Sequence Model

 Z stands for Standard Normal Distributions. Its Fairly important in real life . Japan use Z- Score on exam to estimate each student study skills. Z score used to convert Gaussian Distribution into Standard Normal Distributions Z-score is essential concept of Z-statistics  Refer to Wiki: Standard score Refer to Khan academy: Z-score introduction Refer to youtube: Why Do We Need z Scores Refer to youtube: Statistics 101: Understanding Z-scores Refer to Crash Course: Z-Scores and Percentiles: Crash Course Statistics #18 Refer to youtube: z-score Calculations & Percentiles in a Normal Distribution Z-Score is all about comparison compare different kind of data set . In another word ,Z score indicates How many standard deviations away (aboce or below) from the mean to hte given point  Why do we need Z_scores.? “Z-scores in general allow us to compare things that are NOT in the same scale, as long as they are NORMALLY distributed.” — CrashCourse For example, although we kno...

Random variables  Discrete Random Variables                             In Discrete Random Varaibles we have Whole number of data Like , Roll no , no of students. Those numbers which can't be float are Discrete Random Variables   Continuous Random variables  In continuous Random variables we have continuous value which define some Range 0-10 ,3-8 etc. so It can be any value from the particular range ex . from Range 0-10 ,it can be 5.4,6.7 etc. Gaussian Distribution / Normal Distribution  Gaussian or Normal Distribution is very common term in statistics. These are generally used to represent random variables which coming into Machine Learning we can say which is something like the error when we dont know the weight vector for our Linear Regression Model. In a Gaussian distribution the more data near to the mean and is like a bell curve in general We have two main paramters to explain or in...

Population Mean    Basically in population mean we divide the whole sum with the total number of N        Mean           Median                (These all are Measure of Tendency)      Mode  Sample Mean Taking Sample from population like n example.. taking out some namkeen from the packet  Random Variable              Discreted Random Variable              Continuous Random Variable 

 SLIDE 1 What is SVM? “Support Vector Machine” (SVM) is a supervised  machine learning algorithm  which can be used for both classification or regression challenges. However,  it is mostly used in classification problems. SLIDE 2 how does it work ? Identify the right hyper-plane (Scenario-1): SLIDE3: Identify the right hyper-plane (Scenario-2):  SLIDE4: Here, maximizing the distances between nearest data point (either class) and hyper-plane will help us to decide the right hyper-plane. This distance is called as  Margin SLIDE5: Identify the right hyper-plane (Scenario-3): SLIDE6: Find the hyper-plane to segregate to classes  SLIDE  Cost function and Gradient Function In the SVM algorithm, we are looking to maximize the margin between the data points and the hyperplane. The loss function that helps maximize the margin is hinge loss. SLIDE  Regularization The objective of the regularization parameter is to balance the margin maximizat...

SLIDE 1 What is SVM?   “Support Vector Machine” (SVM) is a supervised  machine learning algorithm  which can be used for both classification or regression challenges. However,  it is mostly used in classification problems. In the SVM algorithm, we plot each data item as a point in n-dimensional space (where n is number of features you have) with the value of each feature being the value of a particular coordinate. The SVM classifier is a frontier which best segregates the two classes (hyper-plane/ line). SLIDE 2 Identify the right hyper-plane (Scenario-1):  Here, we have three hyper-planes (A, B and C). Now, identify the right hyper-plane to classify star and circle. emember a thumb rule to identify the right hyper-plane: “Select the hyper-plane which segregates the two classes better”. In this scenario, hyper-plane “B” has excellently performed this job. SLIDE 3: Here, we have three hyper-planes (A, B and C) and all are segregating the class...