🙊 Collection of Free Courses to Learn Data Science, Data Engineering, Machine Learning, MLOps, and LLMOps

🙄 Begin your data professional journey from the basics of statistics to building a production-grade AI application.

https://www.kdnuggets.com/collection-of-free-courses-to-learn-data-science-data-engineering-machine-learning-mlops-and-llmops

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🖥 A little word cloud generator in Python

Creating a word cloud based on the 'cl.txt' file

Particularly useful for NLP tasks or social media analysis

from wordcloud import WordCloud

import matplotlib.pyplot as plt

# Read text from a file
with open('cl.txt', 'r', encoding='utf-8') as file:
text = file.read()

# Generate word cloud
wordcloud = WordCloud(width=800, height=400, background_color='white').generate(text)

# Display the generated word cloud using matplotlib
plt.figure(figsize=(10, 5))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis('off')
plt.show()

A word cloud is a visual representation of a list of categories/tags. The more often a word occurs, the larger the size it takes on in the cloud.

pip install wordcloud

🥰 Github: https://github.com/amueller/word_cloud?ref=blog.electroica.com

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🖥 Build a typing assistant with Mistral 7B and Ollama - Python Tutorial

Incredible - local AI chatbot based on Ollama and Mistral 7B in just a hundred lines of Python code (!)

💻 Tutorial

🖥 GitHub

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🖥 Latexify: Generates LaTeX math description from Python functions.

Cool Python library that allows you to display functions in LaTeX format

pip install latexify

That is, you simply define the function as usual, like this:

def quadratic(a,b,c):
return (-b + math.sqrt(b**2 - 4*a*c)) / (2*a)

To output a LaTeX formula, we simply place the @latexify.function decorator above the function definition and print the function name quadratic in the Google Colab cell.
It turns out like this - and we will see the formula:

@latexify.function
def quadratic(a,b,c):
return (-b + math.sqrt(b**2 - 4*a*c)) / (2*a)

quadratic

Perfect for those who study at the intersection of IT and physics/mathematics/other disciplines;
Here in Google Colab you can test how it works

🖥 GitHub

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☄️ 6 data science YouTube courses for beginners

⭐️ If you are looking to enter the field of data science and are going to start learning data science topics, these 6 free YouTube courses are a unique opportunity!


1️⃣ Python course with freeCodeCamp
📝 4.5 hour video that covers everything you need to become a Python programmer.

┌ 🏷 Python with freeCodeCamp
└ 🟡 LINK


2️⃣ statistics course with StatQuest
📝 One of the main and prerequisite topics for learning data science is statistics, which this learning course has made easier than ever.

┌ 🏷 Statistics with StatQuest
└ ⏺ LINK

3️⃣ Mathematics course with 3Blue1Brown
✍️ Learning linear algebra, neural networks and central limit theorem for data science.

┌ 🏷 Mathematics with 3Blue1Brown
└ 🔃 LINK


4️⃣ Data cleaning course with DataCamp
📝 Importance and techniques of how to obtain cleansed data and face the challenges of data cleansing.

┌ 🏷 Data Cleaning with DataCamp
└ 🟡 LINK


5️⃣ Machine learning course with Krish Naik
📝 6-hour video that introduces different aspects of ML, from linear regression to clustering algorithms.

┌ 🏷 Machine Learning with Krish Naik
└ 👁 LINK


6️⃣ Data visualization course with Simplilearn
📝 Getting to know how to visualize data using Matplotlib, Seaborn and Bokeh libraries.

┌ 🏷 Data Visualizations with Simplilearn
└ 🟡 LINK

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🖥 25 free data science courses, Gen AI, ML, ...
◀️ From reputable universities and institutions

⛓ In universities, I am always asked about free resources for learning data science, and I thought it would be better to share these resources with you here. I hope this collection will be of great help to those who want to become professionals in the field of data science! 💯


🔄 Data science courses

⏺ Python for Everybody course ➡️ link

⏺ Data analysis with Python course ➡️ link

⏺ Databases and SQL course ➡️ link

⏺ Intro to Inferential Statistics course ➡️ link

⏺ Machine Learning Zoomcamp course ➡️ link


🔄 Data engineering courses

⏺ Data Engineering course ➡️ link

⏺ Data Engineer Learning course ➡️ link

⏺ Database Engineer course ➡️ link

⏺ Big Data Specialization course ➡️ link

⏺ Data Engineering Zoomcamp course ➡️ link


🔵 Machine learning courses

⏺ Intro to ML course ➡️ link

⏺ ML for Everybody course ➡️ link

⏺ ML course in Python with Scikit-Learn ➡️ link

⏺ ML Crash Course ➡️ link

⏺ Course CS229: ML ➡️ link


🟡 MLOps courses

⏺ Python Essentials for MLOps course ➡️ link

⏺ MLOps for Beginners course ➡️ link

⏺ MLOps Specialization course ➡️ link

⏺ MLOps Specialization course ➡️ link

⏺ Made with ML course ➡️ link


🔄 Productive artificial intelligence courses

⏺ Generative AI for Beginners course ➡️ link

⏺ Generative AI Fundamentals course ➡️ link

⏺ Intro to Generative AI course ➡️ link

⏺ Generative AI course with LLMs ➡️ link

⏺ Generative AI for Everyone course ➡️ link

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🔈 list of top 50 data science cheat sheets

🔘 From the day I started summarizing data science topics on LinkedIn, I decided to summarize each topic in a few pages. I finally came up with a list of 50 cheat sheets from various areas of data science. This list covers pretty much everything a data person might need, from how to plot with Matplotlib to using ChatGPT.

⏺ Python: link

⏺ Pandas library: link

⏺ NumPy library: link

⏺ Matplotlib library: link

⏺ seaborn library: link

⏺ scikit-learn library: link

⏺ TensorFlow library: link

⏺ Keras library: link

⏺ PyTorch framework: link

⏺ SQL language: link

👀 GeoPandas project: link

👀 Git version control system: link

👀 AWS cloud platform: link

✅ Azure cloud platform: link

✅ Google Cloud Platform cloud computing: link

✅ Docker platform: link

✅ Kubernetes platform: link

✅ The Linux Command Line training: link

✅ Jupyter notebook: link

✅️ Data preparation: link

✅️ Data Visualization: Link

✅️ Statistical inference: link

✅️ possibility: link

✅️ Linear Algebra: Link

✅️ Differential calculation: link

✅ Time series: link

✅ Natural language processing: link

✅ Neural network: link

✅ Deep Learning: Link

✅ Machine learning: link

✅ Apache Spark Framework: Link

✅ Apache Hadoop framework: link

✅ Big O Notation tool: link

✅ Regular Expression training: link

✅ Unix / Linux Permissions training: link

✅ Python String Formatting tutorial: link

✅ Flask framework: link

✅ Django framework: link

✅ plotly library: link

✅ PostgreSQL database: link

✅ MySQL database: link

✅ MongoDB database: link

✅ TensorFlow Probability library: link

✅ Chatbot GPT-3: link

✅ Training GPT-3 API Reference: link

✅ SciPy library: link

✅ ChatGPT chatbot: link

✅ Training Colors in Data Viz: link

✅ Geospatial DS in Python training: link

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🔄 The largest data visualization tools with Python
🔥 The most powerful data visualization ecosystem

⭐️ The PyViz ecosystem, with nearly 150 different libraries in 12 categories , is one of the most powerful tools to facilitate learning and using data visualization in Python. This ecosystem includes from the main visualizations to the graphic and location libraries and the creation of the dashboard.

✅ To access these 150 top and unique Python libraries, you can use the following link:👇🏼


┌ 🏷 Data visualization in Python
└ 🚀 PyViz


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🎁 205+ free data science and ML courses
✅ from the Udacity platform

✅ Udacity platform It has a wide range of machine learning and data science courses, some of these courses are free and some are paid.

✅ I collected all the free Udacity courses on machine learning, data science, etc. and put them inside the PDF with an active link. Just click on the link of each course. So easily!👌🏼

🗂 +205 Udacity FREE Courses

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6 of the best cloud notebooks for data science

⏺ Cloud notebooks are analytical tools that can be accessed only through an Internet browser without the need to install special software, and provide the possibility of running codes, analyzing data, and creating reports in an online environment.

🔃 In the following, I have provided you with 6 of the best cloud notebooks for data science projects , each of which has its own applications and capabilities in data analysis, programming, and data science project management.


┌ 🏷 6 Free Cloud Notebooks for DS
├ ✅ Deepnote
├ ✅ Kaggle
├ ✅ Hex
├ ✅ Colab
├ ✅ Naas
└ ✅ Datalore

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🇦🇪 Complete set of data science interview questions
📂 with comprehensive answers

🇩🇿 Have you ever been in a situation where the interviewer asked you a theoretical or technical question in the field of data science and you couldn't answer it? Is it just because you were not fully prepared? It happens to many. For example, I have the weakness of mental locking in front of new questions during technical interviews.

🇪🇬 But to overcome this problem, I started looking at sample data science interview questions and collected a collection of the most complete and best data science interview questions with answers from various sources to help you for all data science related jobs. Be prepared and don't repeat my mistakes during interviews!

🔥 Well, if you agree, let's start this interesting part:

┌ 🏷 Data Science Interviews Resources
└ 📂 GitHub-Repos


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⚡️⚡️⚡️⚡️⚡️⚡️⚡️⚡️

🔗 "Data Analysis for Social Scientists" course

☄️ MIT University's new course "Data Analysis for Social Scientists" which, with an attractive and data-based concept, gives scientists in this field the opportunity to benefit from data analysis tools and answer important questions in the fields of economics. , body politics and culture.

➡️ In these 24 educational videos , you will get to know the basic principles of statistics and probability, and then modern data analysis techniques will be discussed, and you will learn topics such as regression analysis, machine learning, and data visualization.


Everything in this course is free ! To access the educational videos of this course + slides and assignments, you can use the following links: 👇

┌ 🏷 Data Analysis for Social Scientists
├ 🌐 Homepage
└ 🎬 Lecture Videos

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⚡️⚡️⚡️⚡️⚡️⚡️⚡️⚡️

The missing link of a professional data scientist!
✅ "Data Storytelling" skill

☄️ One of the skills that I always advise children to work on strengthening in data science courses is the skill of "data storytelling" !

🎯 As a data scientist, you should be able to present your project after completion, through "data visualization, attractive report writing, and various charts and tables, in the form of an attractive story to the employer and other members of the organization, and this information to convey the truth to them!

🗂️ I have put here a collection of pamphlets, files and reports that can help you a lot to strengthen this skill!

┌ 🏷 Data Storytelling
├ 📚 Storytelling with data
├ 📚 Storytelling with data practice
├ 📚 Guide to becoming a Data Storyteller
├ 📚 The Data Storytelling Handbook
├ 📚 Data Storytelling Report 2021
├ 📚 A Guide to Effective Data Storytelling
├ 📚 8 rules for better data storytelling
└ 📚 Data Storytelling in SDG Reporting

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🟢 7 valuable resources that I used to prepare for data science interviews!

🟢 One of the most important factors to get data science jobs in the best companies is success in job interviews.

🗂 I have put here 7 valuable resources that helped me a lot while preparing for data science interviews. I hope these resources can help you succeed in data science interviews


1️⃣ machine learning
📕 Link: Machine Learning


2️⃣ Python programming language
📕 Link: Python Programming Language


3️⃣ SQL programming language
📕 Link: SQL Programming Language


4️⃣ R programming language
📕 Link: R Programming Language


5️⃣ Pandas library
📕 Link: Pandas Python Library


6️⃣ NumPy library
📕 Link: NumPy Python Library


7️⃣ Matplotlib library
📕 Link: Matplotlib Python Library

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👨‍💻 My data science training source

👀 I have been taking courses related to data science, such as Python programming, machine learning, deep learning, natural language processing, etc. for several years, and after reviewing online courses, reading various books, educational blogs, etc. .. I decided to put all the content that made me progress during this time in the Github repo that I put the link below.

📂 Also, I have designed the scripts both in the presentation format (such as PowerPoint) and in the course notes format.

┌ 🏷 Teaching Data Science
└ 📂 GitHub-Repos

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🐻Plotting with Pandas series 🐼

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