Python Roadmap for Beginners in 2024

This roadmap is for beginners specifically, with no advanced topics.
I also added links to FREE learning resources from our website and the community.

With any suggestions on what to add/change, email me povilas@pythonmldaily.com.

PART 1. PYTHON FUNDAMENTALS Must learn these, whatever Python specialization you choose below.
Topics	Learning Resources
Core syntax: variables, operators, conditions, loops Data structures: lists, sets, dictionaries, tuples Working with files Working with strings Project structure: functions, importing modules/libraries	[Our Course] Python 101 for PHP Developers [YouTube Video] Programming with Mosh: Python for Beginners - Learn Python in 1 Hour [Learning Path] Real Python: Introduction to Python
PART 2. CHOOSE YOUR SPECIALIZATION From here, you need to choose what Python projects you want to work with. There are different use-cases, with totally separate libraries to learn. Below you see the main options I suggest.

Option 1. Data Science. You'll work with data transformation/automation projects. This is also fundamental knowledge to dive into Machine Learning, later.
Topics	Learning Resources
Data Transformation with Pandas library	[Course] Kaggle: Learn Pandas [YouTube] FreeCodeCamp: Pandas & Python for Data Analysis by Example – Full Course for Beginners [Learning Path] Real Python: Pandas for Data Science [YouTube] Our videos about Pandas
Data Visualization with Matplotlib/Seaborn libraries	[YouTube] NeuralNine: Matplotlib Full Python Course - Data Science Fundamentals [Course] Kaggle: Data Visualization with Seaborn [Tutorial] Analytics Vidhya: Introduction to Matplotlib using Python for Beginners
Data Calculation with NumPy library	[Official Docs] NumPy: the absolute basics for beginners [YouTube] FreeCodeCamp: Python NumPy Tutorial for Beginners
Option 2. AI / ML: Using Pre-Built Models Your projects may involve using off-the-shelf models like OpenAI GPT, Llama, etc.
Topics	Learning Resources
Working with LLMs, OpenAI and GPT	[Our course] Your First Python Chatbot with GPT-3.5 and LangChain [Our course] Python Chatbot: Ask Database of Cars with OpenAI GPT
Natural Language Processing (NLP): spaCy/NLTK libraries	[Our Video] SpaCy vs NLTK vs Scikit-Learn: CV/Job Match Experiment with Python
OCR and Image/Video Processing	[Our Tutorial] Python OCR: Invoice Data from PDF/Image - 4 Libraries Tested
Option 3. Machine Learning: Building Your Own Models You will use algorithms and libraries to build and train your own ML models.
Topics	Learning Resources
Basic ML: Supervised learning Scikit-learn library Regression and Classification	[Our course] Intro to Machine Learning with Linear Regression in Python [GitHub Course] Microsoft: ML For Beginners [YouTube] FreeCodeCamp: Machine Learning with Python and Scikit-Learn – Full Course
Advanced ML: Unsupervised learning Reinforcement learning Deep learning Tensorflow/PyTorch/Keras and other libraries	Here I will intentionally not provide any links. This topic is totally not for beginners. But if you get to this level, you will definitely find resources, depending on which ML field you choose.
Option 4. Web-Development In our Python ML Daily project, we don't focus on web-solutions. But it's still a very popular usage of Python.
Topics	Learning Resources
Web Apps and APIs with Flask framework	[Tutorial] Real Python: Build a Scalable Flask Web Project From Scratch [YouTube] Dave Gray: How to Create a Web Application in Python using Flask [Our Tutorial] Publish ML Model as API or Web Page: Flask and Digital Ocean
Web projects with Django framework	[Official Docs] Writing your first Django app [Tutorial] Real Python: Get Started With Django: Build a Portfolio App [YouTube] Programming with Mosh: Python Django Tutorial for Beginners

Video version of this roadmap, with explanations: