Data Science & Machine Learning

23 Jun 2023 18:10

800 Data Science Questions with Answers

Data Science & Machine Learning

21 Jun 2023 17:46

Data Engineering Project workflow

Data Science & Machine Learning

18 Jun 2023 15:15

How does a model work on a stakeholder point of view

Data Science & Machine Learning

18 Jun 2023 08:01

Deep Learning on Microcontrollers
Atul Krishna Gupta, 2023

Data Science & Machine Learning

17 Jun 2023 13:40

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Data Science & Machine Learning

15 Jun 2023 05:45

Here are some incredible platforms where you can download datasets for your project:


Our World in Data https://ourworldindata.org/

World Health Organization (https://www.who.int/data/gho

Statcounter (https://gs.statcounter.com/

Food and Agriculture Organization of the UN (FAO) (https://www.fao.org/home/en

World Bank (https://data.worldbank.org/)

Data Science & Machine Learning

07 Jun 2023 13:23

Introduction to Programming Using Python
Daniel Liang, 2012

Data Science & Machine Learning

03 Jun 2023 22:08

Python Machine Learning Workbook for Beginners
AI Publishing, 2020

Data Science & Machine Learning

01 Jun 2023 18:06

"Approaching (Almost) Any Machine Learning Problem" book.

Data Science & Machine Learning

31 May 2023 20:25

A Handbook of Statistical Analyses Using Stata
Sophia Rabe-Hesketh, 2007

Data Science & Machine Learning

29 May 2023 16:12

Open Source Projects - Beyond Code
John Mertic, 2023

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

23 Jun 2023 16:17

8 AI Tools Just for Fun:

1. Tattoo Artist
https://tattoosai.com

2. Talk to Books
https://books.google.com/talktobooks/

3. Vintage Headshots
https://myheritage.com/ai-time-machine

4. Hello to Past
https://hellohistory.ai

5. Fake yourself
https://fakeyou.com

6. Unreal Meal
https://unrealmeal.ai

7. Reface AI
https://hey.reface.ai

8. Voice Changer
https://voicemod.net

Data Science & Machine Learning

20 Jun 2023 11:08

Professional Automated Trading
Eugene A. Durenard, 2013

Data Science & Machine Learning

18 Jun 2023 09:52

Type: Fresher Job
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Data Science & Machine Learning

18 Jun 2023 08:01

Arduino V Machine Learning
Steven F. Barrett, 2023

Data Science & Machine Learning

15 Jun 2023 15:07

Machine Learning in Production
Suhas Pote, 2023

Data Science & Machine Learning

14 Jun 2023 08:37

ChatGPT for Data Science Interview
KDnuggets, 2023

Data Science & Machine Learning

07 Jun 2023 13:23

Unlocking the Power of Generative AI Models
Henner Gimpel, 2023

Data Science & Machine Learning

03 Jun 2023 22:08

Deep Learning Crash Course for Beginners with Python
AI Publishing, 2021

Data Science & Machine Learning

01 Jun 2023 07:29

Industry Data Science vs Academia Data Science

Comparing Data Science in academia and Data Science in industry is like comparing tennis with table tennis: they sound similar but in the end, they are completely different!

5 big differences between Data Science in academia and in industry 👇:

1️⃣ Model vs Data: Academia focuses on models, industry focuses on data. In academia, it’s all about trying to find the best model architecture to optimise a defined metric. In industry, loading and processing the data accounts for around 80% of the job.

2️⃣ Novelty vs Efficiency: The end goal of academia is often to publish a paper and to do so, you will need to find and implement a novel approach. Industry is all about efficiency: reusing existing models as much as possible and applying them to your use case.

3️⃣ Complex vs Simple: More often than not, academia requires complex solutions. I know that this isn’t always the case but unfortunately, complex papers get a higher chance of being accepted at top conferences. In industry, it’s all about simplicity: trying to find the simplest solution that solves a specific problem.

4️⃣ Theory vs Engineering: To succeed in academia, you need to have strong theoretical and maths skills. To succeed in industry, you need to develop strong engineering skills. It is great to be able to train a model in a notebook but if you cannot deploy your model in production, it will be completely useless.

5️⃣ Knowledge impact vs $ impact: In academia, it’s all about creating new work and expanding human knowledge. In industry, it is all about using data to drive value and increase revenue.

Data Science & Machine Learning

29 May 2023 16:12

Introduction to Artificial Intelligence and Expert Systems
Dan W. Patterson, 1990

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