👩‍💻 Python Developer Roadmap is a guide for aspiring Python developers that helps structure and plan their learning and career development!

🌟 It provides a step-by-step plan that covers key aspects of Python development, from basic knowledge and syntax to more advanced topics such as databases, web development, testing, machine learning, and microservices development.

🔐 License: MIT

🖥 Github

https://t.me/CodeProgrammer ✅

🎯 All free IBM courses for data science
✅ Along with a certificate of completion


1️⃣ Data Science Fundamentals Course

✏️ Learn basic data science concepts such as analysis, modeling, and its real-world applications.
✂️✂️✂️✂️✂️

2️⃣ Applied Data Science Course with Python

✏️ Learn how to use Python for data analysis, modeling, and practical projects.
✂️✂️✂️✂️✂️

3️⃣ Data Analysis Course with Python

✏️ Data analysis skills using Python libraries such as Pandas and NumPy.
✂️✂️✂️✂️✂️

4️⃣ Data visualization course with Python

✏️ Learn to create advanced charts with tools like Matplotlib and Seaborn.
✂️✂️✂️✂️✂️

5️⃣ Applied Data Science Course with R

✏️ Using the R language to analyze data and implement data science projects.
✂️✂️✂️✂️✂️

6️⃣ Data visualization course with R

✏️ Learn how to create professional charts and visualize data with tools like ggplot2.
✂️✂️✂️✂️✂️

7️⃣ Big Data Fundamentals Course

✏️ Learn the fundamentals of big data and related technologies such as Hadoop and Spark.
✂️✂️✂️✂️✂️

8️⃣ Scala Programming Course for Data Science

✏️ Familiarity with the Scala language and its use in data analysis projects.
✂️✂️✂️✂️✂️

9️⃣ Data Science for Business Course

✏️ Learn how to use data to improve business decisions.
✂️✂️✂️✂️✂️

1️⃣ Deep Learning Fundamentals Course

✏️ Familiarity with the basics of deep learning and the concepts of neural networks.
✂️✂️✂️✂️✂️

1️⃣ Deep Learning Course with TensorFlow

✏️ Working with TensorFlow to build and train deep learning models.

https://t.me/CodeProgrammer ✅

✅ A comprehensive playlist to step into and master the world of machine learning and data science!


1️⃣ Data Science Principles:

😉 Essential Mathematics for Machine Learning: Link
😉 Overview and commonly used terms: Link
😉 Current interview trends: Link
😉 Linear Regression Guide: Link
😉 Logistic Regression Playlist: Link
😉 Classification criteria: Link
😉 Simple Bayes Classifier: Link
😉 Types of variables: Link
😉 Dimension reduction: Link
😉 Entropy, mutual entropy, KL divergence: link
😉 Dynamic Pricing Overview: Link


2️⃣ Building recommender systems:

😉 Netflix Calibrated Recommendations: Link
😉 Netflix Integrated Recommendation Model: Link
😉 The Evolution of Recommender Systems: Link
😉 Embedding tutorial: Link
😉 Annoy library for approximate nearest neighbor: link
😉 Reducer product for ANN: Link
😉 Model-based account recommendations: Link
😉 PID controller for diversity: link
😉 Instagram Recommender System: Link
😉 LinkedIn CTR Modeling: Link
😉 Meituan's two-tower recommendation model: Link
😉 Scalable Two Tower Model Question-Item: Link
😉 Twitter Recommender Algorithm: Link
😉 eBay language model for recommender system: link
😉 Overcoming biases for recommender systems: Link


3️⃣ Advanced Model Techniques and Applications:

😉 Importance of Model Calibration: Link
😉 Detect and monitor data changes: Link
😉 Neural Networks Training: Link
😉 Analytics-based advertising with Pinterest: Link
😉 Using Pre-trained Bert: Link
😉 Model Compression with Knowledge Distillation: Link
😉 Multi-Armed Bandit Strategies: Link


4️⃣ The world of large language models (LLMs):

😉 Conversational AI: Link
😉 The dual nature of conversational language models: link
😉 Frontier Developments in LLM: Link
😉 Improving the performance of open source LLMs: Link
😉 Building artificial intelligence in Shah Rukh Khan style: Link

📂 Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Pandas

http://t.me/codeprogrammer ⭐️

Data Visualization Cheat sheets and Resources

Corpus of 32 DV cheat sheets, 32 DV charts and 7 recommended DV books

📂 Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Pandas #DV

http://t.me/codeprogrammer ⭐️

IP Address Information using Python 🖥

📂 Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Pandas #DV

http://t.me/codeprogrammer ⭐️

🚀 Free Python Course with Certificate🚀

Unlock the world of coding with our
Python Fundamentals for Beginners course—completely FREE! 🚀 Whether you're new to programming or want to brush up on your skills, this course has everything you need to get started.

Why Choose This Course?
💡 100% FREE—No hidden fees
💡 Flexible learning at your own pace
💡 Certificate of completion to boost your resume
💡 Practical, hands-on learning with real examples

👉 Apply now - https://t.ly/6IvgN

📂 Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Pandas #DV

http://t.me/codeprogrammer ⭐️

Python Cheatsheets ⭐️

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📂 Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Pandas #DV

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Python Notes ⭐️

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📂 Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Pandas #DV

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🎥 Free course "Computational Thinking and Data Science"
📊 MIT University

👨🏻‍💻 One of the best resources I've found for learning computational thinking and data science is this free course from MIT. It covers concepts like data analysis, computational modeling, and using algorithms to solve complex problems. I've included the links to the slides and videos from the course below:👇

📄 Slides link: Lecture Slides and Files

📹 Video links: Lecture Videos

📂 Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Pandas #DV #MIT

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👨🏻‍💻 One of the most popular GitHub repositories for "learning and using algorithms in Python" is The Algorithms - Python repo with 196K stars.

✏️ It has a lot of organized and categorized code that you can use to find, read, and run different algorithms. Everything you can think of is here; from simple algorithms like sorting to advanced algorithms for machine learning, artificial intelligence, neural networks, and more.

✅ Why should we use it?

🔢 For learning: If you're looking to learn algorithms in action, this is great.

🔢 For practice: You can take the codes, run them, and modify them to better understand.

🔢 For projects : You can even use the codes here in real-life or academic projects.

🔢 For interviews: If you're preparing for data science interviews, this is full of practical algorithms.


┌ 🏳️‍🌈 The Algorithms - Python
└ 🐱 GitHub-Repos

📂 Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Pandas #DV #MIT

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15 ways to optimize neural network training

📂 Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Pandas #DV #MIT

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GANs clearly explained with visuals

This website provides a clear explanation, Try it out yourself: poloclub.github.io/ganlab/

📂 Tags: #DataScience #Python #ML #AI #LLM #Courses #Pandas #DV #GAN

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🪙 Everything you need to get started in machine learning.

Start with Python, explore scikit-learn, and neural networks using PyTorch. Perfect for beginners—get the skills you need to advance your career in just a few hours.

Start for free ▶️ ow.ly/HBtl50UtwCA

📂 Tags: #DataScience #Python #ML #AI #LLM #Courses #Pandas #DV #GAN

http://t.me/codeprogrammer ⭐️

Arcade Academy - Learn Python 🖥

📖 Book

📂 Tags: #DataScience #Python #ML #AI #LLM #Courses #Pandas #DV #GAN

http://t.me/codeprogrammer ⭐️

pandas Project: Make a Gradebook With Python & pandas

Link: https://realpython.com/pandas-project-gradebook/

📂 Tags: #DataScience #Python #ML #AI #LLM #Courses #Pandas #DV #GAN

http://t.me/codeprogrammer ⭐️

👩‍💻 algorithms is a useful repository with a collection of algorithms implemented in Python!

🌟 It covers a wide range of algorithmic topics, including sorting, searching, graph manipulation, data structures, dynamic programming, cryptography, and more. The main goal of the repository is to provide an educational resource for learning algorithms and improving programming skills.

🔐 License: MIT

🖥 Github

https://t.me/CodeProgrammer ✅

👩‍💻 A simple explanation of working with list in Python!

https://t.me/CodeProgrammer ✅

📊 Data Visualization Learning Roadmap

👩🏻‍💻 Many people ask me where to start to learn "data visualization"?

✏️ The truth is that data visualization has its own language that we need to learn. We need to start with the basics and then gradually move on to more complex topics.

✅ I have used some great resources along the way that I am still learning from. With every project, consultation, or course I take, my experiences grow. Here is a summary of the useful resources that have helped me:👇


1️⃣ Part One: Articles and Books:

📑 Full article by Lisa Charlotte Muth

🔗 Article link: started data visualization

➖➖➖➖➖➖➖➖➖

2️⃣ Books:

📚 Book: Storytelling with Data

📚 Book: Practical Charts

➖➖➖➖➖➖➖➖➖

3️⃣ Learning communities:

👤 Membership link: Storytelling with Data

☑️ Membership link: DataVis Community

➖➖➖➖➖➖➖➖➖

4️⃣ Podcasts:

🌟 Link: SWD Podcast
🌟 Link: Data Viz Today
🌟 Link: PolicyViz

➖➖➖➖➖➖➖➖➖

5️⃣ Key resources:

📚 Data Visualization Guide: This guide includes great resources for learning and developing data visualization skills.

➖➖➖➖➖➖➖➖➖

6️⃣ Training courses:

📣 Effective data visualization / Learning to turn data into art.

📣 Information design /storytelling with data in Illustrator.

📣 Data Visualization / This course provides creative tools and techniques for visualization.

➖➖➖➖➖➖➖➖➖

7️⃣ Data visualization tools:

⬅️ Link: Scimago Graphica
⬅️ Link: Datawrapper
⬅️ Link: Flourish
⬅️ Link: Tableau Public
⬅️ Link: RStudio & Python

https://t.me/CodeProgrammer ✅

NumPy Practical Examples: Useful Techniques

Link: https://realpython.com/numpy-example/

#numpy #python

https://t.me/CodeProgrammer ⭐🐍