Best of Machine Learning with Python
Here's a ranked list of 920 awesome machine learning projects with a total of 3,4 Million stars grouped into 34 categories.
Stars⭐️: 6.9K
Fork: 962
Repo: https://github.com/ml-tooling/best-of-ml-python#image-data
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Here's a ranked list of 920 awesome machine learning projects with a total of 3,4 Million stars grouped into 34 categories.
Stars⭐️: 6.9K
Fork: 962
Repo: https://github.com/ml-tooling/best-of-ml-python#image-data
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GitHub
GitHub - ml-tooling/best-of-ml-python: 🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.
🏆 A ranked list of awesome machine learning Python libraries. Updated weekly. - ml-tooling/best-of-ml-python
2022 Python and Machine Learning in Financial Analysis
Looking to improve your machine learning skills for financial analysis? Here's a free resource for you😉
Rating⭐️: 4.3 out 5
Students 👨🎓 : 33,014
Duration ⏰ : 20 hours on-demand video
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Course Link
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Looking to improve your machine learning skills for financial analysis? Here's a free resource for you😉
Rating⭐️: 4.3 out 5
Students 👨🎓 : 33,014
Duration ⏰ : 20 hours on-demand video
Teacher 👨🏫: S.Emadedin Hashemi
Course Link
This course coupon expires until 3rd of May. Let's jump on this while we still can😁
#machinelearning #pythoncourses #python
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Udemy
Complete Python and Machine Learning in Financial Analysis
Using Python, Machine Learning, and Deep Learning in Financial Analysis with step-by-step coding (with all codes)
The Machine Learning Crash Course With TensorFlow APIs
Machine Learning Crash Course features a series of lessons with video lectures, real-world case studies, and hands-on practice exercises.
Link: **https://developers.google.com/machine-learning/crash-course
**Contents:
🔘 30+ Exercises
🔘 25 Lessons
🔘 15 hours course duration
🔘 Lectures from Google Researchers
🔘 Real World Case Studies
🔘 Interactive Visualisation of Algorithms in action
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Machine Learning Crash Course features a series of lessons with video lectures, real-world case studies, and hands-on practice exercises.
Link: **https://developers.google.com/machine-learning/crash-course
**Contents:
🔘 30+ Exercises
🔘 25 Lessons
🔘 15 hours course duration
🔘 Lectures from Google Researchers
🔘 Real World Case Studies
🔘 Interactive Visualisation of Algorithms in action
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Google for Developers
Machine Learning | Google for Developers
20 AWESOME SOURCES OF FREE DATA SETS
If you are after solid data to do your projects with ease and lessen the stress of doing the data collection yourself, here's a good resource containing amazing sites where you can get your data sets for free😁
https://www.searchenginejournal.com/free-data-sources/302601/#close
If you are after solid data to do your projects with ease and lessen the stress of doing the data collection yourself, here's a good resource containing amazing sites where you can get your data sets for free😁
https://www.searchenginejournal.com/free-data-sources/302601/#close
Search Engine Journal
28 Awesome Sources Of Free Data
Explore this list of reliable and reputable free data sources to power your data-driven narratives and marketing campaigns.
Forwarded from Free programming books
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Structured vs unstructured data
It is useful to distinguish between structured and unstructured data. The former is typically represented in some well-structured form, often as a table or number of tables, while the latter is just a collection of files. Sometimes we can also talk about semi-structured data, that have some sort of a structure
that may vary greatly.
It is useful to distinguish between structured and unstructured data. The former is typically represented in some well-structured form, often as a table or number of tables, while the latter is just a collection of files. Sometimes we can also talk about semi-structured data, that have some sort of a structure
that may vary greatly.
Facts you need to know about GPUs for Deep Learning
Have you heard about GPUs?🤓 What is GPU and why should i care?🤨
Well I know you might be wondering what this has to do with your deep learning projects😉
Graphics Processing Units (GPUs) are specialized processing cores that you can use to speed computational processes.
It was initially designed to process images and visual data. But now, It is used in reducing the efficiency and power needed to run DL projects,
👌It enables the distribution of training processes and can significantly speed machine learning operations.
👌It is a safer bet for quick deep learning since data science model training is based on simple matrix arithmetic calculations.
👌Training models is a hardware-intensive operation, and a good GPU will ensure that neural network operations operate smoothly.
👌It has a good Video RAM,which frees up CPU for other tasks and providing necessary memory bandwidth for huge datasets.
Have you heard about GPUs?🤓 What is GPU and why should i care?🤨
Well I know you might be wondering what this has to do with your deep learning projects😉
Graphics Processing Units (GPUs) are specialized processing cores that you can use to speed computational processes.
It was initially designed to process images and visual data. But now, It is used in reducing the efficiency and power needed to run DL projects,
👌It enables the distribution of training processes and can significantly speed machine learning operations.
👌It is a safer bet for quick deep learning since data science model training is based on simple matrix arithmetic calculations.
👌Training models is a hardware-intensive operation, and a good GPU will ensure that neural network operations operate smoothly.
👌It has a good Video RAM,which frees up CPU for other tasks and providing necessary memory bandwidth for huge datasets.
Awesome Public Datasets for Your Projects
This contains numerous datasets ranging from :
Agriculture
Biology
Climate+Weather
Complex Networks
Computer Networks
Cyber Security
Data Challenges
Earth Science
Economics
Education
Energy
Entertainment
Finance
...
There's alot you can lay your hands on here
Stars⭐️: 48.8K
Fork: 8.7K
Repo: https://github.com/awesomedata/awesome-public-datasets
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This contains numerous datasets ranging from :
Agriculture
Biology
Climate+Weather
Complex Networks
Computer Networks
Cyber Security
Data Challenges
Earth Science
Economics
Education
Energy
Entertainment
Finance
...
There's alot you can lay your hands on here
Stars⭐️: 48.8K
Fork: 8.7K
Repo: https://github.com/awesomedata/awesome-public-datasets
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GitHub
GitHub - awesomedata/awesome-public-datasets: A topic-centric list of HQ open datasets.
A topic-centric list of HQ open datasets. Contribute to awesomedata/awesome-public-datasets development by creating an account on GitHub.
Machine learning for dummies
IBMs limited edition
Judith Hurwitz
Daniel Kirsch
https://www.ibm.com/downloads/cas/GB8ZMQZ3
IBMs limited edition
Judith Hurwitz
Daniel Kirsch
https://www.ibm.com/downloads/cas/GB8ZMQZ3
Let's talk about some simple stat terms - mean, median and mode
Mean, median, and mode are three kinds of "averages". There are many "averages" in statistics, but these are, I think, the three most common, and are certainly the three you are most likely to encounter in your pre-statistics courses, if the topic comes up at all.
The "mean" is the "average" you're used to, where you add up all the numbers and then divide by the number of numbers.
The "median" is the "middle" value in the list of numbers. To find the median, your numbers have to be listed in numerical order from smallest to largest, so you may have to rewrite your list before you can find the median.
The "mode" is the value that occurs most often. If no number in the list is repeated, then there is no mode for the list.
Task:
Find the mean, median, mode, and range for the following list of values:
13, 18, 13, 14, 13, 16, 14, 21, 13
Solution:
mean: 15
median: 14
mode: 13
Explanation:
The mean is the usual average, so I'll add and then divide:
(13 + 18 + 13 + 14 + 13 + 16 + 14 + 21 + 13) ÷ 9 = 15
The median is the middle value, so first I'll have to rewrite the list in numerical order:
13, 13, 13, 13, 14, 14, 16, 18, 21
There are nine numbers in the list, so the middle one will be the (9 + 1) ÷ 2 = 10 ÷ 2 = 5th number: 14
The mode is the number that is repeated more often than any other, so 13 is the mode.
Mean, median, and mode are three kinds of "averages". There are many "averages" in statistics, but these are, I think, the three most common, and are certainly the three you are most likely to encounter in your pre-statistics courses, if the topic comes up at all.
The "mean" is the "average" you're used to, where you add up all the numbers and then divide by the number of numbers.
The "median" is the "middle" value in the list of numbers. To find the median, your numbers have to be listed in numerical order from smallest to largest, so you may have to rewrite your list before you can find the median.
The "mode" is the value that occurs most often. If no number in the list is repeated, then there is no mode for the list.
Task:
Find the mean, median, mode, and range for the following list of values:
13, 18, 13, 14, 13, 16, 14, 21, 13
Solution:
median: 14
mode: 13
(13 + 18 + 13 + 14 + 13 + 16 + 14 + 21 + 13) ÷ 9 = 15
The median is the middle value, so first I'll have to rewrite the list in numerical order:
13, 13, 13, 13, 14, 14, 16, 18, 21
There are nine numbers in the list, so the middle one will be the (9 + 1) ÷ 2 = 10 ÷ 2 = 5th number: 14
The mode is the number that is repeated more often than any other, so 13 is the mode.
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1) Data Manipulation in Python: Master Python, Numpy & Pandas
Rating ⭐️: 4.3 out of 5
Students 👨🏫: 80,451
Created by: Meta Brains
🔗 Course link
2) Python for Deep Learning: Build Neural Networks in Python
Rating ⭐️: 4.2 out of 5
Students 👨🏫: 44,128
Created by: Meta Brains
🔗 Course link
Note: Free coupon is inserted in URL. Courses are FREE FOR 3 DAYS
#python #datanalysis #datascience #deeplearing #numpy #pandas
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Udemy
Data Manipulation in Python: Master Python, Numpy & Pandas
Learn Python, NumPy & Pandas for Data Science: Master essential data manipulation for data science in python
Data Preprocessing: Understanding and Detecting Outliers
Here's a guide to understanding, detecting and handling outliers👀.
I hope you gain the confidence you need to handle them😁
Outlier Detection and Analysis Methods
Link: Click Me 😌
Detecting and Treating Outliers | Treating the odd one out!
Link: Click Me 😌
Python Treatment for Outliers in Data Science
Link: Click Me 😌
Why You Shouldn’t Just Delete Outliers
Link: Click Me😌
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Here's a guide to understanding, detecting and handling outliers👀.
I hope you gain the confidence you need to handle them😁
Outlier Detection and Analysis Methods
Link: Click Me 😌
Detecting and Treating Outliers | Treating the odd one out!
Link: Click Me 😌
Python Treatment for Outliers in Data Science
Link: Click Me 😌
Why You Shouldn’t Just Delete Outliers
Link: Click Me😌
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