Machine Learning with Python : COMPLETE COURSE FOR BEGINNERS
Complete Machine Learning Course with Python for beginners
Rating⭐️: 4.6 out 5
Students 👨‍🎓 : 18533
Duration ⏰ : 13 hours on-demand video
Teacher 👨‍🏫: Prashant Mishra
🔗 Course link


I have noticed this one is currently free (but only for first 1000 enrols !!!) so I thought some of you might be interested 😊

#machinelearning #pythoncourses #python
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The Best Data Science Approaches In The Data Mining World

One of the best approaches in the data mining world is called the CRISP-DM. This means Cross Industry Standard Process for Data Mining. It describes the data project as having six phases.

1) Business Understanding
a) What is the business objectives and situation assessment
b) Determine the data mining goal and create a project plan

2) Data Understanding
a) Collect Initial data and describe data
b) Explore data and verify data quality

3) Data Preparation
a) Get,Select and Clean the data set
b) Construct and Integrate data

4) Modeling
a) Select model technique and generate test design
b) Build and access model

5) Evaluation
a) Evaluate and review process
b) Determine the next steps

6) Deployment
a) Plan deployment
b) Plan monitoring and maintenance
c) Produce final report and review project

Difference between AI, ML and DL

Contains a simplified guide to understanding the terms AI, ML and DL

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😉

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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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.