💡 A complete package for success in data science and machine learning interviews!

👩🏻‍💻 I found a GitHub repo full of resources you need to succeed in Data Science and Machine Learning interviews!

❓ What do you find in it?

1⃣ Practical cheat sheets: Important tips gathered in one place.

🔢 Cool books: resources worth your time!

🔢 Frequently Asked Interview Questions: Topics that are asked in most interviews and that you are likely to encounter.

🔢 Portfolio projects: To make your resume stronger.


✅ In short, a complete package for preparing for data science interviews, without the confusion!

🔗 Here is the repost link: 👇

🔗 Cracking the data science interview

#DataScience #MachineLearning #InterviewPrep #CareerGrowth #TechResources #GitHubRepo #CheatSheets #PortfolioProjects #InterviewQuestions #DataScientists #SuccessTips #TechCareer #CodingLife #LearnAndGrow #InterviewReady

https://www.tgoop.com/CodeProgrammer 🦾

🔴 Free Python course for GenAI


👩🏻‍💻 Guys , GenAI Community has created a free Python training course where you will learn both Python and its application in generative artificial intelligence!


✏️ The classes are live, but the videos of the classes are recorded and you can access them in your user panel. They last 10 to 12 weeks and are completely free!

✅ If you want to learn Python from scratch and enter the world of Generative Artificial Intelligence (GenAI), this course is for you! You don't need a programming background, just a desire to learn!

🥵 What will you learn in this course?

✅ Python concepts: conditionals, loops, data structures
✅ Working with APIs, File Management, and Regex
✅ Object-oriented programming and modular coding
✅ And a whole host of other skills that will pave your way into the world of artificial intelligence and data science.

🔔 I should also mention that this community grew 5 times last year and is now one of the largest AI communities in the world. In the last hackathon they held, 5,000 people from all over the world participated and a prize of $25,000 was distributed among them.

📣 It's completely free! No cost, no limits, with a community of people eager to learn.👇
🔗 Learn Python for FREE (GenAI Python)

Here are some suitable hashtags for the description in English, in a single line:

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https://www.tgoop.com/CodeProgrammer 🐍

🚀 Master Linear Algebra for Data Science with Gilbert Strang’s Essential Guide!

Discover "Linear Algebra for Data Science" – an 80-page powerhouse booklet by MIT’s legendary professor Gilbert Strang. Crafted during the Corona era as part of his ZoomNotes series, this concise resource distills practical linear algebra concepts into clear, actionable insights.

📘 What’s Inside?
- Core topics: Matrix Factorizations, Eigenvalues, Vector Spaces, and more!
- Data Science connections: Learn how linear algebra powers ML, statistics, and optimization.
- Perfectly complements Strang’s MIT video lectures for a seamless learning experience.

💡 Why Choose This Booklet?
- MIT-approved clarity: Strang’s teaching brilliance shines in every page.
- Compact & focused: Skip the fluff – dive straight into key concepts.
- Timeless resource: Ideal for students, professionals, and lifelong learners.

🔗 Grab your copy and unlock the math behind data-driven innovation!

#LinearAlgebra #DataScience #MachineLearning #MachineLearningIT #GilbertStrang #MathForAI #STEMEducation #MatrixMath #Eigenvalues #VectorSpaces #LearnMath #OnlineLearning #MathResources #DataAnalytics #ZoomNotes

Boost your skills with Strang’s genius – where theory meets real-world application! 🌟

https://www.tgoop.com/CodeProgrammer ✈️

⚠️ O'Reilly Media, one of the most reputable publishers in the fields of programming, data mining, and AI, has made 10 data science books available to those interested in this field for free .

✔️ To use the online and PDF versions of these books, you can use the following links:👇

0⃣ Python Data Science Handbook
┌ Online
└ PDF

1⃣ Python for Data Analysis book
┌ Online
└ PDF

🔢 Fundamentals of Data Visualization book
┌ Online
└ PDF

🔢 R for Data Science book
┌ Online
└ PDF

🔢 Deep Learning for Coders book
┌ Online
└ PDF

🔢 DS at the Command Line book
┌ Online
└ PDF

🔢 Hands-On Data Visualization Book
┌ Online
└ PDF

🔢 Think Stats book
┌ Online
└ PDF

🔢 Think Bayes book
┌ Online
└ PDF

🔢 Kafka, The Definitive Guide
┌ Online
└ PDF

#DataScience #Python #DataAnalysis #DataVisualization #RProgramming #DeepLearning #CommandLine #HandsOnLearning #Statistics #Bayesian #Kafka #MachineLearning #AI #Programming #FreeBooks

https://www.tgoop.com/CodeProgrammer ✅

🚀 80 Python Interview Questions with Answers & Code! 🚀

✅ Why this resource? 
- Covers frequently asked questions in Python interviews 

📄 Each question comes with detailed answers and ready-to-use code snippets, making it perfect for beginners and experienced developers alike. Whether you're preparing for a job interview or leveling up your Python skills, this guide has you covered! 👀 

🔥 Don’t miss out! Save this, share it, and start preparing today! 💼 

#Python #DataScience #Programming #InterviewPrep #Coding #PythonInterview #TechInterview #DataScientist #PythonProgramming #LearnPython #CodeNewbie #CareerGrowth #TechJobs #PythonCode #PythonTips 

https://www.tgoop.com/CodeProgrammer

Applied Machine Learning in Python: a Hands-on Guide with Code 🧠

🚀 Exciting news! free, online e-book has been updated with fresh theory 📕, detailed illustrations 🎨, well-documented demos 📝, links to YouTube lectures 🎥, and interactive dashboards 📊!

Each chapter is downloadable 📥, making it easy for you to dive in, learn, and complete your #DataScience projects efficiently! 🧑‍💻

Explore it now: https://geostatsguy.github.io/MachineLearningDemos_Book/intro.html

#DeepLearning #Python #MachineLearning #AI #DataAnalytics #TechEducation #FreeLearning #Ebook #DataVisualization #Coding #STEM #TechCommunity #LearnToCode

https://www.tgoop.com/CodeProgrammer

Free Certification Courses to Learn Data Analytics in 2025:

1. Python
🔗 https://imp.i384100.net/5gmXXo

2. SQL
🔗 https://edx.org/learn/relational-databases/stanford-university-databases-relational-databases-and-sql

3. Statistics and R
🔗 https://edx.org/learn/r-programming/harvard-university-statistics-and-r

4. Data Science: R Basics
🔗https://edx.org/learn/r-programming/harvard-university-data-science-r-basics

5. Excel and PowerBI
🔗 https://learn.microsoft.com/en-gb/training/paths/modern-analytics/

6. Data Science: Visualization
🔗https://edx.org/learn/data-visualization/harvard-university-data-science-visualization

7. Data Science: Machine Learning
🔗https://edx.org/learn/machine-learning/harvard-university-data-science-machine-learning

8. R
🔗https://imp.i384100.net/rQqomy

9. Tableau
🔗https://imp.i384100.net/MmW9b3

10. PowerBI
🔗 https://lnkd.in/dpmnthEA

11. Data Science: Productivity Tools
🔗 https://lnkd.in/dGhPYg6N

12. Data Science: Probability
🔗https://mygreatlearning.com/academy/learn-for-free/courses/probability-for-data-science

13. Mathematics
🔗http://matlabacademy.mathworks.com

14. Statistics
🔗 https://lnkd.in/df6qksMB

15. Data Visualization
🔗https://imp.i384100.net/k0X6vx

16. Machine Learning
🔗 https://imp.i384100.net/nLbkN9

17. Deep Learning
🔗 https://imp.i384100.net/R5aPOR

18. Data Science: Linear Regression
🔗https://pll.harvard.edu/course/data-science-linear-regression/2023-10

19. Data Science: Wrangling
🔗https://edx.org/learn/data-science/harvard-university-data-science-wrangling

20. Linear Algebra
🔗 https://pll.harvard.edu/course/data-analysis-life-sciences-2-introduction-linear-models-and-matrix-algebra

21. Probability
🔗 https://pll.harvard.edu/course/data-science-probability

22. Introduction to Linear Models and Matrix Algebra
🔗https://edx.org/learn/linear-algebra/harvard-university-introduction-to-linear-models-and-matrix-algebra

23. Data Science: Capstone
🔗 https://edx.org/learn/data-science/harvard-university-data-science-capstone

24. Data Analysis
🔗 https://pll.harvard.edu/course/data-analysis-life-sciences-4-high-dimensional-data-analysis

25. IBM Data Science Professional Certificate
https://imp.i384100.net/9gxbbY

26. Neural Networks and Deep Learning
https://imp.i384100.net/DKrLn2

27. Supervised Machine Learning: Regression and Classification
https://imp.i384100.net/g1KJEA

#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #SupervisedLearning #IBMDataScience #FreeCourses #Certification #LearnDataScience

https://www.tgoop.com/CodeProgrammer ✈️

The Big Book of Large Language Models by Damien Benveniste

✅ Chapters:
1⃣ Introduction

🔢 Language Models Before Transformers

🔢 Attention Is All You Need: The Original Transformer Architecture

🔢 A More Modern Approach To The Transformer Architecture

🔢 Multi-modal Large Language Models

🔢 Transformers Beyond Language Models

🔢 Non-Transformer Language Models

🔢 How LLMs Generate Text

🔢 From Words To Tokens

1⃣0⃣ Training LLMs to Follow Instructions

1⃣1⃣ Scaling Model Training

1⃣🔢 Fine-Tuning LLMs

1⃣🔢 Deploying LLMs

Read it: https://book.theaiedge.io/

#ArtificialIntelligence #AI #MachineLearning #LargeLanguageModels #LLMs #DeepLearning #NLP #NaturalLanguageProcessing #AIResearch #TechBooks #AIApplications #DataScience #FutureOfAI #AIEducation #LearnAI #TechInnovation #AIethics #GPT #BERT #T5 #AIBook #AIEnthusiast

https://www.tgoop.com/CodeProgrammer

🔰 How to become a data scientist in 2025?

👨🏻‍💻 If you want to become a data science professional, follow this path! I've prepared a complete roadmap with the best free resources where you can learn the essential skills in this field.


🔢 Step 1: Strengthen your math and statistics!

✏️ The foundation of learning data science is mathematics, linear algebra, statistics, and probability. Topics you should master:

✅ Linear algebra: matrices, vectors, eigenvalues.

🔗 Course: MIT 18.06 Linear Algebra


✅ Calculus: derivative, integral, optimization.

🔗 Course: MIT Single Variable Calculus


✅ Statistics and probability: Bayes' theorem, hypothesis testing.

🔗 Course: Statistics 110

➖➖➖➖➖

🔢 Step 2: Learn to code.

✏️ Learn Python and become proficient in coding. The most important topics you need to master are:

✅ Python: Pandas, NumPy, Matplotlib libraries

🔗 Course: FreeCodeCamp Python Course

✅ SQL language: Join commands, Window functions, query optimization.

🔗 Course: Stanford SQL Course

✅ Data structures and algorithms: arrays, linked lists, trees.

🔗 Course: MIT Introduction to Algorithms

➖➖➖➖➖

🔢 Step 3: Clean and visualize data

✏️ Learn how to process and clean data and then create an engaging story from it!

✅ Data cleaning: Working with missing values ​​and detecting outliers.

🔗 Course: Data Cleaning

✅ Data visualization: Matplotlib, Seaborn, Tableau

🔗 Course: Data Visualization Tutorial

➖➖➖➖➖

🔢 Step 4: Learn Machine Learning

✏️ It's time to enter the exciting world of machine learning! You should know these topics:

✅ Supervised learning: regression, classification.

✅ Unsupervised learning: clustering, PCA, anomaly detection.

✅ Deep learning: neural networks, CNN, RNN


🔗 Course: CS229: Machine Learning

➖➖➖➖➖

🔢 Step 5: Working with Big Data and Cloud Technologies

✏️ If you're going to work in the real world, you need to know how to work with Big Data and cloud computing.

✅ Big Data Tools: Hadoop, Spark, Dask

✅ Cloud platforms: AWS, GCP, Azure

🔗 Course: Data Engineering

➖➖➖➖➖

🔢 Step 6: Do real projects!

✏️ Enough theory, it's time to get coding! Do real projects and build a strong portfolio.

✅ Kaggle competitions: solving real-world challenges.

✅ End-to-End projects: data collection, modeling, implementation.

✅ GitHub: Publish your projects on GitHub.

🔗 Platform: Kaggle🔗 Platform: ods.ai

➖➖➖➖➖

🔢 Step 7: Learn MLOps and deploy models

✏️ Machine learning is not just about building a model! You need to learn how to deploy and monitor a model.

✅ MLOps training: model versioning, monitoring, model retraining.

✅ Deployment models: Flask, FastAPI, Docker

🔗 Course: Stanford MLOps Course

➖➖➖➖➖

🔢 Step 8: Stay up to date and network

✏️ Data science is changing every day, so it is necessary to update yourself every day and stay in regular contact with experienced people and experts in this field.

✅ Read scientific articles: arXiv, Google Scholar

✅ Connect with the data community:

🔗 Site: Papers with code
🔗 Site: AI Research at Google

#ArtificialIntelligence #AI #MachineLearning #LargeLanguageModels #LLMs #DeepLearning #NLP #NaturalLanguageProcessing #AIResearch #TechBooks #AIApplications #DataScience #FutureOfAI #AIEducation #LearnAI #TechInnovation #AIethics #GPT #BERT #T5 #AIBook #AIEnthusiast

https://www.tgoop.com/CodeProgrammer

MIT's "Machine Learning" lecture notes

PDF: https://introml.mit.edu/_static/spring24/LectureNotes/6_390_lecture_notes_spring24.pdf

#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #SupervisedLearning #IBMDataScience #FreeCourses #Certification #LearnDataScience

https://www.tgoop.com/CodeProgrammer ✈️

Practical Deep Learning

A free course designed for people with some coding experience, who want to learn how to apply deep learning and machine learning to practical problems.
New!

Enroll Free: https://course.fast.ai/

#DeepLearning #MachineLearning #FreeCourse #CodingSkills #AI #ArtificialIntelligence #PracticalAI #TechEducation #LearnToCode #DataScience #NeuralNetworks #PythonProgramming #TechInnovation #SelfPacedLearning #FutureSkills

https://www.tgoop.com/CodeProgrammer 👩‍💻

Running a Neural Network Model in OpenCV

Many machine learning models have been developed, each with strengths and weaknesses. This catalog is not complete without neural network models. In OpenCV, you can use a neural network model developed using another framework. In this post, you will learn about the workflow of applying a neural network in OpenCV. Specifically, you will learn:

🏐 What OpenCV can use in its neural network model
🏐 How to prepare a neural network model for OpenCV

Read: https://machinelearningmastery.com/running-a-neural-network-model-in-opencv/

#NeuralNetworks #OpenCV #MachineLearning #AI #DeepLearning #ModelDeployment #ComputerVision #TechTutorials #DataScience #MLModels

https://www.tgoop.com/CodeProgrammer