Get started in Data Science with Microsoft's FREE course for beginners.
- 10 weeks
- 20 lessons
- Lecture notes
- 100% FREE
https://microsoft.github.io/Data-Science-For-Beginners/
- 10 weeks
- 20 lessons
- Lecture notes
- 100% FREE
https://microsoft.github.io/Data-Science-For-Beginners/
R for Data Science
A weekly data project aimed at the R ecosystem. As this project was borne out of the R4DS Online Learning Community and the R for Data Science textbook, an emphasis was placed on understanding how to summarize and arrange data to make meaningful charts with ggplot2, tidyr, dplyr, and other tools in the tidyverse ecosystem. However, any code-based methodology is welcome - just please remember to share the code used to generate the results.
Creator: rfordatascience
Stars ⭐️: 5.6k
Forked By: 2.3k
https://github.com/rfordatascience/tidytuesday
#R #data_science
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Join @datascience_bds for more cool repositories.
*This channel belongs to @bigdataspecialist group
A weekly data project aimed at the R ecosystem. As this project was borne out of the R4DS Online Learning Community and the R for Data Science textbook, an emphasis was placed on understanding how to summarize and arrange data to make meaningful charts with ggplot2, tidyr, dplyr, and other tools in the tidyverse ecosystem. However, any code-based methodology is welcome - just please remember to share the code used to generate the results.
Creator: rfordatascience
Stars ⭐️: 5.6k
Forked By: 2.3k
https://github.com/rfordatascience/tidytuesday
#R #data_science
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Join @datascience_bds for more cool repositories.
*This channel belongs to @bigdataspecialist group
GitHub
GitHub - rfordatascience/tidytuesday: Official repo for the #tidytuesday project
Official repo for the #tidytuesday project. Contribute to rfordatascience/tidytuesday development by creating an account on GitHub.
Data Science for Engineers, IIT Madras
🆓 Free Online Course
💻 50 Lecture Videos
⏰ 8 Module
🏃♂️ Self paced
Teacher 👨🏫 : Prof. Shankar Narasimhan, Prof. Ragunathan Rengasamy
🔗 https://nptel.ac.in/courses/106106179
#Data_Science #IIT
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👉Join @bigdataspecialist for more👈
🆓 Free Online Course
💻 50 Lecture Videos
⏰ 8 Module
🏃♂️ Self paced
Teacher 👨🏫 : Prof. Shankar Narasimhan, Prof. Ragunathan Rengasamy
🔗 https://nptel.ac.in/courses/106106179
#Data_Science #IIT
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👉Join @bigdataspecialist for more👈
book.pdf
2.4 MB
Foundations of Data Science
by Avrim Blum, John Hopcroft, and Ravindran Kannan
📄 479 pages
#data_science #foundations_of_data_Science
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Join @datascience_bds for more
by Avrim Blum, John Hopcroft, and Ravindran Kannan
📄 479 pages
#data_science #foundations_of_data_Science
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Join @datascience_bds for more
Your Guide to Latent Dirichlet Allocation
Latent Dirichlet Allocation (LDA) is a “generative probabilistic model” of a collection of composites made up of parts. Its uses include Natural Language Processing (NLP) and topic modelling, among others.
In terms of topic modelling, the composites are documents and the parts are words and/or phrases (phrases n words in length are referred to as n-grams).
But you could apply LDA to DNA and nucleotides, pizzas and toppings, molecules and atoms, employees and skills, or keyboards and crumbs.
The probabilistic topic model estimated by LDA consists of two tables (matrices). The first table describes the probability or chance of selecting a particular part when sampling a particular topic (category).
Link
#ml #data_science
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Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
Latent Dirichlet Allocation (LDA) is a “generative probabilistic model” of a collection of composites made up of parts. Its uses include Natural Language Processing (NLP) and topic modelling, among others.
In terms of topic modelling, the composites are documents and the parts are words and/or phrases (phrases n words in length are referred to as n-grams).
But you could apply LDA to DNA and nucleotides, pizzas and toppings, molecules and atoms, employees and skills, or keyboards and crumbs.
The probabilistic topic model estimated by LDA consists of two tables (matrices). The first table describes the probability or chance of selecting a particular part when sampling a particular topic (category).
Link
#ml #data_science
➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖
Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
MIT 6.S191: Introduction to Deep Learning 2021
Created by MIT
⏰ 29 hours worth of material
🎬 43 Video lessons
👨🏫 Teacher: Alexander Amini
🔗 Course link
#deeplearning #ai #MIT
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Created by MIT
⏰ 29 hours worth of material
🎬 43 Video lessons
👨🏫 Teacher: Alexander Amini
🔗 Course link
#deeplearning #ai #MIT
➖➖➖➖➖➖➖➖➖➖➖➖➖➖
👉Join @bigdataspecialist for more👈
Data Science Ethics (Login Required)
Utilize the framework provided in the course to analyze concerns related to data science ethics.
Explore the broader impact of the data science field on modern society and the principles of fairness, accountability and transparency.
Examine the need for voluntary disclosure when leveraging metadata to inform basic algorithms and/or complex artificial intelligence systems.
Learn best practices for responsible data management.
Gain an understanding of the significance of the Fair Information Practices Principles Act and the laws concerning the "right to be forgotten."
🎬 video lessons
Rating⭐️: 4.1 out 5
🏃♂️ Self paced
Source: University of Michigan
🔗 Course Link
#data_science
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Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
Utilize the framework provided in the course to analyze concerns related to data science ethics.
Explore the broader impact of the data science field on modern society and the principles of fairness, accountability and transparency.
Examine the need for voluntary disclosure when leveraging metadata to inform basic algorithms and/or complex artificial intelligence systems.
Learn best practices for responsible data management.
Gain an understanding of the significance of the Fair Information Practices Principles Act and the laws concerning the "right to be forgotten."
🎬 video lessons
Rating⭐️: 4.1 out 5
🏃♂️ Self paced
Source: University of Michigan
🔗 Course Link
#data_science
➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖
Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
Accelerating Deep Learning with GPUs (Login Required)
Training complex deep learning models with large datasets takes along time. In this course, you will learn how to use accelerated GPU hardware to overcome the scalability problem in deep learning.
You can use accelerated hardware such as Google’s Tensor Processing Unit (TPU) or Nvidia GPU to accelerate your convolutional neural network computations time on the Cloud. These chips are specifically designed to support the training of neural networks, as well as the use of trained networks (inference). Accelerated hardware has recently been proven to significantly reduce training time.
🆓 Free Online Course
Rating⭐️: 4.7 out 5
🎬 video lesson
🏃♂️ Self paced
Duration ⏰: More than 7 hours worth of material
Source: cognitiveclass
🔗 Course Link
#deep_Learning
➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖
Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
Training complex deep learning models with large datasets takes along time. In this course, you will learn how to use accelerated GPU hardware to overcome the scalability problem in deep learning.
You can use accelerated hardware such as Google’s Tensor Processing Unit (TPU) or Nvidia GPU to accelerate your convolutional neural network computations time on the Cloud. These chips are specifically designed to support the training of neural networks, as well as the use of trained networks (inference). Accelerated hardware has recently been proven to significantly reduce training time.
🆓 Free Online Course
Rating⭐️: 4.7 out 5
🎬 video lesson
🏃♂️ Self paced
Duration ⏰: More than 7 hours worth of material
Source: cognitiveclass
🔗 Course Link
#deep_Learning
➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖
Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
cognitiveclass.ai
Accelerating Deep Learning with GPUs
Training complex deep learning models with large datasets takes along time. In this course, you will learn how to use accelerated GPU hardware to overcome the scalability problem in deep learning.