Hi!
I have accumulated a lot of information that I want to share:

- Books #armbooks
- Courses #armcourses
- YouTube channels #armyoutube
- Articles and blogs #armarticles
- Tutorials #armtutorials
- Python Libraries #armpackages
- Kaggle Notebooks #armkaggle
- Telegram channels #armtelegram
- Cheetsheets #armcheetsheets
- Repositories #armrepo
- Newsletters #armnewsletters

All the posts in the series can be found using the hashtag #armknowledgesharing (and the hashtags above)

Atomic Habits: An Easy & Proven Way to Build Good Habits & Break Bad Ones

This book is not about data science or machine learning, but I think anyone interested in building a productive life would love to read it, understand it, and start applying the techniques described in the book to real life.

I have read and listened to it in my native language and I am going to read it again in English. I believe that such books should be re-read once a year or two to refresh the information in memory.

10 Things This Book Will Teach You

Learn how to…
- Build a system for getting 1% better every day.
- Break your bad habits and stick to good ones.
- Avoid the common mistakes most people make when changing habits.
- Overcome a lack of motivation and willpower.
- Develop a stronger identity and believe in yourself.
- Make time for new habits (even when life gets crazy).
- Design your environment to make success easier.
- Make tiny, easy changes that deliver big results.
- Get back on track when you get off course.
- And most importantly, how to put these ideas into practice in real life.
…and much more.

I also recommend to sign up for the 3-2-1 Newsletter from the author of the book using the link in the comments section:

"The 3-2-1 Newsletter is one of the most popular newsletters in the world. Every Thursday, the latest issue is sent to over 2,000,000 people. Each message includes 3 short ideas from me, 2 quotes from others, and 1 question for you to ponder"

#armbooks #armknowledgesharing #book #habits #selfhelp #motivation

Open Machine Learning Course

Topics: #EDA, #Visualization, #Classification, #Regression, #Ensembles, #FeatureEngineering, #Clustering, #OnlineLearning, #TimeSeries, #GradientBoosting

mlcourse.ai is an open Machine Learning course by OpenDataScience (ods.ai), led by Yury Kashnitsky, Ph.D.. Having both a Ph.D. degree in applied math and a Kaggle Competitions Master tier, Yury aimed at designing an ML course with a perfect balance between theory and practice. Thus, the course meets you with math formulae in lectures, and a lot of practice in a form of assignments and Kaggle Inclass competitions. Currently, the course is in a self-paced mode. Here we guide you through the self-paced mlcourse.ai.

#armcourses #armknowledgesharing

Kaggle Learn

Kaggle not only allows you to participate in Data Science competitions, but also provides access to its courses.

Each course focuses on a particular topic and has several lessons. Passing them is not difficult and does not take much time (several hours), but the courses are interesting and allow you to remember the basics (and maybe learn something new for yourself).

It is convenient that for each course it is indicated which courses you need to take before studying this particular course and which after.

I've finished Python, Intro to Machine Learning, Intermediate Machine Learning when they were introduced. Now I'm going through Machine Learning Explainability.

Some of the must-have courses:
- Feature Engineering (https://lnkd.in/e7xF_9-Z)
- Time Series (https://lnkd.in/eA-cYvHi)
- Data Cleaning (https://lnkd.in/edCfAkat)
- Intro to AI Ethics (https://lnkd.in/eBiT2YHM)
- Machine Learning Explainability (https://lnkd.in/eTnCWkFD)

The courses are free.

There are also guides, like Natural Language Processing Guide
https://www.kaggle.com/learn-guide/natural-language-processing

#armknowledgesharing #armcourses
#kaggle #python #machinelearning #datascience

@data_science_weekly

The Matrix Calculus You Need For Deep Learning

This paper is an attempt to explain all the matrix calculus you need in order to understand the training of deep neural networks. We assume no math knowledge beyond what you learned in calculus 1, and provide links to help you refresh the necessary math where needed. Note that you do not need to understand this material before you start learning to train and use deep learning in practice; rather, this material is for those who are already familiar with the basics of neural networks, and wish to deepen their understanding of the underlying math. Don't worry if you get stuck at some point along the way---just go back and reread the previous section, and try writing down and working through some examples.

Article: https://arxiv.org/abs/1802.01528
Browser-friendly version: https://ar5iv.labs.arxiv.org/html/1802.01528
Explained.ai version: https://explained.ai/matrix-calculus/

#armknowledgesharing #armarticles
#deeplearning #math #matrix #matrixcalculus

@data_science_weekly

Data Science for Tabular Data: Advanced Techniques

This is a collection of the best Kaggle notebooks (kernels) and other resources (including notebooks (kernels) and posts in discussion from Prize Competition Winners) with Advanced Techniques of Data Science for Tabular Data.

Table of Contents:
- Exploratory Data Analysis (EDA)
- Feature Engineering (FE)
- Model Hyper-parameter Optimization
- Models Selection
- Time Series
- Probability Calibration
- Universal Tool-kits
- DS Tutorials

#armkaggle #armknowledgesharing
#datascience #kaggle #tabular #dataanalysis

@data_science_weekly

Python & ML tasks
Задачи по Python и машинному обучению

Today I want to share with you a telegram channel which will help you retain your knowledge of python and maybe learn something new.

Every day a question is posted and you can answer it using the quiz under the question.
If your answer is wrong, you can find out the correct one and read the explanation.

#armknowledgesharing #armtelegram #python

@data_science_weekly

The author of the channel is Valerii Babushkin. He is a Vice President (Data Science) at Blockchain.com.

He writes about Machine Learning, Deep Learning, AB Tests, Article Reviews, Job Interviews.

He has his own YouTube channel, and you can also search for videos and podcasts with him.

Telegram (rus version)
Youtube channel

#armknowledgesharing #armtelegram

@data_science_weekly