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Data Science & Machine Learning

Unsupervised Learning using Python
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Data Science & Machine Learning

Ankur_A_Patel_Hands_On_Unsupervised_Learning_Using_Python_How_to.epub

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Data Science & Machine Learning

How Uber works

With a huge database of drivers, as soon as a user requests for car, their algorithms match a user with the most suitable driver within a 15 second window to the nearest driver. Uber stores and analyses data on every single trip the users take which is leveraged to predict the demand for cars, set the fares and allocate sufficient resources. Data science team at Uber also performs in-depth analysis of the public transport networks across different cities so that they can focus on cities that have poor transportation and make the best use of the data to enhance customer service experience.

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Data Science & Machine Learning

Machine_Learning_For_Dummies_by_John_Paul_Mueller,_Luca_Massaron.pdf

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Data Science & Machine Learning

practical statistics for data scientist.pdf

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Data Science & Machine Learning

Pandas Tricks to Create a DataFrame From an Existing One.pdf

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Data Science & Machine Learning

machine-learning-cheat-sheet.pdf

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Data Science & Machine Learning

Python_Complete_cheatsheet.pdf

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Data Science & Machine Learning

Some interview questions related to Data science

1- what is difference between structured data and unstructured data.

2- what is multicollinearity.and how to remove them

3- which algorithms you use to find the most correlated features in the datasets.

4- define entropy

5- what is the workflow of principal component analysis

6- what are the applications of principal component analysis not with respect to dimensionality reduction

7- what is the Convolutional neural network. Explain me its working

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Data Science & Machine Learning

Decision trees and Random forests?

Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It works for both categorical and continuous input and output variables. In this technique, we split the population or sample into two or more homogeneous sets (or sub-populations) based on most significant splitter / differentiator in input variables.

Random Forest is a versatile machine learning method capable of performing both regression and classification tasks. It also undertakes dimensional reduction methods, treats missing values, outlier values and other essential steps of data exploration, and does a fairly good job. It is a type of ensemble learning method, where a group of weak models combine to form a powerful model.

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Data Science & Machine Learning

😉5 Machine Learning Algorithms with Project Ideas

📉Linear Regression -> House Price Prediction
📈Logistic Regression -> Loan Default Prediction
🗞️ SVM -> News Classification
🏛️ KNN -> Breast Cancer Classification
🧮 Naive Bayes -> Text Classification

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Data Science & Machine Learning

Data Science Interview Questions.pdf

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Data Science & Machine Learning

Supervised Learning Cheatsheet.pdf

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