also you can see 7.5 to 19.5.', 'start': 1452.781, 'duration': 7.604}, {'end': 1464.067, 'text': 'So most of your data points that will be lying over here will be based on that.', 'start': 1460.385, 'duration': 3.682}, {'end': 1477.715, 'text': 'Okay? Everybody clear? If I probably remove those three elements and try to see that particular data set, then this box plot will look bigger.', 'start': 1466.348, 'duration': 11.367}, {'end': 1482.338, 'text': 'Clear? Okay.', 'start': 1478.616, 'duration': 3.722}, {'end': 1488.197, 'text': 'So I will just share this notebook to everyone.', 'start': 1485.455, 'duration': 2.742}], 'summary': 'Identified outlier range of 7.5 to 19.5, removing 3 elements will affect box plot size.', 'duration': 35.416, 'max_score': 1452.781, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xTLfLtqGsyg/pics/xTLfLtqGsyg1452781.jpg'}], 'start': 952.039, 'title': 'Calculating z score, iqr, and visualizing data', 'summary': 'Covers the computation of z score and iqr, including steps for calculating iqr, finding lower and upper fences, and implementing the process to find outliers. it also involves calculating q1, q3, lower fence, and higher fence using python code, and visualizing the data through a box plot to identify outliers and determine the range of the dataset.', 'chapters': [{'end': 1186.719, 'start': 952.039, 'title': 'Z score and iqr computation', 'summary': 'Discusses the computation of z score and iqr, covering the steps for calculating iqr, finding lower and upper fences, and implementing the process to find outliers using iqr.', 'duration': 234.68, 'highlights': ['The chapter covers the steps for calculating IQR, including finding Q1 (25th percentile) and Q3 (75th percentile), and subtracting Q3 minus Q1 to obtain IQR.', 'The process of finding the lower fence involves using the formula Q1 - 1.5 * IQR, while the upper fence is determined using the formula Q3 + 1.5 * IQR.', "The discussion emphasizes the implementation steps, which include sorting the dataset using the 'sorted' function and executing the outlined computation steps to find outliers using IQR."]}, {'end': 1553.505, 'start': 1186.779, 'title': 'Calculate q1, q3 and visualize data', 'summary': 'Covers the calculation of q1, q3, lower fence, and higher fence using python code, and visualizing the data through a box plot to identify outliers and determine the range of the dataset.', 'duration': 366.726, 'highlights': ['The lower fence is calculated as Q1 minus 1.5 multiplied by the interquartile range (IQR), and the higher fence is calculated as Q3 plus 1.5 multiplied by IQR, resulting in a range of 7.5 to 19.5 for the dataset.', 'The interquartile range (IQR) is computed as 3, facilitating the calculation of the lower and higher fences.', 'Visualizing the data through a box plot helps identify a significant outlier in the dataset, with most data points lying within the range of 7.5 to 19.5.']}], 'duration': 601.466, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xTLfLtqGsyg/pics/xTLfLtqGsyg952039.jpg', 'highlights': ['The process of finding the lower fence involves using the formula Q1 - 1.5 * IQR, while the upper fence is determined using the formula Q3 + 1.5 * IQR.', 'The lower fence is calculated as Q1 minus 1.5 multiplied by the interquartile range (IQR), and the higher fence is calculated as Q3 plus 1.5 multiplied by IQR, resulting in a range of 7.5 to 19.5 for the dataset.', 'Visualizing the data through a box plot helps identify a significant outlier in the dataset, with most data points lying within the range of 7.5 to 19.5.']}, {'end': 3031.658, 'segs': [{'end': 1612.979, 'src': 'embed', 'start': 1579.737, 'weight': 0, 'content': [{'end': 1584.879, 'text': 'Okay? And we will try to see that what all things we can actually do with the help of probability.', 'start': 1579.737, 'duration': 5.142}, {'end': 1590.66, 'text': 'Probability is by default used in machine learning also, in deep learning also, many places.', 'start': 1584.919, 'duration': 5.741}, {'end': 1600.488, 'text': "Let's say one example, okay? Suppose I have two categories of dataset, like this, right? I have another category of dataset.", 'start': 1590.7, 'duration': 9.788}, {'end': 1612.979, 'text': "If I try to create a best fit line, you can see that, let's say that this belongs to class A, this belongs to class B.", 'start': 1602.55, 'duration': 10.429}], 'summary': 'Exploring the applications of probability in machine learning and deep learning, such as in creating best fit lines for different classes of datasets.', 'duration': 33.242, 'max_score': 1579.737, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xTLfLtqGsyg/pics/xTLfLtqGsyg1579737.jpg'}, {'end': 1651.414, 'src': 'embed', 'start': 1626.289, 'weight': 1, 'content': [{'end': 1635.956, 'text': 'Now, my question is that what probability of this particular point belongs to class A and what probability of this particular point belongs to class B? Because it is passing through the line.', 'start': 1626.289, 'duration': 9.667}, {'end': 1640.579, 'text': 'So, based on probability, we can definitely get a lot of things.', 'start': 1636.676, 'duration': 3.903}, {'end': 1641.92, 'text': 'In linear regression, it is used.', 'start': 1640.659, 'duration': 1.261}, {'end': 1643.501, 'text': 'In logistically, it is used and all.', 'start': 1641.96, 'duration': 1.541}, {'end': 1651.414, 'text': 'Right So probability really focuses, like base is basically used over there and different, different things are used.', 'start': 1643.649, 'duration': 7.765}], 'summary': 'The focus is on probability in linear and logistic regression for class a and b points.', 'duration': 25.125, 'max_score': 1626.289, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xTLfLtqGsyg/pics/xTLfLtqGsyg1626289.jpg'}, {'end': 1714.79, 'src': 'embed', 'start': 1679.801, 'weight': 2, 'content': [{'end': 1699.67, 'text': 'If you want to give a definition, what exactly is a probability? So here you can say that probability is a measure of the likelihood of an event.', 'start': 1679.801, 'duration': 19.869}, {'end': 1705.584, 'text': 'Okay, probability is a measure of the likelihood of an event.', 'start': 1702.202, 'duration': 3.382}, {'end': 1707.325, 'text': 'The reason why I am writing you this.', 'start': 1705.724, 'duration': 1.601}, {'end': 1711.448, 'text': 'all definitions, guys, understand, you really need to think.', 'start': 1707.325, 'duration': 4.123}, {'end': 1713.57, 'text': 'you know what exactly is happening over here?', 'start': 1711.448, 'duration': 2.122}, {'end': 1714.79, 'text': 'What is the definition?', 'start': 1713.63, 'duration': 1.16}], 'summary': 'Probability is a measure of the likelihood of an event.', 'duration': 34.989, 'max_score': 1679.801, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xTLfLtqGsyg/pics/xTLfLtqGsyg1679801.jpg'}, {'end': 1900.757, 'src': 'embed', 'start': 1873.151, 'weight': 4, 'content': [{'end': 1876.314, 'text': 'Over here I am basically going to define as addition rule.', 'start': 1873.151, 'duration': 3.163}, {'end': 1878.276, 'text': 'This is super important.', 'start': 1877.235, 'duration': 1.041}, {'end': 1883.561, 'text': 'Probably in your aptitudes you will be using this.', 'start': 1878.736, 'duration': 4.825}, {'end': 1887.104, 'text': 'Addition rule or we also say it as probability or or.', 'start': 1884.041, 'duration': 3.063}, {'end': 1891.251, 'text': 'Or, or also you say it as like this, or.', 'start': 1888.909, 'duration': 2.342}, {'end': 1896.854, 'text': 'Okay Now, in order to understand additional rule, you need to understand about two things.', 'start': 1891.651, 'duration': 5.203}, {'end': 1900.757, 'text': 'One is mutual exclusive events.', 'start': 1897.095, 'duration': 3.662}], 'summary': 'Defining addition rule for probability, essential for aptitudes, requires understanding mutual exclusive events.', 'duration': 27.606, 'max_score': 1873.151, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xTLfLtqGsyg/pics/xTLfLtqGsyg1873151.jpg'}, {'end': 2003.136, 'src': 'embed', 'start': 1978.564, 'weight': 5, 'content': [{'end': 1985.228, 'text': 'You will only get at one experiment or one event that you are probably rolling a dice at a single time.', 'start': 1978.564, 'duration': 6.664}, {'end': 1987.31, 'text': 'You will only be able to get one number.', 'start': 1985.789, 'duration': 1.521}, {'end': 1988.811, 'text': 'You will not be able to get two numbers.', 'start': 1987.41, 'duration': 1.401}, {'end': 1991.533, 'text': 'So this is specifically an example of mutual exclusive.', 'start': 1988.851, 'duration': 2.682}, {'end': 1995.015, 'text': 'Another example again, tossing a coin.', 'start': 1992.273, 'duration': 2.742}, {'end': 1997.317, 'text': 'In this particular case, also right?', 'start': 1995.616, 'duration': 1.701}, {'end': 2001.094, 'text': 'tossing a coin in this particular case also, what happens?', 'start': 1998.652, 'duration': 2.442}, {'end': 2003.136, 'text': 'you may either get head or tail.', 'start': 2001.094, 'duration': 2.042}], 'summary': 'Experiments involve single outcomes, such as rolling a dice or tossing a coin.', 'duration': 24.572, 'max_score': 1978.564, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xTLfLtqGsyg/pics/xTLfLtqGsyg1978564.jpg'}, {'end': 2260.922, 'src': 'embed', 'start': 2227.997, 'weight': 6, 'content': [{'end': 2241.428, 'text': 'So, whenever you have a mutual exclusive event at that point of time, you can define this specific definition, which is also called as addition rule,', 'start': 2227.997, 'duration': 13.431}, {'end': 2246.373, 'text': 'which is also called as addition rule right for mutual exclusive.', 'start': 2241.428, 'duration': 4.945}, {'end': 2251.998, 'text': 'Now here, what is probability of A? You know that it is 1 by 2 plus 1 by 2.', 'start': 2247.174, 'duration': 4.824}, {'end': 2253.64, 'text': 'So the answer will be 1.', 'start': 2251.998, 'duration': 1.642}, {'end': 2260.922, 'text': 'So probability of A or B to come is basically 1.', 'start': 2253.64, 'duration': 7.282}], 'summary': 'Using the addition rule for mutually exclusive events, the probability of a or b occurring is 1.', 'duration': 32.925, 'max_score': 2227.997, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xTLfLtqGsyg/pics/xTLfLtqGsyg2227997.jpg'}, {'end': 2683.83, 'src': 'embed', 'start': 2641.446, 'weight': 7, 'content': [{'end': 2643.267, 'text': 'This will basically be the probability.', 'start': 2641.446, 'duration': 1.821}, {'end': 2665.083, 'text': 'Clear everybody? Yeah, 4 by 13.', 'start': 2646.868, 'duration': 18.215}, {'end': 2668.404, 'text': 'Yeah? Okay.', 'start': 2665.083, 'duration': 3.321}, {'end': 2671.865, 'text': 'Now, you have probably understood additional rule.', 'start': 2669.204, 'duration': 2.661}, {'end': 2673.006, 'text': 'Addition rule.', 'start': 2672.486, 'duration': 0.52}, {'end': 2676.027, 'text': 'Now, we need to understand one more rule in probability.', 'start': 2673.666, 'duration': 2.361}, {'end': 2681.569, 'text': 'See guys, if you do this much, I think you will be able to solve any problem statement that comes in your mind.', 'start': 2676.047, 'duration': 5.522}, {'end': 2683.83, 'text': 'Okay? That is what I feel.', 'start': 2681.869, 'duration': 1.961}], 'summary': 'Introduction to probability rules, including 4 by 13 and addition rule.', 'duration': 42.384, 'max_score': 2641.446, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xTLfLtqGsyg/pics/xTLfLtqGsyg2641446.jpg'}, {'end': 2767.337, 'src': 'embed', 'start': 2738.468, 'weight': 8, 'content': [{'end': 2740.908, 'text': "Now let's go ahead and discuss about the multiplication rule.", 'start': 2738.468, 'duration': 2.44}, {'end': 2744.469, 'text': 'In multiplication rule, one thing you need to understand here.', 'start': 2741.468, 'duration': 3.001}, {'end': 2752.711, 'text': 'we need to understand something called as independent, independent events and non-independent events.', 'start': 2744.469, 'duration': 8.242}, {'end': 2754.471, 'text': 'These are something very, very important.', 'start': 2752.831, 'duration': 1.64}, {'end': 2758.052, 'text': 'Okay Okay.', 'start': 2755.451, 'duration': 2.601}, {'end': 2759.472, 'text': 'So non-independent events.', 'start': 2758.212, 'duration': 1.26}, {'end': 2767.337, 'text': 'Yeah Okay, so independent events and non-independent events.', 'start': 2762.553, 'duration': 4.784}], 'summary': 'Discussion on the multiplication rule and independent events.', 'duration': 28.869, 'max_score': 2738.468, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xTLfLtqGsyg/pics/xTLfLtqGsyg2738468.jpg'}, {'end': 2851.913, 'src': 'embed', 'start': 2826.144, 'weight': 9, 'content': [{'end': 2835.797, 'text': 'Right? So over here what you can understand is that each and every events, each and every events, each and every events are independent.', 'start': 2826.144, 'duration': 9.653}, {'end': 2844.088, 'text': 'Okay? One, if one, one comes or if two comes or if any events come, it is not going to impact any other event.', 'start': 2836.718, 'duration': 7.37}, {'end': 2848.771, 'text': 'Every time you probably have to roll and everybody has an equal probability to come over here.', 'start': 2844.768, 'duration': 4.003}, {'end': 2851.913, 'text': 'This is what is an independent event called as.', 'start': 2849.391, 'duration': 2.522}], 'summary': 'Each event is independent with equal probability.', 'duration': 25.769, 'max_score': 2826.144, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xTLfLtqGsyg/pics/xTLfLtqGsyg2826144.jpg'}, {'end': 3018.426, 'src': 'heatmap', 'start': 2957.423, 'weight': 0.715, 'content': [{'end': 2964.548, 'text': 'Right? So, here what is happening, after this particular event, it has impacted this event.', 'start': 2957.423, 'duration': 7.125}, {'end': 2967.451, 'text': 'Right? Because the number of marbles are reduced.', 'start': 2965.229, 'duration': 2.222}, {'end': 2969.993, 'text': 'Right? And finally, you got 2 over here.', 'start': 2968.011, 'duration': 1.982}, {'end': 2972.835, 'text': 'So, this is a perfect example of a dependent age.', 'start': 2970.073, 'duration': 2.762}, {'end': 2982.567, 'text': 'Clear, guys? So, multiplication rule basically says that in the case of an independent event, we have to solve it in a different way.', 'start': 2974.383, 'duration': 8.184}, {'end': 2985.929, 'text': 'In the case of a dependent event, we have to solve in a different way.', 'start': 2983.087, 'duration': 2.842}, {'end': 2991.532, 'text': 'Because of this dependent event, there is an amazing algorithm which is called as Knape bias.', 'start': 2986.749, 'duration': 4.783}, {'end': 2994.273, 'text': 'Have you heard of Knape bias? I think most of you have heard of.', 'start': 2991.572, 'duration': 2.701}, {'end': 2999.276, 'text': 'Right? There is a topic which is called as conditional probability.', 'start': 2995.253, 'duration': 4.023}, {'end': 3003.418, 'text': 'This is where conditional probability will come into existence.', 'start': 3000.456, 'duration': 2.962}, {'end': 3006.416, 'text': 'Okay? So I will talk about it.', 'start': 3003.818, 'duration': 2.598}, {'end': 3009.479, 'text': "So let's go and solve some problems.", 'start': 3006.557, 'duration': 2.922}, {'end': 3011.901, 'text': "So let's go and solve a problem.", 'start': 3010.42, 'duration': 1.481}, {'end': 3013.983, 'text': 'I hope everybody is liking it.', 'start': 3012.502, 'duration': 1.481}, {'end': 3015.324, 'text': 'You have to keep on hitting like.', 'start': 3014.043, 'duration': 1.281}, {'end': 3016.545, 'text': 'We have to make 1000 likes.', 'start': 3015.384, 'duration': 1.161}, {'end': 3018.426, 'text': 'Then only I will get Red Bull.', 'start': 3016.665, 'duration': 1.761}], 'summary': 'Dependent events impact outcomes with reduced marbles. introduces knape bias and conditional probability.', 'duration': 61.003, 'max_score': 2957.423, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xTLfLtqGsyg/pics/xTLfLtqGsyg2957423.jpg'}, {'end': 3011.901, 'src': 'embed', 'start': 2983.087, 'weight': 10, 'content': [{'end': 2985.929, 'text': 'In the case of a dependent event, we have to solve in a different way.', 'start': 2983.087, 'duration': 2.842}, {'end': 2991.532, 'text': 'Because of this dependent event, there is an amazing algorithm which is called as Knape bias.', 'start': 2986.749, 'duration': 4.783}, {'end': 2994.273, 'text': 'Have you heard of Knape bias? I think most of you have heard of.', 'start': 2991.572, 'duration': 2.701}, {'end': 2999.276, 'text': 'Right? There is a topic which is called as conditional probability.', 'start': 2995.253, 'duration': 4.023}, {'end': 3003.418, 'text': 'This is where conditional probability will come into existence.', 'start': 3000.456, 'duration': 2.962}, {'end': 3006.416, 'text': 'Okay? So I will talk about it.', 'start': 3003.818, 'duration': 2.598}, {'end': 3009.479, 'text': "So let's go and solve some problems.", 'start': 3006.557, 'duration': 2.922}, {'end': 3011.901, 'text': "So let's go and solve a problem.", 'start': 3010.42, 'duration': 1.481}], 'summary': "Dependent events lead to the knape bias algorithm and conditional probability. let's solve problems.", 'duration': 28.814, 'max_score': 2983.087, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xTLfLtqGsyg/pics/xTLfLtqGsyg2983087.jpg'}], 'start': 1553.505, 'title': 'Probability in machine learning', 'summary': 'Discusses the importance of probability in machine learning, particularly in the context of linear and logistic regression, covering probability basics, addition rule, non-mutual exclusive events, probability rules and formulas, and multiplication rule and independent events, with examples and practical applications.', 'chapters': [{'end': 1643.501, 'start': 1553.505, 'title': 'Probability in machine learning', 'summary': 'Discusses the importance of probability in machine learning, particularly in the context of linear regression and logistic regression, highlighting its significance in making predictions and decision-making.', 'duration': 89.996, 'highlights': ['Probability plays a crucial role in machine learning, especially in linear regression and logistic regression, aiding in making predictions and decisions.', 'Probability is utilized in various aspects of machine learning, including linear regression and logistic regression, to determine the likelihood of data points belonging to different classes.', 'The discussion emphasizes the significance of probability in machine learning, particularly in linear regression and logistic regression, in understanding the likelihood of data points belonging to specific classes.']}, {'end': 2024.935, 'start': 1643.649, 'title': 'Probability basics and addition rule', 'summary': 'Covers the basics of probability, including defining probability, calculating probabilities for different events like rolling a dice and tossing a coin, and introduces the addition rule and mutual exclusive events.', 'duration': 381.286, 'highlights': ['The definition of probability as a measure of the likelihood of an event and the calculation of probabilities for specific events, such as rolling a dice or tossing a coin, are explained with clear examples.', 'Introduction to the addition rule and the concept of mutual exclusive events is discussed, providing examples like rolling a dice and tossing a coin to illustrate the concept.', 'Clear explanations and examples are used to help understand the basics of probability and the addition rule, with interactive questions to engage the audience in the learning process.']}, {'end': 2459.027, 'start': 2025.615, 'title': 'Non-mutual exclusive events', 'summary': 'Discusses non-mutual exclusive events, providing examples with a deck of cards and coin toss, and calculating probabilities using the addition rule for non-mutual exclusive events.', 'duration': 433.412, 'highlights': ['The chapter explains non-mutual exclusive events using examples with a deck of cards and coin toss, emphasizing that multiple events can occur simultaneously.', 'The addition rule for non-mutual exclusive events is defined as probability of A or B, where A and B are events, is equal to probability of A plus probability of B.', 'The probability of choosing a card that is queen or a heart is calculated by considering non-mutual exclusive events.']}, {'end': 2737.928, 'start': 2464.071, 'title': 'Probability rules and formulas', 'summary': 'Explains the addition rule for non-mutual exclusive events, using a deck of cards example to calculate the probability of getting a queen or heart, and introduces the multiplication rule for probability.', 'duration': 273.857, 'highlights': ['The addition rule is explained using a deck of cards example to calculate the probability of getting a queen or heart. The probability of getting a queen or heart is calculated using the addition rule as (4/52 + 13/52) - 1/52 = 16/52, simplifying to 4/13.', 'Introduction of the multiplication rule for probability. The chapter introduces the multiplication rule for probability, transitioning from the explanation of the addition rule.']}, {'end': 3031.658, 'start': 2738.468, 'title': 'Multiplication rule and independent events', 'summary': 'Discusses the concept of independent and dependent events in the context of the multiplication rule, providing examples of rolling a dice for independent events and drawing marbles from a bag for dependent events while also introducing the concept of conditional probability and knape bias.', 'duration': 293.19, 'highlights': ['The concept of independent and dependent events is explained using the examples of rolling a dice and drawing marbles from a bag, demonstrating the impact of one event on another. The chapter provides examples of rolling a dice to illustrate independent events and drawing marbles from a bag to illustrate dependent events, highlighting the impact of one event on another.', 'The concept of conditional probability and Knape bias is introduced in the context of dependent events. Conditional probability and Knape bias are introduced as concepts relevant to dependent events, demonstrating the additional complexity involved in solving problems related to dependent events.', 'The need to solve independent and dependent events differently is emphasized, highlighting the distinct algorithms and solving methods required for each type of event. The chapter emphasizes the need to solve independent and dependent events differently, underlining the requirement for distinct algorithms and solving methods for each type of event.']}], 'duration': 1478.153, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xTLfLtqGsyg/pics/xTLfLtqGsyg1553505.jpg', 'highlights': ['Probability is crucial in machine learning, aiding in predictions and decisions.', 'Probability is utilized in linear and logistic regression to determine data point likelihood.', 'The chapter emphasizes the significance of probability in understanding data point likelihood.', 'The definition of probability and calculation for specific events are explained with examples.', 'Introduction to the addition rule and mutual exclusive events is discussed with clear examples.', 'The chapter explains non-mutual exclusive events using examples with a deck of cards and coin toss.', 'The addition rule for non-mutual exclusive events is defined and calculated using examples.', 'The addition rule is explained using a deck of cards example to calculate the probability.', 'Introduction of the multiplication rule for probability is provided.', 'The concept of independent and dependent events is explained using clear examples.', 'The chapter introduces conditional probability and Knape bias in the context of dependent events.', 'The need to solve independent and dependent events differently is emphasized.']}, {'end': 3420.355, 'segs': [{'end': 3111.134, 'src': 'embed', 'start': 3081.727, 'weight': 0, 'content': [{'end': 3088.32, 'text': 'in the first event, you have rolled a dice, you are getting 5 and then again you rolled a dice, then you got 4..', 'start': 3081.727, 'duration': 6.593}, {'end': 3093.783, 'text': 'So what is the probability of getting 5 and then 4? This is a simple question.', 'start': 3088.32, 'duration': 5.463}, {'end': 3097.926, 'text': 'And for this, this obviously is an independent event.', 'start': 3094.344, 'duration': 3.582}, {'end': 3098.646, 'text': 'You know that.', 'start': 3098.166, 'duration': 0.48}, {'end': 3104.35, 'text': 'Right? You know that.', 'start': 3102.889, 'duration': 1.461}, {'end': 3111.134, 'text': "Right? Now, how do we solve this particular problem? So I'll say independent event.", 'start': 3104.77, 'duration': 6.364}], 'summary': 'Probability of getting 5 and then 4 from rolling dice, independent events.', 'duration': 29.407, 'max_score': 3081.727, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xTLfLtqGsyg/pics/xTLfLtqGsyg3081727.jpg'}, {'end': 3240.931, 'src': 'embed', 'start': 3192.928, 'weight': 1, 'content': [{'end': 3224.576, 'text': 'Okay?. Now What is the probability of drawing a queen and then a and then a aces from a deck of cards?', 'start': 3192.928, 'duration': 31.648}, {'end': 3228.241, 'text': 'See over here two events are actually happening.', 'start': 3226.38, 'duration': 1.861}, {'end': 3233.125, 'text': "Okay So let's go ahead.", 'start': 3231.344, 'duration': 1.781}, {'end': 3236.567, 'text': 'First of all, again, you need to find out whether this is an independent or dependent event.', 'start': 3233.205, 'duration': 3.362}, {'end': 3240.931, 'text': 'Obviously, in this case, this will be a dependent event because a deck of cards will get reduced.', 'start': 3237.088, 'duration': 3.843}], 'summary': 'Probability of drawing a queen and then an ace from a deck of cards is a dependent event.', 'duration': 48.003, 'max_score': 3192.928, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xTLfLtqGsyg/pics/xTLfLtqGsyg3192928.jpg'}, {'end': 3382.886, 'src': 'embed', 'start': 3356.775, 'weight': 2, 'content': [{'end': 3364.541, 'text': 'all the machine learning algorithms where probably I will be discussing about them, where Bayes theorem, when I talk about Bayes theorem,', 'start': 3356.775, 'duration': 7.766}, {'end': 3365.902, 'text': 'conditional probability will also come.', 'start': 3364.541, 'duration': 1.361}, {'end': 3375.33, 'text': 'Okay? So here, what is probability of king, sorry, it is queen and king, right? Queen and aces, sorry.', 'start': 3366.322, 'duration': 9.008}, {'end': 3382.886, 'text': "So here what I'll do, probability of queen multiplied by probability of aces given queen.", 'start': 3376.243, 'duration': 6.643}], 'summary': 'Discussing various machine learning algorithms and applying bayes theorem to calculate conditional probability.', 'duration': 26.111, 'max_score': 3356.775, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xTLfLtqGsyg/pics/xTLfLtqGsyg3356775.jpg'}], 'start': 3031.718, 'title': 'Probability and conditional events', 'summary': "Discusses the probability of independent and dependent events using examples such as rolling dice and drawing cards, emphasizing the application of the multiplication rule. it also explains the concept of conditional probability in the case of independent events using examples of marble and card probabilities, emphasizing its importance in bayes' theorem and machine learning algorithms.", 'chapters': [{'end': 3240.931, 'start': 3031.718, 'title': 'Probability of independent and dependent events', 'summary': 'Discusses the probability of independent and dependent events using examples such as rolling dice and drawing cards, emphasizing the application of the multiplication rule and providing a clear explanation of the formulas and calculations involved.', 'duration': 209.213, 'highlights': ['The chapter explains the probability of rolling a 5 and then a 4 on a dice, calculating the independent event by applying the multiplication rule, resulting in a probability of 1/36.', 'It also presents an example of a dependent event by calculating the probability of drawing a queen and then an ace from a deck of cards, highlighting the reduction in the deck as a factor in determining dependence.']}, {'end': 3420.355, 'start': 3241.511, 'title': 'Conditional probability in events', 'summary': "Explains the concept of conditional probability in the case of independent events using examples of marble and card probabilities, emphasizing its importance in bayes' theorem and machine learning algorithms.", 'duration': 178.844, 'highlights': ['Conditional probability is demonstrated using the example of drawing marbles from a bag, with the probability of a subsequent event being dependent on the outcome of the first event. ', "The importance of conditional probability in Bayes' theorem and machine learning algorithms is emphasized for understanding and application in probability calculations. ", 'The calculation of the probability of drawing a queen and an ace from a deck of cards is explained using the concept of conditional probability and the total number of cards in the deck. Probability of queen: 4/52, Probability of ace given queen: 4/51']}], 'duration': 388.637, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xTLfLtqGsyg/pics/xTLfLtqGsyg3031718.jpg', 'highlights': ['The chapter explains the probability of rolling a 5 and then a 4 on a dice, calculating the independent event by applying the multiplication rule, resulting in a probability of 1/36.', 'The calculation of the probability of drawing a queen and an ace from a deck of cards is explained using the concept of conditional probability and the total number of cards in the deck. Probability of queen: 4/52, Probability of ace given queen: 4/51', "The importance of conditional probability in Bayes' theorem and machine learning algorithms is emphasized for understanding and application in probability calculations.", 'It also presents an example of a dependent event by calculating the probability of drawing a queen and then an ace from a deck of cards, highlighting the reduction in the deck as a factor in determining dependence.']}, {'end': 3850.597, 'segs': [{'end': 3572.881, 'src': 'embed', 'start': 3495.686, 'weight': 0, 'content': [{'end': 3503.55, 'text': 'Clear everybody? Shall I continue? Okay.', 'start': 3495.686, 'duration': 7.864}, {'end': 3509.234, 'text': "Now let's say that, first of all, let's discuss about permutation.", 'start': 3505.611, 'duration': 3.623}, {'end': 3518.839, 'text': "Let's say that I have taken some students to a school trip.", 'start': 3510.854, 'duration': 7.985}, {'end': 3531.081, 'text': 'and then we have gone to something like a chocolate factory in which many chocolates are basically they, they create a lot of chocolates.', 'start': 3521.377, 'duration': 9.704}, {'end': 3535.323, 'text': 'they they.', 'start': 3531.081, 'duration': 4.242}, {'end': 3537.584, 'text': 'okay. so they, they make a lot of chocolates.', 'start': 3535.323, 'duration': 2.261}, {'end': 3544.787, 'text': "okay. so i i catch a world of a student and i say that, okay, i'll give you an assignment.", 'start': 3537.584, 'duration': 7.203}, {'end': 3557.078, 'text': "okay, i'll give you an assignment And let's say that in this chocolate factory, six different types of chocolates are created, like dairy milk,", 'start': 3544.787, 'duration': 12.291}, {'end': 3560.558, 'text': 'right?, Like five star milky bar.', 'start': 3557.078, 'duration': 3.48}, {'end': 3564.739, 'text': "Okay And let's say eclairs.", 'start': 3562.579, 'duration': 2.16}, {'end': 3566.82, 'text': 'Okay Jam.', 'start': 3565.999, 'duration': 0.821}, {'end': 3569.7, 'text': 'How many? One, two, three, four, five.', 'start': 3567.92, 'duration': 1.78}, {'end': 3572.881, 'text': 'And one more chocolate, uh, normal toffee.', 'start': 3570.32, 'duration': 2.561}], 'summary': 'Discussion on permutation and 6 types of chocolates at a factory.', 'duration': 77.195, 'max_score': 3495.686, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xTLfLtqGsyg/pics/xTLfLtqGsyg3495686.jpg'}, {'end': 3708.403, 'src': 'embed', 'start': 3633.321, 'weight': 3, 'content': [{'end': 3640.264, 'text': 'Now, in the first instance, how many different options this particular student can have of seeing the chocolates?', 'start': 3633.321, 'duration': 6.943}, {'end': 3644.485, 'text': 'He may definitely have six different options right?', 'start': 3640.924, 'duration': 3.561}, {'end': 3654.617, 'text': 'Now, once he sees probably any one chocolate right? He may have six options, because six different any chocolate he may see right?', 'start': 3645.908, 'duration': 8.709}, {'end': 3658.842, 'text': 'So obviously he may have six options, out of which he writes one name over here.', 'start': 3655.158, 'duration': 3.684}, {'end': 3663.566, 'text': "Let's say in the next instance, how many chocolates will remain?", 'start': 3660.123, 'duration': 3.443}, {'end': 3666.008, 'text': 'Total 5 will remain right?', 'start': 3664.247, 'duration': 1.761}, {'end': 3667.61, 'text': 'So how many options?', 'start': 3666.629, 'duration': 0.981}, {'end': 3668.811, 'text': 'he will have to write the name?', 'start': 3667.61, 'duration': 1.201}, {'end': 3672.174, 'text': '5. he will have to write the name of the chocolate.', 'start': 3668.831, 'duration': 3.343}, {'end': 3678.7, 'text': 'Then finally, here you will be seeing that when he comes and write the third name over there, they will be having 4 options.', 'start': 3673.175, 'duration': 5.525}, {'end': 3686.673, 'text': 'Now if I try to multiply this, 6 multiplied by 5 multiplied by 4, it is nothing but 120.', 'start': 3679.581, 'duration': 7.092}, {'end': 3693.736, 'text': 'Now 120 what it is? It is all the possible permutations with respect to the chocolate name that he may see.', 'start': 3686.673, 'duration': 7.063}, {'end': 3697.258, 'text': 'Okay? All the possible permutations.', 'start': 3694.997, 'duration': 2.261}, {'end': 3702.741, 'text': 'Like he may see in this way, dairy milk, gems, milky bar.', 'start': 3697.478, 'duration': 5.263}, {'end': 3708.403, 'text': 'He may also see in different way, milky bar, gem, dairy milk.', 'start': 3704.141, 'duration': 4.262}], 'summary': 'The student has 6 options to see chocolates, leading to 120 permutations.', 'duration': 75.082, 'max_score': 3633.321, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xTLfLtqGsyg/pics/xTLfLtqGsyg3633321.jpg'}, {'end': 3761.449, 'src': 'embed', 'start': 3731.82, 'weight': 4, 'content': [{'end': 3736.942, 'text': 'These are all things I started studying when I was preparing for data science.', 'start': 3731.82, 'duration': 5.122}, {'end': 3740.444, 'text': 'Okay Before this visualized way, I used to not study.', 'start': 3737.022, 'duration': 3.422}, {'end': 3750.881, 'text': "Okay So permutation formula, how do you write? Now let's go back to school days, we are directly used to ratify all the formulas.", 'start': 3740.784, 'duration': 10.097}, {'end': 3757.566, 'text': 'nPr is equal to n factorial divided by n minus r factorial.', 'start': 3752.402, 'duration': 5.164}, {'end': 3761.449, 'text': 'Over here n is nothing but the total number of chocolates.', 'start': 3759.027, 'duration': 2.422}], 'summary': 'Preparing for data science, learned permutation formula: npr = n!/ (n-r)!', 'duration': 29.629, 'max_score': 3731.82, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xTLfLtqGsyg/pics/xTLfLtqGsyg3731820.jpg'}, {'end': 3823.749, 'src': 'embed', 'start': 3795.314, 'weight': 5, 'content': [{'end': 3797.575, 'text': 'Just give me a quick yes if you have understood till here.', 'start': 3795.314, 'duration': 2.261}, {'end': 3802.796, 'text': 'Okay? Now, this is with respect to permutation.', 'start': 3798.655, 'duration': 4.141}, {'end': 3807.237, 'text': 'Now, how does combination come into existence now?', 'start': 3803.956, 'duration': 3.281}, {'end': 3810.398, 'text': 'And what is the difference between permutation and combination?', 'start': 3807.997, 'duration': 2.401}, {'end': 3815.067, 'text': 'Now, in combination, always understand permutation.', 'start': 3811.806, 'duration': 3.261}, {'end': 3820.848, 'text': 'if I have the same element like this, I have dairy milk, I have gems.', 'start': 3815.067, 'duration': 5.781}, {'end': 3822.508, 'text': 'okay?. I have gems.', 'start': 3820.848, 'duration': 1.66}, {'end': 3823.749, 'text': 'I have probably eclairs.', 'start': 3822.508, 'duration': 1.241}], 'summary': 'Explaining permutation and combination with examples.', 'duration': 28.435, 'max_score': 3795.314, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xTLfLtqGsyg/pics/xTLfLtqGsyg3795314.jpg'}], 'start': 3420.996, 'title': 'Permutation & combination in a chocolate factory', 'summary': 'Discusses permutation and combination using a school trip to a chocolate factory as an example, highlighting the creation of six different types of chocolates, the process of selecting chocolates, and the demonstration of 120 possible permutations.', 'chapters': [{'end': 3607.49, 'start': 3420.996, 'title': 'Permutation & combination: school trip chocolates', 'summary': 'Discusses permutation and combination using a school trip to a chocolate factory as an example, highlighting the creation of six different types of chocolates and the assignment given to a student.', 'duration': 186.494, 'highlights': ['The chapter discusses the concept of permutation and combination using an example of a school trip to a chocolate factory where six different types of chocolates are created.', 'The assignment involves the selection of different types of chocolates from the factory, providing a practical application of permutation and combination concepts.', 'The example highlights the creation of six different types of chocolates, including Dairy Milk, Five Star, Milky Bar, Eclairs, Jam, and a normal toffee.', 'The instructor engages the students by incorporating humor and personal anecdotes, creating a lively and interactive learning environment.']}, {'end': 3678.7, 'start': 3608.45, 'title': 'Diary of chocolates in the factory', 'summary': 'Explores the process of selecting chocolates in a factory, where a student has 6 options initially, which reduces to 5 and then 4 as he selects and writes down the names of the chocolates he sees.', 'duration': 70.25, 'highlights': ['The student initially has 6 different options of seeing the chocolates in the factory, which reduces to 5 and then 4 as he selects and writes down the names of the chocolates he sees.', 'At the first instance, the student can have six different options of seeing the chocolates in the factory.', 'The number of options for the student reduces to 5 and then 4 as he selects and writes down the names of the chocolates he sees.']}, {'end': 3850.597, 'start': 3679.581, 'title': 'Permutation and combination explained', 'summary': 'Explains the concept of permutation and combination using the example of chocolate names, demonstrating that there are 120 possible permutations and highlighting the difference between permutation and combination.', 'duration': 171.016, 'highlights': ['Permutation results in 120 possible options for arranging chocolate names The 6 multiplied by 5 multiplied by 4 equals 120, representing all the possible permutations of arranging the chocolate names, such as dairy milk, gems, and milky bar.', 'Demonstration of permutation formula nPr = n! / (n-r)! The formula nPr = n factorial divided by n minus r factorial is explained using the example of arranging chocolates, resulting in a simple calculation to obtain the total answer of 120.', 'Explanation of combination as unique arrangements without repetition The difference between permutation and combination is illustrated, emphasizing that in combination, the same element cannot be reused to form a different arrangement, resulting in unique combinations.']}], 'duration': 429.601, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xTLfLtqGsyg/pics/xTLfLtqGsyg3420996.jpg', 'highlights': ['The example highlights the creation of six different types of chocolates, including Dairy Milk, Five Star, Milky Bar, Eclairs, Jam, and a normal toffee.', 'The assignment involves the selection of different types of chocolates from the factory, providing a practical application of permutation and combination concepts.', 'The instructor engages the students by incorporating humor and personal anecdotes, creating a lively and interactive learning environment.', 'Permutation results in 120 possible options for arranging chocolate names The 6 multiplied by 5 multiplied by 4 equals 120, representing all the possible permutations of arranging the chocolate names, such as dairy milk, gems, and milky bar.', 'Demonstration of permutation formula nPr = n! / (n-r)! The formula nPr = n factorial divided by n minus r factorial is explained using the example of arranging chocolates, resulting in a simple calculation to obtain the total answer of 120.', 'Explanation of combination as unique arrangements without repetition The difference between permutation and combination is illustrated, emphasizing that in combination, the same element cannot be reused to form a different arrangement, resulting in unique combinations.', 'The student initially has 6 different options of seeing the chocolates in the factory, which reduces to 5 and then 4 as he selects and writes down the names of the chocolates he sees.', 'At the first instance, the student can have six different options of seeing the chocolates in the factory.', 'The number of options for the student reduces to 5 and then 4 as he selects and writes down the names of the chocolates he sees.']}, {'end': 4695.031, 'segs': [{'end': 3876.729, 'src': 'embed', 'start': 3850.597, 'weight': 0, 'content': [{'end': 3858.66, 'text': 'you have a other formula which will actually help you to focus on the uniqueness of the objects that you are picking up.', 'start': 3850.597, 'duration': 8.063}, {'end': 3868.344, 'text': 'Okay, so for this, the formula is nCr, which is nothing but n factorial divided by r factorial, n minus r factorial.', 'start': 3858.94, 'duration': 9.404}, {'end': 3871.045, 'text': 'What is n factorial? You know that.', 'start': 3869.404, 'duration': 1.641}, {'end': 3876.729, 'text': 'The 6 factorial, what is r factorial? 3 factorial and 6 minus 3 factorial.', 'start': 3871.546, 'duration': 5.183}], 'summary': 'Use ncr formula to focus on object uniqueness.', 'duration': 26.132, 'max_score': 3850.597, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xTLfLtqGsyg/pics/xTLfLtqGsyg3850597.jpg'}, {'end': 4006.323, 'src': 'embed', 'start': 3960.993, 'weight': 3, 'content': [{'end': 3969.94, 'text': "Okay Let's say, first of all, the first topic that we are probably going to discuss about is something called as P-value.", 'start': 3960.993, 'duration': 8.947}, {'end': 3974.483, 'text': 'Super super important topic, many people gets confused.', 'start': 3971.121, 'duration': 3.362}, {'end': 3978.886, 'text': 'Gets confused.', 'start': 3977.606, 'duration': 1.28}, {'end': 3982.129, 'text': 'In this.', 'start': 3981.869, 'duration': 0.26}, {'end': 3992.134, 'text': "Now, let's take one example.", 'start': 3989.972, 'duration': 2.162}, {'end': 3994.235, 'text': 'Everybody uses a laptop.', 'start': 3992.994, 'duration': 1.241}, {'end': 3997.337, 'text': 'Everybody uses a laptop.', 'start': 3996.036, 'duration': 1.301}, {'end': 4000.379, 'text': "Let's say that this is my laptop.", 'start': 3998.838, 'duration': 1.541}, {'end': 4002.1, 'text': 'This is my mouse pad.', 'start': 4001.02, 'duration': 1.08}, {'end': 4006.323, 'text': 'Okay? Right? This is my mouse pad.', 'start': 4003.021, 'duration': 3.302}], 'summary': 'Discussion on the importance of p-value in statistics, illustrated with a relatable example.', 'duration': 45.33, 'max_score': 3960.993, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xTLfLtqGsyg/pics/xTLfLtqGsyg3960993.jpg'}, {'end': 4279.755, 'src': 'heatmap', 'start': 4147.386, 'weight': 4, 'content': [{'end': 4150.13, 'text': 'Okay? I hope everybody understood this one.', 'start': 4147.386, 'duration': 2.744}, {'end': 4159.667, 'text': 'Every 100 times probably I touch this mouse pad, the probability of touching this region is 80 times.', 'start': 4152.322, 'duration': 7.345}, {'end': 4161.13, 'text': 'That is 80 percentage.', 'start': 4159.768, 'duration': 1.362}, {'end': 4167.475, 'text': 'Similarly, if I say my p-value over here is 0.01.', 'start': 4161.77, 'duration': 5.705}, {'end': 4172.339, 'text': 'What does this mean? Now tell me what does this mean? You specify me the answer.', 'start': 4167.475, 'duration': 4.864}, {'end': 4176.221, 'text': 'You specify me the answer.', 'start': 4174.921, 'duration': 1.3}, {'end': 4180.406, 'text': 'Right? You specify me the answer.', 'start': 4178.703, 'duration': 1.703}, {'end': 4191.277, 'text': 'If my p-value is 0.01, how many times probably I am actually touching over there? Similarly, you can consider any region.', 'start': 4181.988, 'duration': 9.289}, {'end': 4198.603, 'text': 'This region is the tippest, like broadest, right? So this region may be p is equal to 0.9.', 'start': 4191.357, 'duration': 7.246}, {'end': 4204.869, 'text': 'That basically means out of every 100 touches, I am basically touching 90 times over here.', 'start': 4198.603, 'duration': 6.266}, {'end': 4206.73, 'text': 'This will be one time.', 'start': 4205.549, 'duration': 1.181}, {'end': 4213.892, 'text': "This will be only one time, right? So I hope you're getting the understanding of P-value.", 'start': 4207.151, 'duration': 6.741}, {'end': 4221.993, 'text': 'P-value basically says, most of the time, what is the probability with respect to a P-value for that specific experiment.', 'start': 4213.952, 'duration': 8.041}, {'end': 4225.034, 'text': 'Okay This is very much simple for you all.', 'start': 4222.593, 'duration': 2.441}, {'end': 4232.675, 'text': "Perfect Now let's go ahead and let's understand something called as, now I'm going to combine multiple topics.", 'start': 4225.534, 'duration': 7.141}, {'end': 4237.636, 'text': "The first topic that I'm going to combine is something called as hypothesis testing.", 'start': 4233.435, 'duration': 4.201}, {'end': 4242.631, 'text': "In that I'm going to combine confidence interval.", 'start': 4240.447, 'duration': 2.184}, {'end': 4249.081, 'text': "In that I'm going to combine significance value.", 'start': 4246.697, 'duration': 2.384}, {'end': 4256.334, 'text': "In that I'm going to combine many things.", 'start': 4254.713, 'duration': 1.621}, {'end': 4260.178, 'text': 'Okay So like this, we will try to understand things.', 'start': 4256.475, 'duration': 3.703}, {'end': 4262.64, 'text': 'This is super important guys.', 'start': 4261.119, 'duration': 1.521}, {'end': 4266.203, 'text': "If you have not probably attended the first hour session, it's okay.", 'start': 4262.72, 'duration': 3.483}, {'end': 4267.624, 'text': 'Understand this.', 'start': 4266.924, 'duration': 0.7}, {'end': 4269.306, 'text': 'This is super, super important.', 'start': 4267.724, 'duration': 1.582}, {'end': 4271.648, 'text': 'Definitely helpful for your.', 'start': 4269.926, 'duration': 1.722}, {'end': 4275.431, 'text': 'Okay For your interviews.', 'start': 4273.71, 'duration': 1.721}, {'end': 4279.755, 'text': "Okay Now let's say I am solving a problem.", 'start': 4275.812, 'duration': 3.943}], 'summary': 'P-value indicates probability of touching specific region; discusses hypothesis testing, confidence interval, and significance value.', 'duration': 33.02, 'max_score': 4147.386, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xTLfLtqGsyg/pics/xTLfLtqGsyg4147386.jpg'}, {'end': 4475.82, 'src': 'embed', 'start': 4449.916, 'weight': 5, 'content': [{'end': 4455.401, 'text': 'First of all, in this particular scenario, we have to focus on something called as hypothesis testing.', 'start': 4449.916, 'duration': 5.485}, {'end': 4459.484, 'text': 'You have to focus on hypothesis testing.', 'start': 4457.883, 'duration': 1.601}, {'end': 4464.729, 'text': 'In hypothesis testing, the first thing is that we need to define our null hypothesis.', 'start': 4460.225, 'duration': 4.504}, {'end': 4472.479, 'text': 'The null hypothesis is usually given in the problem statement.', 'start': 4467.358, 'duration': 5.121}, {'end': 4475.82, 'text': 'We want to test whether the coin is a fair coin or not.', 'start': 4473.259, 'duration': 2.561}], 'summary': 'Focus on hypothesis testing to determine fairness of a coin.', 'duration': 25.904, 'max_score': 4449.916, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xTLfLtqGsyg/pics/xTLfLtqGsyg4449916.jpg'}], 'start': 3850.597, 'title': 'Combinations formula and p-value', 'summary': 'Introduces the ncr formula for unique combinations and provides an example of 20 unique combinations from 6 objects taken 3 at a time. it also explains the concept of p-value, its significance in probability, and its role in hypothesis testing.', 'chapters': [{'end': 3913.689, 'start': 3850.597, 'title': 'Combinations formula for uniqueness', 'summary': 'Introduces the ncr formula to calculate unique combinations, illustrating an example of 20 unique combinations resulting from 6 objects taken 3 at a time using the formula ncr = n! / (r! * (n-r)!).', 'duration': 63.092, 'highlights': ['The nCr formula is introduced to calculate unique combinations, exemplifying 20 unique combinations from 6 objects taken 3 at a time using nCr = n! / (r! * (n-r)!) formula.', 'Explanation of the nCr formula and its application, demonstrating the calculation of 20 unique combinations from 6 objects taken 3 at a time through detailed step-by-step computation.', 'Illustration of the calculation process using the nCr formula to obtain 20 unique combinations from 6 objects taken 3 at a time, providing a clear understanding of the concept and its practical application.']}, {'end': 4695.031, 'start': 3914.589, 'title': 'Understanding p-value and hypothesis testing', 'summary': 'Discusses the concept of p-value, explaining its significance and interpretation in terms of probability, and then delves into hypothesis testing, emphasizing the importance of defining null and alternate hypotheses and the process of accepting or rejecting the null hypothesis based on experimental results.', 'duration': 780.442, 'highlights': ['P-value indicates the probability of a specific event occurring, such as touching a particular area on a laptop mouse pad, with examples of P-values like 0.8 and 0.01 provided for better comprehension. P-value examples: 0.8, 0.01', 'The concept of P-value is illustrated by relating it to the probability of touching different regions on a laptop mouse pad, where a higher P-value, such as 0.9, corresponds to a higher probability of touching a specific area. P-value example: 0.9', "The explanation of P-value's significance includes a practical example of touching a laptop mouse pad, connecting the concept to real-world scenarios for better understanding. ", 'The chapter transitions to discussing hypothesis testing, emphasizing the importance of defining null and alternate hypotheses and the process of accepting or rejecting the null hypothesis based on experimental results, using the example of testing whether a coin is fair through 100 tosses. Example: Testing fairness of a coin through 100 tosses']}], 'duration': 844.434, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xTLfLtqGsyg/pics/xTLfLtqGsyg3850597.jpg', 'highlights': ['Illustration of the calculation process using the nCr formula to obtain 20 unique combinations from 6 objects taken 3 at a time, providing a clear understanding of the concept and its practical application.', 'Explanation of the nCr formula and its application, demonstrating the calculation of 20 unique combinations from 6 objects taken 3 at a time through detailed step-by-step computation.', 'The nCr formula is introduced to calculate unique combinations, exemplifying 20 unique combinations from 6 objects taken 3 at a time using nCr = n! / (r! * (n-r)!) formula.', "The explanation of P-value's significance includes a practical example of touching a laptop mouse pad, connecting the concept to real-world scenarios for better understanding.", 'The concept of P-value is illustrated by relating it to the probability of touching different regions on a laptop mouse pad, where a higher P-value, such as 0.9, corresponds to a higher probability of touching a specific area.', 'The chapter transitions to discussing hypothesis testing, emphasizing the importance of defining null and alternate hypotheses and the process of accepting or rejecting the null hypothesis based on experimental results, using the example of testing whether a coin is fair through 100 tosses.']}, {'end': 5484.885, 'segs': [{'end': 4776.66, 'src': 'embed', 'start': 4734.008, 'weight': 0, 'content': [{'end': 4739.129, 'text': 'Which is called as significance value.', 'start': 4734.008, 'duration': 5.121}, {'end': 4745.329, 'text': 'Now this significance value is basically given by alpha.', 'start': 4741.147, 'duration': 4.182}, {'end': 4749.111, 'text': "Suppose let's consider that I am considering alpha as 0.05.", 'start': 4745.669, 'duration': 3.442}, {'end': 4754.073, 'text': 'Okay, 0.05.', 'start': 4749.111, 'duration': 4.962}, {'end': 4759.896, 'text': 'Now this 0.05, what exactly it is? What exactly it actually means? Okay.', 'start': 4754.073, 'duration': 5.823}, {'end': 4771.278, 'text': "This means that if I do 1 minus 0.05, this answer, Let's say that this answer how much it will come?", 'start': 4760.636, 'duration': 10.642}, {'end': 4776.66, 'text': 'It will basically come as okay.', 'start': 4772.418, 'duration': 4.242}], 'summary': 'Significance value alpha is 0.05, indicating 95% confidence level.', 'duration': 42.652, 'max_score': 4734.008, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xTLfLtqGsyg/pics/xTLfLtqGsyg4734008.jpg'}, {'end': 4981.665, 'src': 'embed', 'start': 4951.16, 'weight': 1, 'content': [{'end': 4956.543, 'text': 'So if I get 10 heads, which region it is falling? It will fall somewhere here.', 'start': 4951.16, 'duration': 5.383}, {'end': 4959.605, 'text': 'It is not inside the confidence interval.', 'start': 4957.964, 'duration': 1.641}, {'end': 4964.188, 'text': 'So we can definitely say that coin is not fair.', 'start': 4960.526, 'duration': 3.662}, {'end': 4969.911, 'text': 'So for that particular case, we reject the null hypothesis and we accept the alternate hypothesis.', 'start': 4964.968, 'duration': 4.943}, {'end': 4977.941, 'text': 'I hope everybody is able to understand the terminologies that we are using over here.', 'start': 4971.275, 'duration': 6.666}, {'end': 4981.665, 'text': 'I cannot teach you separate topics.', 'start': 4979.623, 'duration': 2.042}], 'summary': 'Coin is not fair as it falls outside the confidence interval. null hypothesis rejected.', 'duration': 30.505, 'max_score': 4951.16, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xTLfLtqGsyg/pics/xTLfLtqGsyg4951160.jpg'}, {'end': 5206.62, 'src': 'embed', 'start': 5175.607, 'weight': 2, 'content': [{'end': 5178.568, 'text': "So let's say that your alpha is 0.20.", 'start': 5175.607, 'duration': 2.961}, {'end': 5179.948, 'text': 'Now, what will be your confidence interval?', 'start': 5178.568, 'duration': 1.38}, {'end': 5183.301, 'text': 'What will be your confidence interval?', 'start': 5181.92, 'duration': 1.381}, {'end': 5189.066, 'text': "Let's say that your confidence interval will be now 80% instead of 95%.", 'start': 5183.822, 'duration': 5.244}, {'end': 5191.468, 'text': 'So now your graph will look somewhere here like this.', 'start': 5189.066, 'duration': 2.402}, {'end': 5193.89, 'text': 'It will be still more in this side.', 'start': 5191.748, 'duration': 2.142}, {'end': 5196.852, 'text': 'So this side will basically have 0.10.', 'start': 5194.33, 'duration': 2.522}, {'end': 5199.254, 'text': 'This side will basically have 0.10.', 'start': 5196.852, 'duration': 2.402}, {'end': 5202.176, 'text': 'And this all will be your 0.80%.', 'start': 5199.254, 'duration': 2.922}, {'end': 5206.62, 'text': 'When you combine all this, when you add up all this, it will be 1.', 'start': 5202.176, 'duration': 4.444}], 'summary': "With alpha at 0.20, changing confidence interval to 80% impacts the graph's distribution.", 'duration': 31.013, 'max_score': 5175.607, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xTLfLtqGsyg/pics/xTLfLtqGsyg5175607.jpg'}, {'end': 5262.432, 'src': 'embed', 'start': 5228.346, 'weight': 3, 'content': [{'end': 5231.268, 'text': 'I have just considered standard deviation as 10.', 'start': 5228.346, 'duration': 2.922}, {'end': 5233.429, 'text': 'I told you, right? I am assuming some values.', 'start': 5231.268, 'duration': 2.161}, {'end': 5242.514, 'text': 'Here the main aim is to understand, to make you find out, the relationship between significance, value, confidence intervals.', 'start': 5234.11, 'duration': 8.404}, {'end': 5243.855, 'text': 'what is hypothesis testing?', 'start': 5242.514, 'duration': 1.341}, {'end': 5245.356, 'text': 'these all are integrated together.', 'start': 5243.855, 'duration': 1.501}, {'end': 5255.281, 'text': 'Right?. Now, tell me, tell me one thing if your alpha value is 0.3, what is your confidence interval?', 'start': 5247.016, 'duration': 8.265}, {'end': 5262.432, 'text': 'Vishwashwaram, I just took it for heads only right?', 'start': 5260.151, 'duration': 2.281}], 'summary': 'The aim is to understand the relationship between significance, value, confidence intervals, and hypothesis testing, with an alpha value of 0.3 and standard deviation of 10.', 'duration': 34.086, 'max_score': 5228.346, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xTLfLtqGsyg/pics/xTLfLtqGsyg5228346.jpg'}], 'start': 4696.352, 'title': 'Significance value and confidence intervals', 'summary': 'Explains the significance value (alpha) as 0.05, determining a 95% confidence interval and its impact on hypothesis testing, highlighting the rejection of null hypothesis when falling outside the confidence interval. it also discusses the relationship between significance value, confidence intervals, and hypothesis testing, emphasizing the upcoming topics on confidence interval calculation and various tests.', 'chapters': [{'end': 5148.075, 'start': 4696.352, 'title': 'Understanding significance value and confidence interval', 'summary': 'Explains the concept of significance value (alpha) as 0.05, which determines the confidence interval of 95% and its implications on hypothesis testing, emphasizing that falling outside the confidence interval leads to rejection of the null hypothesis.', 'duration': 451.723, 'highlights': ['The significance value, often denoted as alpha, is crucial in defining the confidence interval, with alpha set at 0.05, corresponding to a 95% confidence interval.', 'Falling outside the 95% confidence interval leads to the rejection of the null hypothesis, as demonstrated by getting 10 heads out of 100 experiments, indicating that the coin is not fair.', 'Explaining the relationship between significance value, confidence interval, and hypothesis testing, emphasizing the rejection of the null hypothesis if the observed outcome falls outside the confidence interval.', 'Clarification on the distinction between significance value and p-value, with emphasis on the significance value determining what should be within the confidence interval.']}, {'end': 5484.885, 'start': 5148.315, 'title': 'Understanding confidence intervals and hypothesis testing', 'summary': "Discusses the relationship between significance value, confidence intervals, and hypothesis testing, emphasizing the impact of alpha value on confidence intervals and the upcoming topics on confidence interval calculation and various tests. it also emphasizes the importance of tomorrow's session and encourages participation and revision.", 'duration': 336.57, 'highlights': ['The chapter emphasizes the impact of alpha value on confidence intervals, explaining that a higher alpha value leads to a wider confidence interval and vice versa, with examples such as 0.05 resulting in a 95% confidence interval and 0.20 resulting in an 80% confidence interval.', 'The speaker encourages revision and emphasizes the importance of the upcoming session on confidence interval calculation and various tests, highlighting its significance in understanding the concept.', "The chapter underlines the importance of tomorrow's session, indicating it will cover crucial topics such as type 1 and type 2 errors, one tail, two tail tests, one sample Z test, one sample T test, dependent sample T test, Z test for proportion, and ANOVA test.", "The speaker concludes by encouraging active participation, highlighting the significance of the upcoming sessions, and urging the audience to prepare and revise for the final day's discussion on distributions."]}], 'duration': 788.533, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/xTLfLtqGsyg/pics/xTLfLtqGsyg4696352.jpg', 'highlights': ['The significance value, often denoted as alpha, is crucial in defining the confidence interval, with alpha set at 0.05, corresponding to a 95% confidence interval.', 'Falling outside the 95% confidence interval leads to the rejection of the null hypothesis, as demonstrated by getting 10 heads out of 100 experiments, indicating that the coin is not fair.', 'The chapter emphasizes the impact of alpha value on confidence intervals, explaining that a higher alpha value leads to a wider confidence interval and vice versa, with examples such as 0.05 resulting in a 95% confidence interval and 0.20 resulting in an 80% confidence interval.', 'Explaining the relationship between significance value, confidence interval, and hypothesis testing, emphasizing the rejection of the null hypothesis if the observed outcome falls outside the confidence interval.']}], 'highlights': ['Covers advanced statistics in python for data science, including iqr implementation, probability, permutation, combination, outlier detection using z score, visualization, probability in machine learning, conditional events, permutation & combination, combinations formula, p-value, significance value, and confidence intervals, with practical examples and python implementation.', 'The chapter emphasizes the process of detecting outliers using Z score and standard deviation.', 'The process of finding the lower fence involves using the formula Q1 - 1.5 * IQR, while the upper fence is determined using the formula Q3 + 1.5 * IQR.', 'Probability is crucial in machine learning, aiding in predictions and decisions.', 'The chapter explains the probability of rolling a 5 and then a 4 on a dice, calculating the independent event by applying the multiplication rule, resulting in a probability of 1/36.', 'The example highlights the creation of six different types of chocolates, including Dairy Milk, Five Star, Milky Bar, Eclairs, Jam, and a normal toffee.', 'Illustration of the calculation process using the nCr formula to obtain 20 unique combinations from 6 objects taken 3 at a time, providing a clear understanding of the concept and its practical application.', 'The significance value, often denoted as alpha, is crucial in defining the confidence interval, with alpha set at 0.05, corresponding to a 95% confidence interval.']}