Data Science & Machine Learning

Machine Learning Cheatsheet #python #ml #cheatsheet #ai

🚀Join our first free lessons and explore the fields of tech! You will find the answers to all your questions at our webinars. Open the link https://crst.co/imKOy, make your choice and apply now while there are still seats available. See you there! ▶️ May 4  - Manual QA Course. Free first lesson! ▶️ May 10 - Sales Engineer Course. Free first lesson!▶️ May 23 - Tech Sales Training. Free first lesson! ▶️ June 2 - Systems Engineer. Free first lesson! Special offer for all participants! ️✅ Apply by the link https://crst.co/hHZ9a

You are given a data set. The data set has missing values which spread along 1 standard deviation from the median. What percentage of data would remain unaffected? Why? Answer: This question has enough hints for you to start thinking! Since, the data is spread across median, let’s assume it’s a normal distribution. We know, in a normal distribution, ~68% of the data lies in 1 standard deviation from mean (or mode, median), which leaves ~32% of the data unaffected. Therefore, ~32% of the data would remain unaffected by missing values.

🔰 Python for Machine Learning & Data Science Masterclass ⏱ 44 Hours 📦 170 Lessons Learn about Data Science and Machine Learning with Python! Including Numpy, Pandas, Matplotlib, Scikit-Learn and more! Taught By: Jose Portilla Download Full Course: https://t.me/datasciencefree/69 Download All Courses: https://t.me/datasciencefree/2

DATA SCIENCE INTERVIEW QUESTIONS WITH ANSWERS 1. What is a logistic function? What is the range of values of a logistic function? f(z) = 1/(1+e -z ) The values of a logistic function will range from 0 to 1. The values of Z will vary from -infinity to +infinity. 2. What is the difference between R square and adjusted R square? R square and adjusted R square values are used for model validation in case of linear regression. R square indicates the variation of all the independent variables on the dependent variable. i.e. it considers all the independent variable to explain the variation. In the case of Adjusted R squared, it considers only significant variables(P values less than 0.05) to indicate the percentage of variation in the model. Thus Adjusted R2 is always lesser then R2. 3. What is stratify in Train_test_split? Stratification means that the train_test_split method returns training and test subsets that have the same proportions of class labels as the input dataset. So if my input data has 60% 0's and 40% 1's as my class label, then my train and test dataset will also have the similar proportions. 4. What is Backpropagation in Artificial Neuron Network? Backpropagation is the method of fine-tuning the weights of a neural network based on the error rate obtained in the previous epoch (i.e., iteration). Proper tuning of the weights allows you to reduce error rates and make the model reliable by increasing its generalization. ENJOY LEARNING 👍👍

https://t.me/CryptoSignalsLeaked/7791 We are giving away 100$ in cash, two memberships for R&A signals for 1 month, and it's allll for free!🔥 All you  Don't miss out! It's free for everyone!

How is kNN different from k-means clustering? kNN, or k-nearest neighbors is a classification algorithm, where the k is an integer describing the number of neighboring data points that influence the classification of a given observation. K-means is a clustering algorithm, where the k is an integer describing the number of clusters to be created from the given data. Both accomplish different tasks.

Which models do you know for solving time series problems? Simple Exponential Smoothing: approximate the time series with an exponentional function Trend-Corrected Exponential Smoothing (Holt‘s Method): exponential smoothing that also models the trend Trend- and Seasonality-Corrected Exponential Smoothing (Holt-Winter‘s Method): exponential smoothing that also models trend and seasonality Time Series Decomposition: decomposed a time series into the four components trend, seasonal variation, cycling varation and irregular component Autoregressive models: similar to multiple linear regression, except that the dependent variable y_t depends on its own previous values rather than other independent variables. Deep learning approaches (RNN, LSTM, etc.)

🚀Join us this week in the FREE Webinars and explore the fields of tech! You will find the answers to all your questions at our webinars. Open the link https://crst.co/gSN3a, make your choice and apply now while there are still seats available. See you there! ▶️ April 18, 11 AM PT — Most In-Demand IT Jobs 2022: Become a Software Tester ▶️ April 19, 11 AM PT —  Career in Tech from Scratch: Become a QA Engineer ▶️ April 19, 1 PM PT — Your First Remote Job in Tech Sales with Zero Experience ▶️ April 21, 11 AM PT — Most In-Demand IT Jobs 2022: Become a Software Tester ▶️ April 25 — Manual QA course. First free lesson! ▶️ April 25 — Tech Sales course. First free lesson! ▶️ April 25 — Systems Engineer course. First free lesson! Special offer for all participants! ️✅ Apply by the link https://crst.co/gSN3a

DATA SCIENCE INTERVIEW QUESTIONS WITH ANSWERS 1. What are the assumptions required for linear regression? What if some of these assumptions are violated? Ans: The assumptions are as follows: The sample data used to fit the model is representative of the population The relationship between X and the mean of Y is linear The variance of the residual is the same for any value of X (homoscedasticity) Observations are independent of each other For any value of X, Y is normally distributed. Extreme violations of these assumptions will make the results redundant. Small violations of these assumptions will result in a greater bias or variance of the estimate. 2.What is multicollinearity and how to remove it? Ans: Multicollinearity exists when an independent variable is highly correlated with another independent variable in a multiple regression equation. This can be problematic because it undermines the statistical significance of an independent variable. You could use the Variance Inflation Factors (VIF) to determine if there is any multicollinearity between independent variables — a standard benchmark is that if the VIF is greater than 5 then multicollinearity exists. 3. What is overfitting and how to prevent it? Ans: Overfitting is an error where the model ‘fits’ the data too well, resulting in a model with high variance and low bias. As a consequence, an overfit model will inaccurately predict new data points even though it has a high accuracy on the training data. Few approaches to prevent overfitting are: - Cross-Validation:Cross-validation is a powerful preventative measure against overfitting. Here we use our initial training data to generate multiple mini train-test splits. Now we use these splits to tune our model. - Train with more data: It won’t work every time, but training with more data can help algorithms detect the signal better or it can help my model to understand general trends in particular. - We can remove irrelevant information or the noise from our dataset. - Early Stopping: When you’re training a learning algorithm iteratively, you can measure how well each iteration of the model performs. Up until a certain number of iterations, new iterations improve the model. After that point, however, the model’s ability to generalize can weaken as it begins to overfit the training data. Early stopping refers stopping the training process before the learner passes that point. - Regularization: It refers to a broad range of techniques for artificially forcing your model to be simpler. There are mainly 3 types of Regularization techniques:L1, L2,&,Elastic- net. - Ensembling : Here we take number of learners and using these we get strong model. They are of two types : Bagging and Boosting. 4. Given two fair dices, what is the probability of getting scores that sum to 4 and 8? Ans: There are 4 combinations of rolling a 4 (1+3, 3+1, 2+2): P(rolling a 4) = 3/36 = 1/12 There are 5 combinations of rolling an 8 (2+6, 6+2, 3+5, 5+3, 4+4): P(rolling an 8) = 5/36 ENJOY LEARNING 👍👍