Data Science & Machine Learning

26 May 2023 12:58

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Data Science & Machine Learning

23 May 2023 21:40

Building Feature Extraction with Machine Learning
Bharath H. Aithal, 2023

Data Science & Machine Learning

19 May 2023 01:06

SolidWorks 2017 Black Book
Gaurav Verma, 2016

Data Science & Machine Learning

14 May 2023 18:30

Data Science for Complex Systems
Anindya S. Chakrabarti, 2023

Data Science & Machine Learning

09 May 2023 18:06

Educational Data Science: Essentials, Approaches, and Tendencies
Alejandro Peña-Ayala, 2023

Data Science & Machine Learning

03 May 2023 03:36

Harvard University offers a ton of FREE online courses.
From Computer Science to Artificial Intelligence.
Here are 10 FREE courses you don't want to miss

1. Introduction to Computer Science
An introduction to the intellectual enterprises of computer science and the art of programming.
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This course takes you deeply into the design and implementation of web apps with Python, JavaScript, and SQL using frameworks like Django, React, and Bootstrap.
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3. Introduction to Programming with Scratch

A gentle introduction to programming that prepares you for subsequent courses in coding.
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4. Introduction to Programming with Python
An introduction to programming using Python, a popular language for general-purpose programming, data science, web programming, and more.
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6. Introduction to Artificial Intelligence with Python
Learn to use machine learning in Python in this introductory course on artificial intelligence.
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This is CS50’s introduction to computer science for business professionals.
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Join Harvard University instructor Pavlos Protopapas in this online course to learn how to use Python to harness and analyze data.
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Data Science & Machine Learning

01 May 2023 19:11

Python and R for the Modern Data Scientist
Rick Scavetta, 2021

Data Science & Machine Learning

27 Apr 2023 21:53

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Data Science & Machine Learning

23 Apr 2023 06:29

1. What is the Difference Between a Shallow Copy and Deep Copy in python?

Deepcopy creates a different object and populates it with the child objects of the original object. Therefore, changes in the original object are not reflected in the copy. copy.deepcopy() creates a Deep Copy. Shallow copy creates a different object and populates it with the references of the child objects within the original object. Therefore, changes in the original object are reflected in the copy. copy.copy creates a Shallow Copy.

2. How can you remove duplicate values in a range of cells?

1. To delete duplicate values in a column, select the highlighted cells, and press the delete button. After deleting the values, go to the ‘Conditional Formatting’ option present in the Home tab. Choose ‘Clear Rules’ to remove the rules from the sheet.

2. You can also delete duplicate values by selecting the ‘Remove Duplicates’ option under Data Tools present in the Data tab.

3. Define shelves and sets in Tableau?

Shelves: Every worksheet in Tableau will have shelves such as columns, rows, marks, filters, pages, and more. By placing filters on shelves we can build our own visualization structure. We can control the marks by including or excluding data.
Sets: The sets are used to compute a condition on which the dataset will be prepared. Data will be grouped together based on a condition. Fields which is responsible for grouping are known assets. For example – students having grades of more than 70%.

4. Given a table Employee having columns empName and empId, what will be the result of the SQL query below?

select empName from Employee order by 2 asc;

“Order by 2” is valid when there are at least 2 columns used in SELECT statement. Here this query will throw error because only one column is used in the SELECT statement.

ENJOY LEARNING 👍👍

Data Science & Machine Learning

21 Apr 2023 09:07

Adventures of a Computational Explorer
Stephen Wolfram, 2019

Data Science & Machine Learning

10 Aug 2021 10:14

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

Data Science & Machine Learning

31 Jul 2021 22:25

What are the benefits of a single decision tree compared to more complex models?

easy to implement
fast training
fast inference
good explainability

Data Science & Machine Learning

24 Jul 2021 22:20

What is feature selection? Why do we need it?

Feature Selection is a method used to select the relevant features for the model to train on. We need feature selection to remove the irrelevant features which leads the model to under-perform.

Data Science & Machine Learning

22 Jul 2021 12:15

What are the main parameters of the random forest model?

max_depth: Longest Path between root node and the leaf

min_sample_split: The minimum number of observations needed to split a given node

max_leaf_nodes: Conditions the splitting of the tree and hence, limits the growth of the trees

min_samples_leaf: minimum number of samples in the leaf node

n_estimators: Number of trees

max_sample: Fraction of original dataset given to any individual tree in the given model

max_features: Limits the maximum number of features provided to trees in random forest model

Data Science & Machine Learning

17 Jul 2021 08:15

What are the main parameters in the gradient boosting model?

There are many parameters, but below are a few key defaults.

learning_rate=0.1 (shrinkage).
n_estimators=100 (number of trees).
max_depth=3.
min_samples_split=2.
min_samples_leaf=1.
subsample=1.0.

Data Science & Machine Learning

23 May 2023 21:40

PHP and MySQL
Claudia Alves, 2020

Data Science & Machine Learning

19 May 2023 01:06

Random Matrix Methods for Machine Learning
Romain Couillet, 2022

Data Science & Machine Learning

18 May 2023 08:56

1. What is the Impact of Outliers on Logistic Regression?

The estimates of the Logistic Regression are sensitive to unusual observations such as outliers, high leverage, and influential observations. Therefore, to solve the problem of outliers, a sigmoid function is used in Logistic Regression.


2. What is the difference between vanilla RNNs and LSTMs?


The main difference between vanilla RNNs and LSTMs is that LSTMs are able to better remember long-term dependencies, while vanilla RNNs tend to forget them. This is due to the fact that LSTMs have a special type of memory cell that can retain information for longer periods of time, while vanilla RNNs only have a single layer of memory cells.

3. What is Masked Language Model in NLP?


Masked language models help learners to understand deep representations in downstream tasks by taking an output from the corrupt input. This model is often used to predict the words to be used in a sentence.


4. Why is the KNN Algorithm known as Lazy Learner?

When the KNN algorithm gets the training data, it does not learn and make a model, it just stores the data. Instead of finding any discriminative function with the help of the training data, it follows instance-based learning and also uses the training data when it actually needs to do some prediction on the unseen datasets. As a result, KNN does not immediately learn a model rather delays the learning thereby being referred to as Lazy Learner.

Data Science & Machine Learning

13 May 2023 09:04

On What Kinds of data does chatgpt trained on

Data Science & Machine Learning

07 May 2023 14:10

Data Science and Analytics Strategy
Kailash Awati, 2023

Data Science & Machine Learning

02 May 2023 18:43

Interpretability in Deep Learning
Ayush Somani, 2023

Data Science & Machine Learning

01 May 2023 05:42

Cheatsheet on Numpy and pandas for easy viewing 👀

Data Science & Machine Learning

24 Apr 2023 09:12

Amazing Hackthon Solved Data Science/ML Project Collection

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https://github.com/analyticsindiamagazine/MachineHack/tree/master/Hackathon_Solutions

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Data Science & Machine Learning

21 Apr 2023 09:07

Mastering Ubuntu Server
Jay LaCroix, 2022

Data Science & Machine Learning

21 Apr 2023 09:07

Analytic SQL in SQL Server 2014/2016
Riadh Ghlala, 2019

Data Science & Machine Learning

02 Aug 2021 12:30

🤓 Technical Python concepts tested in the data science job interviews are:

- Data types.
- Built-in data structures.
- User-defined data structures.
- Built-in functions.
- Loops and conditionals.
- External libraries (Pandas).

Source Article: https://www.kdnuggets.com/2021/07/top-python-data-science-interview-questions.html

Data Science & Machine Learning

24 Jul 2021 22:20

What are the decision trees?

This is a type of supervised learning algorithm that is mostly used for classification problems. Surprisingly, it works for both categorical and continuous dependent variables.

In this algorithm, we split the population into two or more homogeneous sets. This is done based on most significant attributes/ independent variables to make as distinct groups as possible.

A decision tree is a flowchart-like tree structure, where each internal node (non-leaf node) denotes a test on an attribute, each branch represents an outcome of the test, and each leaf node (or terminal node) holds a value for the target variable.

Various techniques : like Gini, Information Gain, Chi-square, entropy.

Data Science & Machine Learning

24 Jul 2021 06:29

Quiz Explaination

Supervised Learning: All data is labeled and the algorithms learn to predict the output from the
input data

Unsupervised Learning: All data is unlabeled and the algorithms learn to inherent structure from
the input data.

Semi-supervised Learning: Some data is labeled but most of it is unlabeled and a mixture of
supervised and unsupervised techniques can be used to solve problem.

Unsupervised learning problems can be further grouped into clustering and association problems.

Clustering: A clustering problem is where you want to discover the inherent groupings
in the data, such as grouping customers by purchasing behavior.

Association: An association rule learning problem is where you want to discover rules
that describe large portions of your data, such as people that buy A also tend to buy B.

Data Science & Machine Learning

20 Jul 2021 08:15

🎓 Introduction to Deep Learning (by MIT) 🎓

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Data Science & Machine Learning

16 Jul 2021 22:01

How does L2 regularization look like in a linear model?

L2 regularization adds a penalty term to our cost function which is equal to the sum of squares of models coefficients multiplied by a lambda hyperparameter.

This technique makes sure that the coefficients are close to zero and is widely used in cases when we have a lot of features that might correlate with each other.