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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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😉

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#machinelearning #pythoncourses #python
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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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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

K-Means clustering explained

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.

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.

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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Machine learning for dummies
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.

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#python #datanalysis #datascience #deeplearing #numpy #pandas
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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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