Wednesday, August 25, 2021

Samsung Z Fold 3


Samsung Galaxy Z Fold3 5G (Phantom Black, 12GB RAM, 256GB Storage) with No Cost EMI/Additional Exchange Offers - ₹1,49,999.00


Model NameSamsung Galaxy Z Fold3 5G
Wireless CarrierUnlocked for All Carriers
BrandSamsung
Form factorFolder
Memory Storage Capacity256 GB



 

About this item

  • 5G Ready powered by Qualcomm Snapdragon 888 Octa-Core processor, 12GB RAM, 256GB internal memory (UFS 3.1) , Android operating system and dual SIM (one nano Sim & one e-Sim)
  • Triple Rear Camera Setup - 12MP Ultra Wide Camera: F2.2, Pixel size: 1.12µm, FOV: 123˚ + 12MP Wide-angle Camera: F1.8 OIS Dual Pixel AF, Pixel size: 1.8µm + 12MP Telephoto Camera: F2.4 OIS 2x Zoom, Pixel size: 1.0µm | Cover Camera - 10MP Selfie Camera: F2.2 FF, Pixel size: 1.22µm | Front Camera - 4MP Under Display Camera: F1.8 FF, Pixel size: 2.0µm
  • Main Display - 7.6” (2,208x1,768), 374ppi, Dynamic AMOLED 2X Display (22.5:18) Infinity Flex Display, Adaptive 120Hz | Cover Display - 6.2” (2,268x832), 387ppi, Dynamic AMOLED 2X Display (24.5:9) Infinity-O Display, Switchable 60/120Hz
  • 4400mAh dual battery (Non-removable) with Fast Wired & Wireless charging, Wireless PowerShare & Fingerprint Sensor (side)
  • IPX8 Rated, Dual Sim (1 Nano Sim + 1 eSim)


Technical Details


Additional Information






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Thursday, August 12, 2021

Self defense training (“for women” or “for children” or “for business executives”)

 

Self-defense training (“for women” or “for children” or “for business executives”)



Parents and close family members wishing to empower their children to identify potential danger, act with confident independence to get to safety, and if necessary know how to physically protect themselves, commonly asked what the recommended minimum age range for the Basic Model Mugging Self Defense course.

We have divided the age groups at 16 and and 12 where self defense course content is adjusted. There is more involved with this subject than simple martial arts skills whereby the mind, body, and emotions must be developed simultaneously. Parents must take into account the mental-emotional maturity and physical development of their child when determining appropriateness. If the child is a survivor of molestation, incest, sexual battery, rape or other violence, should also be considered with the assistance of a psychologist when making the decision for enrollment.

Young women between the ages of 16 and 18 are at a high threat of being raped and targeted for abusive intimate relationships. They have not developed life experience while beginning to independently socialize and date with increasing availability for predators to target them. The violence inflicted upon 14, 15, or 16 year old girls can be similar for any adult woman. Assailants will not adjust their assaults to the age of their victims. Occasionally younger girls are horrifically victimized.


Self Defense for Girls Older Than 16

The basic self defense course presentation is designed for mid to late teens and women; 16 and above. Depending on the age range of the overall group we may adjust the course material for younger teenagers and then recommend they take the basic course for women when they are age 16. However due to personal situations, maturity, and early physical development there are situations that warrant admitting younger teens into the self defense class for women. Younger teens have taken the basic self defense for women course and performed wonderfully. We request a mother, guardian, or other close female adult attend the self defense training with girls who are under 16 years of age. Each parent and teenager must access their comfort and ability levels. Fighting intensity is adjusted to the ability of each student to ensure safety and progressive development of skills.

Understanding the context in which girls-women could be subjected to transfers over to their fighting abilities. We have found teenagers perform quite well with the course explanations that aid in their scenario negotiations and fighting spirit during the full force rape prevention scenarios. High school girls fight back exceptionally well when graduating from the self defense class showing no difference in performance and emotional strength than college age women. The course addresses issues of violence, sexuality, relationships, and strong emotions surrounding the subject or rape, domestic violence, and stalking. There are cases where all of these crimes have also affected teenagers.



Self defense techniques taught are no nonsense fighting skills geared for real world street violence. Overall, conflict avoidance is one of the most significant benefits of the Model Mugging self defense program. Graduates rarely have to use their physical skills. Most often their increased awareness and assertiveness skills carry over to stronger boundaries and reduce the risk of sexual assault.

Boundary setting and verbal de-escalation skills would be taught in a younger teen course so they would be able to recognize danger more clearly and rapidly in order to get to safety without having their fighting skills. Developing their fighting experience in class is what provides the foundational confidence to be proactive with safety and not wait for bad a situation to become worse.

Refer the Below video for the better clarification






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Data Science Interview Questions - Part-3

 Q21. What Are Confounding Variables?

Ans. In statistics, a confounder is a variable that influences both the dependent variable and independent variable. 

 For example, if you are researching whether a lack of exercise leads to weight gain, lack of exercise = independent variable weight gain = dependent variable. 

A confounding variable here would be any other variable that affects both of these variables, such as the age of the subject.

Q22. What Are the Types of Biases That Can Occur During Sampling?

Ans. 

• Selection bias 

• Under coverage bias 

• Survivorship bias 

Q23. What is Survivorship Bias?

Ans. It is the logical error of focusing aspects that support surviving some process and casually overlooking those that did not work because of their lack of prominence. This can lead to wrong conclusions in numerous different means.

Q24. What is selection Bias?

Ans. Selection bias occurs when the sample obtained is not representative of the population intended to be analysed.

Q25. Explain how a ROC curve works?

Ans. The ROC curve is a graphical representation of the contrast between true positive rates and false-positive rates at various thresholds. It is often used as a proxy for the trade-off between the sensitivity(true positive rate) and false-positive rate.


Q26. What is TF/IDF vectorization? 

Ans. TF–IDF is short for term frequency-inverse document frequency, is a numerical statistic that is intended to reflect how important a word is to a document in a collection or corpus. It is often used as a weighting factor in information retrieval and text mining. 

 The TF–IDF value increases proportionally to the number of times a word appears in the document but is offset by the frequency of the word in the corpus, which helps to adjust for the fact that some words appear more frequently in general.

Q27. Why we generally use Softmax non-linearity function as last operation in-network?

Ans. It is because it takes in a vector of real numbers and returns a probability distribution. Its definition is as follows. Let x be a vector of real numbers (positive, negative, whatever, there are no constraints). 

 Then the i’th component of Softmax(x) is —


It should be clear that the output is a probability distribution: each element is non-negative and the sum over all components is 1. 

Q28. Python or R – Which one would you prefer for text analytics?

Ans. We will prefer Python because of the following reasons

• Python would be the best option because it has Pandas library that provides easy to use data structures and high-performance data analysis tools. 

• R is more suitable for machine learning than just text analysis. 

 • Python performs faster for all types of text analytics.  

Q29. How does data cleaning plays a vital role in the analysis?

Ans. Data cleaning can help in analysis because: 

 • Cleaning data from multiple sources helps to transform it into a format that data analysts or data             scientists can work with. 
 • Data Cleaning helps to increase the accuracy of the model in machine learning. 
 • It is a cumbersome process because as the number of data sources increases, the time taken to clean       the data increases exponentially due to the number of sources and the volume of data generated by         these sources. 
 • It might take up to 80% of the time for just cleaning data making it a critical part of the analysis task.

Q30. Differentiate between univariate, bivariate and multivariate analysis. 

Ans. Univariate analyses are descriptive statistical analysis techniques which can be differentiated based on the number of variables involved at a given point of time. For example, the pie charts of sales based on territory involve only one variable and can the analysis can be referred to as univariate analysis. 

 The bivariate analysis attempts to understand the difference between two variables at a time as in a scatterplot. For example, analyzing the volume of sale and spending can be considered as an example of bivariate analysis. 

 Multivariate analysis deals with the study of more than two variables to understand the effect of variables on the responses


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Data Science Interview Question- Part-2

 Q11. In any 15-minute interval, there is a 20% probability that you will see at least one shooting star. What is the probability that you see at least one shooting star in the period of an hour?

Ans. Probability of not seeing any shooting star in 15 minutes is = 1 – P( Seeing one = 1 – 0.2 = 0.8  

Probability of not seeing any shooting star in the period of one hour = (0.8) ^ 4 = 0.4096 

 Probability of seeing at least one shooting star in the one hour shooting star  = 

                                                            1 – P( Not seeing)  = 1 – 0.4096 = 0.5904

Q12. How can you generate a random number between 1 – 7 with only a die?

Ans. 

• Any die has six sides from 1-6. There is no way to get seven equal outcomes from a single rolling of a        die. If we roll the die twice and consider the event of two rolls, we now have 36 different outcomes.
• To get our 7 equal outcomes we have to reduce this 36 to a number divisible by 7. We can thus consider     only 35 outcomes and exclude the other one.
• A simple scenario can be to exclude the combination (6,6), i.e., to roll the die again if 6 appears twice. 
• All the remaining combinations from (1,1) till (6,5) can be divided into 7 parts of 5 each. This way all t      he seven sets of outcomes are equally likely.

Q13. A certain couple tells you that they have two children, at least one of which is a girl. What is the probability that they have two girls?

Ans. In the case of two children, there are 4 equally likely possibilities BB, BG, GB and GG; 
where B = Boy and G = Girl and the first letter denotes the first child. 

 From the question, we can exclude the first case of BB. Thus from the remaining 3 possibilities of BG, GB & BB, we have to find the probability of the case with two girls.

 Thus, P(Having two girls given one girl) = 1 / 3

Q14. A jar has 1000 coins, of which 999 are fair and 1 is double headed. Pick a coin at random, and toss it 10 times. Given that you see 10 heads, what is the probability that the next toss of that coin is also a head?

Ans. There are two ways of choosing the coin. One is to pick a fair coin and the other is to pick the one with two heads.
 Probability of selecting fair coin = 999/1000 = 0.999 
Probability of selecting unfair coin = 1/1000 = 0.001 
Selecting 10 heads in a row = Selecting fair coin * Getting 10 heads + Selecting an unfair coin 
 P (A) = 0.999 * (1/2)^5 = 0.999 * (1/1024) = 0.000976 
P (B) = 0.001 * 1 = 0.001
 P( A / A + B ) = 0.000976 / (0.000976 + 0.001) = 0.4939 
P( B / A + B ) = 0.001 / 0.001976 = 0.5061 
Probability of selecting another head = P(A/A+B) * 0.5 + P(B/A+B) * 1 = 0.4939 * 0.5 + 0.5061 = 0.7531

Q15. What do you understand by statistical power of sensitivity and how do you calculate it?

Ans. Sensitivity is commonly used to validate the accuracy of a classifier (Logistic, SVM, Random Forest etc.). S
ensitivity is nothing but “Predicted True events/ Total events”. True events here are the events which were true and model also predicted them as true.
 Calculation of seasonality is pretty straightforward. 

 Seasonality = ( True Positives ) / ( Positives in Actual Dependent Variable )

Q16. Why Is Re-sampling Done?

Ans.  Resampling is done in any of these cases: 

 • Estimating the accuracy of sample statistics by using subsets of accessible data or drawing randomly     with replacement from a set of data points 
 • Substituting labels on data points when performing significance tests 
 • Validating models by using random subsets (bootstrapping, cross-validation)

Q17. What are the differences between over-fitting and under-fitting?

Ans. In statistics and machine learning, one of the most common tasks is to fit a model to a set of training data, so as to be able to make reliable predictions on general untrained data.

Follow Steve Nouri for more AI and Data science posts: https://lnkd.in/gZu463


In overfitting, a statistical model describes random error or noise instead of the underlying relationship. Overfitting occurs when a model is excessively complex, such as having too many parameters relative to the number of observations. A model that has been overfitted, has poor predictive performance, as it overreacts to minor fluctuations in the training data. 

 Underfitting occurs when a statistical model or machine learning algorithm cannot capture the underlying trend of the data. Underfitting would occur, for example, when fitting a linear model to non-linear data. Such a model too would have poor predictive performance. 

Q18. How to combat Overfitting and Underfitting?

Ans. To combat overfitting and underfitting, you can resample the data to estimate the model accuracy (k-fold cross-validation) and by having a validation dataset to evaluate the model.

Q19. What is regularisation? Why is it useful? 

Ans. Regularisation is the process of adding tuning parameter to a model to induce smoothness in order to prevent overfitting. This is most often done by adding a constant multiple to an existing weight vector. This constant  is often the L1(Lasso) or L2(ridge). The model predictions should then minimize the loss function calculated on the regularized training set.

Q20. What Is the Law of Large Numbers? 

Ans. It is a theorem that describes the result of performing the same experiment a large number of times. This theorem forms the basis of frequency-style thinking. It says that the sample means, the sample variance and the sample standard deviation converge to what they are trying to estimate.
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Data Science Interview Questions - Part-1

 Q1. What is Data Science? List the differences between supervised and unsupervised learning?

Ans. Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data. How is this different from what statisticians have been doing for years? The answer lies in the difference between explaining and predicting.

The differences between supervised and unsupervised learning are as follows;


Q2. What is Selection Bias?

Ans. Selection bias is a kind of error that occurs when the researcher decides who is going to be studied. It is usually associated with research where the selection of participants isn’t random. It is sometimes referred to as the selection effect. It is the distortion of statistical analysis, resulting from the method of collecting samples. If the selection bias is not taken into account, then some conclusions of the study may not be accurate. 

 The types of selection bias include: 

 1. Sampling bias: It is a systematic error due to a non-random sample of a population causing some members of the population to be less likely to be included than others resulting in a biased sample. 

 2. Time interval: A trial may be terminated early at an extreme value (often for ethical reasons), but the extreme value is likely to be reached by the variable with the largest variance, even if all variables have a similar mean. Follow Steve Nouri for more AI and Data science posts: https://lnkd.in/gZu463X

 3. Data: When specific subsets of data are chosen to support a conclusion or rejection of bad data on arbitrary grounds, instead of according to previously stated or generally agreed criteria.

 4. Attrition: Attrition bias is a kind of selection bias caused by attrition (loss of participants) discounting trial subjects/tests that did not run to completion.


Q3. What is bias-variance trade-off?

Ans. Bias: Bias is an error introduced in your model due to oversimplification of the machine learning algorithm. It can lead to underfitting. When you train your model at that time model makes simplified assumptions to make the target function easier to understand. 

 Low bias machine learning algorithms — Decision Trees, k-NN and SVM High bias machine learning algorithms — Linear Regression, Logistic Regression. 

 Variance: Variance is error introduced in your model due to complex machine learning algorithm, your model learns noise also from the training data set and performs badly on test data set. It can lead to high sensitivity and overfitting. 

 Normally, as you increase the complexity of your model, you will see a reduction in error due to lower bias in the model. However, this only happens until a particular point. As you continue to make your model more complex, you end up over-fitting your model and hence your model will start suffering from high variance.

Bias-Variance trade-off: The goal of any supervised machine learning algorithm is to have low bias and low variance to achieve good prediction performance. 

1. The k-nearest neighbour algorithm has low bias and high variance, but the trade-off can be changed by increasing the value of k which increases the number of neighbours that contribute to the prediction and in turn increases the bias of the model. 

2. The support vector machine algorithm has low bias and high variance, but the trade-off can be changed by increasing the C parameter that influences the number of violations of the margin allowed in the training data which increases the bias but decreases the variance.



Q4. What is a confusion matrix? 

Ans. The confusion matrix is a 2X2 table that contains 4 outputs provided by the binary classifier. Various measures, such as error-rate, accuracy, specificity, sensitivity, precision and recall are derived from it. Confusion Matrix


A data set used for performance evaluation is called a test data set. It should contain the correct labels and predicted labels. 


The predicted labels will exactly the same if the performance of a binary classifier is perfect.


The predicted labels usually match with part of the observed labels in real-world scenarios.
  


A binary classifier predicts all data instances of a test data set as either positive or negative. This produces four outcomes-
1. True-positive(TP) — Correct positive prediction 
 2. False-positive(FP) — Incorrect positive prediction 
 3. True-negative(TN) — Correct negative prediction 
 4. False-negative(FN) — Incorrect negative prediction 


Basic measures derived from the confusion matrix 
1. Error Rate = (FP+FN)/(P+N) 
 2. Accuracy = (TP+TN)/(P+N) 
 3. Sensitivity(Recall or True positive rate) = TP/P
 4. Specificity(True negative rate) = TN/N 
 5. Precision(Positive predicted value) = TP/(TP+FP) 
 6. F-Score(Harmonic mean of precision and recall) = (1+b)(PREC.REC)/(b²PREC+REC) where b is commonly 0.5, 1, 2.

Q5. What is the difference between “long” and “wide” format data?
Ans. In the wide-format, a subject’s repeated responses will be in a single row, and each response is in a separate column. In the long-format, each row is a one-time point per subject. You can recognize data in wide format by the fact that columns generally represent groups.


Q6. What do you understand by the term Normal Distribution? 

Ans. Data is usually distributed in different ways with a bias to the left or to the right or it can all be jumbled up.

 However, there are chances that data is distributed around a central value without any bias to the left or right and reaches normal distribution in the form of a bell-shaped curve.


The random variables are distributed in the form of a symmetrical, bell-shaped curve.

 Properties of Normal Distribution are as follows; 
1. Unimodal -one mode  
2. Symmetrical -left and right halves are mirror images 
 3. Bell-shaped -maximum height (mode) at the mean 
 4. Mean, Mode, and Median are all located in the center 
 5. Asymptotic

Q7. What is correlation and covariance in statistics?

Ans. Covariance and Correlation are two mathematical concepts; these two approaches are widely used in statistics. Both Correlation and Covariance establish the relationship and also measure the dependency between two random variables. Though the work is similar between these two in mathematical terms, they are different from each other.



Correlation: Correlation is considered or described as the best technique for measuring and also for estimating the quantitative relationship between two variables. Correlation measures how strongly two variables are related. 

 Covariance: In covariance two items vary together and it’s a measure that indicates the extent to which two random variables change in cycle. It is a statistical term; it explains the systematic relation between a pair of random variables, wherein changes in one variable reciprocal by a corresponding change in another variable. 

Q8. What is the difference between Point Estimates and Confidence Interval? 

Ans.  Point Estimation gives us a particular value as an estimate of a population parameter. Method of Moments and Maximum Likelihood estimator methods are used to derive Point Estimators for population parameters. 

 A confidence interval gives us a range of values which is likely to contain the population parameter. The confidence interval is generally preferred, as it tells us how likely this interval is to contain the population parameter. This likeliness or probability is called Confidence Level or Confidence coefficient and represented by 1 — alpha, where alpha is the level of significance.

Q9. What is the goal of A/B Testing?

Ans. It is a hypothesis testing for a randomized experiment with two variables A and B. 

 The goal of A/B Testing is to identify any changes to the web page to maximize or increase the outcome of interest. A/B testing is a fantastic method for figuring out the best online promotional and marketing strategies for your business. It can be used to test everything from website copy to sales emails to search ads .

An example of this could be identifying the click-through rate for a banner ad.

Q10. What is p-value?

Ans. When you perform a hypothesis test in statistics, a p-value can help you determine the strength of your results. p-value is a number between 0 and 1. Based on the value it will denote the strength of the results. The claim which is on trial is called the Null Hypothesis. 

 Low p-value (≤ 0.05) indicates strength against the null hypothesis which means we can reject the null Hypothesis. High p-value (≥ 0.05) indicates strength for the null hypothesis which means we can accept the null Hypothesis p-value of 0.05 indicates the Hypothesis could go either way. To put it in another way.

 High P values: your data are likely with a true null. Low P values: your data are unlikely with a true null.


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Saturday, August 7, 2021

Best Mobile Phones under Rs 10,000

     In todays world without mobile phone we can't do anything , it's just become the unbreakable part of our life . So there are some important things you need to check while buying a new phone . Now I will tell you about some good android phones which you can afford easily 


Redmi 9A (Nature Green, 2GB RAM, 32GB Storage) | 2GHz Octa-core Helio G25 Processor | 5000 mAh Battery - ₹6,999.00






















  • Country Of Origin - India
  • 13MP rear camera with AI portrait, AI scene recognition, HDR, pro mode | 5MP front camera. Hybrid Sim Slot : Yes
  • 16.58 centimeters (6.53 inch) HD+ multi-touch capacitive touchscreen with 1600 x 720 pixels resolution, 268 ppi pixel density and 20:9 aspect ratio
  • Memory, Storage & SIM: 2GB RAM, 32GB internal memory expandable up to 512GB | Dual SIM (nano+nano) + Dedicated SD card slot
  • Android v10 operating system with upto 2.0GHz clock speed Mediatek Helio G25 octa core processor
  • 5000mAH lithium-polymer large battery with 10W wired charger in-box
  • 1 year manufacturer warranty for device and 6 months manufacturer warranty for in-box accessories including batteries from the date of purchase
  • Box also includes: Power adapter, USB cable, sim eject tool, warranty card and user guide







Model NameRealme C11
Wireless CarrierUnlocked for All Carriers
BrandRealme
Form factorBar
Memory Storage Capacity32 GB


  • 2 GB RAM 32 GB ROM Expandable Upto 256 GB
  • 16.51 cm 6.5 inch LCD
  • 8MP 5MP Front Camera
  • 5000 mAh Battery
  • Connector type: Micro USB






Model NameRedmi 9
Wireless CarrierUnlocked
BrandRedmi
Form factorSmartphone
Memory Storage Capacity64 GB

About this item

  • 13+2MP Rear camera with AI Portrait, AI scene recognition, HDR, Pro mode | 5MP front facing camera
  • 16.58 centimeters (6.53-inch) HD+ multi-touch capacitive touchscreen with 1600 x 720 pixels resolution, 268 ppi pixel density, 20:9 aspect ratio
  • Memory, Storage & SIM: 4GB RAM | 64GB storage expandable up to 512GB| Dual SIM with dual standby (4G+4G)
  • Android v10 operating system with 2.3GHz Mediatek Helio G35 octa core processor
  • 5000mAH lithium-polymer battery with 10W wired charger in-box
  • 1 year manufacturer warranty for device and 6 months manufacturer warranty for in-box accessories including batteries from the date of purchase
  • Box also includes: Power adapter, USB cable, SIM eject tool, Warranty card, User guide

















Model NameRedmi 9 Prime
BrandRedmi
Form factorSmartphone
Memory Storage Capacity64 GB
OSAndroid

About this item

  • 13MP quad rear camera, ultra-wide, macro, portrait, AI scene recognition, HDR, pro mode | 8 MP front camera
  • 16.58 centimeters (6.53-inch) FHD+ capacitive multi-touch touchscreen with 2340 x 1080 pixels resolution, 394 ppi pixel density and 19.5:9 aspect ratio
  • Memory, Storage & SIM: 4GB | 64GB internal memory expandable up to 512GB | Dual SIM (nano+nano) + Dedicated SD card slot
  • Android v10 operating system with 2.0 GHz Mediatek Helio G80 octa core processor
  • 5020 mAh large lithium-polymer battery with 18W charging support
  • 1 year manufacturer warranty for device and 6 months manufacturer warranty for in-box accessories including batteries from the date of purchase
  • Box also includes: Power adapter, USB cable, SIM eject tool, warranty card, user guide, clear soft case


Model NameTecno Spark 7T (4/64GB)
Wireless CarrierUnlocked for All Carriers
BrandTecno
Form factorSmartphone
Memory Storage Capacity64 GB

About this item

  • 48MP AI Dual Rear Camera | 8MP Selfie Camera with Dual Front Flash
  • 6000mAh Powerful Battery | 36 days standby
  • 6.52HD+IPS display | 90.3% screen to body ratio
  • Helio G35 Gaming Processor
  • 3-in-1 SIM slot/ Fingerprint Sensor/ latest Android 11








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