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Best Roadmap To Learn Python Programming

Table of Contents

1. Module 1: Python Basics

   • Variables in Python
   • Strings and Variables in Python
   • Accepting input from users in Python
   • Operators in Python
   • If statement
   • if else statement
   • elif statement in Python
   • For loop in Python
   • while loop in Python
   • Break statement in Python
   • continue statement
   • String properties in Python
   • List [] in Python
   • List [] methods in Python
   • Tuples in Python
   • Dictionaries in Python
   • Functions in Python
   • Modules in Python
   • User define functions with argument
   • Round of Modules and Functions
   • Time Module in Python
   • Example of time module in Python

2. Module 2: OOP Concepts

   • Types of variables & methods
   • Classes
   • Objects
   • Inheritance
   • Encapsulation
   • Polymorphism
   • Abstraction
   • Interface

3. Module 3: Package Managers

   • Pip
   • Conda
   • UV

4. Module 4: DSA

   • Arrays
   • Linked lists
   • Stacks
   • Queues
   • Binary search trees
   • Balanced binary trees
   • Heaps
   • Dictionaries
   • Tries
   • Graph algorithms
     • Graph traversal algorithms: BFS and DFS
     • Shortest paths
     • Spanning trees
   • Hash Tables
   • Recursion
   • Sorting algorithms

5. Module 5: Linux

   • Download Virtualbox or Vmware
     • Download Virtualbox
     • Download Vmware
   • Download & Install Ubuntu
     • Download & Install Ubuntu
   • Refresh Linux commands
     • Refresh Linux commands
   • Learn to use Docker
     • Learn to use Docker
     • Docker-curriculum
     • Download docker-desktop
   • Learn to use Rancher (Docker alternative)
     • Learn to use Rancher
     • Rancher Desktop
   • Learn Kubernetes
     • Learn Kubernetes

6. Module 6: Automation

   • File Manipulation
   • Web Scraping
   • GUI Automation
   • Network Automation

7. Module 7: Types of Testing

   • Unit Testing
   • Integration Testing
   • End-to-end testing
   • Load Testing
     • With Locust

8. Module 8: Advanced/pythonbooks

   • Pip in python
   • What is PYTHONPATH
   • Enumeration | Enumerate in python
   • Deep Copy and Shallow Copy in python
   • What is Python environments | What is the Need of Python Virtual Environments
   • Python Virtual Environment | How to Create Python Virtual Environment
   • List Comprehansion in Python | List Comprehension Python
   • List Comprehension in Python | Chaining in python | If-Else in List Comprehension Python
   • Dictionary Comprehension in Python
   • List Comprehension in Python | if-elif-else in List Comprehension
   • How to Install Pycharm on Windows 10
   • Function in Python
   • How to Create Function in Python
   • Advantages of Functions
   • Difference Between Parameters and Arguments
   • Local Variables in Python
   • Global Variables in Python
   • Everything about global keyword and UnboundLocalError in Python
   • return Statement in Python | Returning Value From Function
   • Positional Arguments in Python | Types of Arguments
   • Keyword Arguments in Python | Default Arguments in Python
   • Variable Length Arguments in Python | Types of Arguments in Python
   • Lambda Function in Python | Anonymous Function in Python
   • Nested Lambda Function in Python
   • IIFE Function in Python
   • First Class Functions in Python
   • Function as Parameter | Function as Argument
   • Returning Function from Function in Python | How to return function
   • Globals function in python | globals() in python
   • Filter() in python | Higher Order functions in python
   • Filter() in python Using Lambda() | Higher Order() in python
   • Filter() example | filter() in python
   • Map() in python | Higher Order() in python
   • Reduce() | Higher Order() in python
   • Recursion in python | What is recursion
   • Recursion in python | Factorial Program using recursion
   • Recursion in python | What is Direct revursion in python | Types of recursion
   • Fibonacci Series using recursion
   • Namespace in python | What is that
   • Nonlocal variable in python | nonlocal keyword in python
   • Closures in python | what is that
   • Decorator in python | Multiple Decorators on a () |
   • Smart division using decorator
   • If__name__=='main'|__main__Usage in Python
   • Exception Handling in python | Try-Except block
   • Exception Handling in python | Printing Exception information in output
   • Exception Hierarchy in python
   • Raise keyword in python | raise Statement in python
   • Creating user-defined exceptions in python
   • User defined exception example
   • Use exception Handling
   • Excepthook in python
   • Exception Handling best practices
   • Pickling and Unpickling in Python
   • What is JSON Data Format | JSON in python | Working With JSON in python
   • JSON to python dictionary conversion
   • HAndling constants in python projects
   • Memory management in python | Stack vs Dynamic Memory
   • Reference counting in python
   • Multithreading in python | Types of Multi Tasking
   • What is Main Thread in python
   • How to create threads in python
   • Creating threads for methods
   • Create threads by extending thread class | run() method
   • Thread Names, id's | Threading in python
   • Built in functions in multithreading
   • Join method in multithreading
   • Why to use multithreading
   • Race condition in python | What is that ?
   • Locking mechanism in multithreading - python
   • Rlock in multithreading | thread synchronization
   • Need of Rlock in python
   • Semaphore in python | Bounded semaphore in python
   • Exception in multithreading python
   • Lifecycle of thread in python
   • Thread communication in python | condition object | queue object
   • What is daemon thread in python
   • Timer object in python
   • Barrier object in multithreading
   • What is Logging in python
   • Logging levels | How to Log messages in python
   • How to format log messages
   • Creating Logger Object
   • Logging Exception details in python
   • Iterator in python
   • Generators in python
   • Fibonacci series using Generators
   • Chaining Generators in python
   • Python - MySQL | How to install MySQL Connector
   • How to Create Connection to MySQL Using python
   • Check Connection Between python and MySQL | Close Connection
   • Execute Method in python | Cursor Object in python
   • Show Databases using python
   • Python Databases Connectivity
   • Commit and Rollback in python - MySQL | Insert Records into MySQL Table using python Script
   • MySQL - Update Table data - Python
   • Fetchone and fetchmany method in python
   • Python MySQL Execute Parameterized Query using Prepared Statement
   • Python MySQL Execute Parameterized Query Example
   • Python MySQL Execute Parameterized Query for Dynamic Program
   • Insert JSON Data Into MySQL Table
   • Difference Between MVT and MVC | How Django Works | MVT vs MVC
   • Mutable Default Arguments in python
   • Time module in python
   • Datetime module in python
   • Timedelta in python
   • __all__in Python | Importing Module in Python
   • init.py in Python | Python Packages
   • What is Regex and Applications
   • Regex in Python | Compile() Function | Searching Function
   • Character classes in regex
   • Mixing Different Types of Arguments in Python
   • Use of Lambda () | Lambda() with sorted()
   • Implementing IIFE() in Python
   • Top 21 Special Methods and Attributes in Python

9. Module 9: Databases

   • Mastery MongoDB
   • Mastery SQL
   • Mastery Firebase
   • ETC...& Future Databases

10. Module 10: Web Frameworks

    • Django
      • Getting started with Django
    • Flask
      • Quickstart
    • FastAPI
      • First Steps
    • FastAPI vs. Flask
      • FastAPI vs. Flask feature comparison

11. Module 11: Data Modeling & Pipelines

    • What is a Data Pipeline? event-driven-architecture/data-pipeline
    • The Path to Insights: Data Models and Pipelines

12. Module 12: Data/ML/AI/IoT Tools & Best Practices Overview

    • Mastery Airflow Apache Airflow
    • Mastery Databricks Learning Library
    • Mastery SNOWFLAKE Explore Snowflake's Resource Library
    • Mastery Iguazio Bringing AI to Life
    • Mastery HuggingFace Bringing AI to Life

13. Module 13: Data science

    • Numpy
    • Pandas
    • Tensorflow Master your path
    • Pytorch Learn PyTorch for Deep Learning: Zero to Mastery book
    • Matplotlib
    • Seaborn
    • Scikit-Learn User Guide

14. Module 14: Machine Learning - Choosing The Right Algorithms

    • 𝗦𝘂𝗽𝗲𝗿𝘃𝗶𝘀𝗲𝗱 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴
      • (Data With Label)
        • Classification
          • Logistic Regression
          • Naive Bayes Classifier
          • K-Nearest Neighbour (KNN)
          • Support Vector Machine (SVM)
          • Random Forest
          • Decision Tree
            • Examples:
              • Email Spam detection
              • Speech recognition
        • Regression
          • Linear Regression
          • Ridge Regression
          • Ordinary least Squares Regression
          • Stepwise Regression
            • Examples:
              • Stock Market prediction
              • Rainfall Prediction
    • 𝗨𝗻𝘀𝘂𝗽𝗲𝗿𝘃𝗶𝘀𝗲𝗱 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴
      • (Data without Label)
        • Clustering
          • K-means clustering
          • K-median
          • Hierarchical clustering
          • Expectation Maximization
            • Examples:
              • Identifying fake news
              • Document analysis
        • Association Analysis
          • APRIORI Algorithm
          • Eclat
          • FP-Growth (Frequent Pattern growth)
            • Examples:
              • Market Basket Analysis
        • Dimensionality Reduction
          • Feature Extraction
            • Principal Component Analysis
          • Feature Selection
            • Wrapper, Filter & Embedded Method
              • Examples:
                • Analysis of written texts and DNA Method microarray data
        • Anomaly Detection
          • Z-score Algorithm
          • Isolation Forest Algorithm
    • 𝗦𝗲𝗺𝗶-𝗦𝘂𝗽𝗲𝗿𝘃𝗶𝘀𝗲𝗱 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴
      • Classification
        • Self-Training
      • Regression
        • Co-Training
    • 𝗥𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴
      • (Sate and Action)
        • Model-Free
          • Q-Learning
          • Hybrid
          • Policy optimisation
        • Model-Based
          • Learn the Model
          • Given the Model
            • Examples:
              • Multi-agent System
              • Motion Planning
              • Navigation

15. Module 15: Deep Learning (DL) & Neural Networks (NN)

    • TRANSFORMERS...Transformers from Scratch
    • Mixture-of-Recursions Mixture-of-Recursions

16. Module 16: The Backbone of Computer Vision

    • KEY LIBRARIES

      • OpenCV -Load image
        • Show image
        • Convert to Grayscale
      • Pillow (PIL) : simple image manipulation
        • Open image
      • TensorFlow PyTorch: Deep Learning frameworks for classification, detection, segmentation

    • PREPROCESSING TECHNIQUES

      • Resizing:
      • Normalization
      • Blurring
        • Global
        • Adaptive
      • Edge Detection
        • Canny

    • FEATURE DETECTION

      • SIFT/ORB: keypoints & descriptors for matching
      • HOG: Histogram of Oriented Gradients (used for detecting objects like people)

    • SEGMENTATION

      • Contours
      • Watershed

    • OBJECT DETECTION

      • Haar Cascades: Pre-trained classifiers (e.g., for faces)
      • YOLO: Real-time object detection
      • SSD: Efficient, single-shot object detection

    • NEURAL NETWORKS

      • CNNs (Convolutional Neural Networks)
        • Used for image classification and object recognition
        • key layers: Convolution, Pooling, Fully Connected
      • Pre-trained models
        • Fine-tune VGG, ResNet, or Inception for your task

    • OBJECT TRACKING

      • MeanShift: Moves window to highest pixel density
      • CamShift: Adjusts window size dynamically for tracking

    • IMAGE AUGMENTATION

      • Increase dataset size by applying transformations
      • Rotation, Flipping, Scaling, Cropping (to improve robustness)

17. Module 17: Documentation Before / LLM - Generative AI - AI Agents - Agentic AI - Agentic RAG...

AI Agents are basically 90% Software engineering and only 10% AI

• My response to AI 2027 Vitalik.eth
• AI 2027 Timelines Forecast
• What you can do about AI 2027
• The New Skill in AI is Not Prompting, It's Context Engineering
• Context Engineering For Agents context-engineering-for-agents
• Generative AI Models generative-ai-models
• LLMOps Unveiled Your Step-by-Step Guide to Building Production-Ready LLM Applications

18. Module 18: All About LLM - Generative AI - AI Agents - Agentic AI - Agentic RAG...

    • HuggingFace just released 9 elite-level AI courses for free
    • MCP with Anthropic MCP with Anthropic

1️⃣ LoRA & qLoRA fine-tuning Learn low-rank adaptation to fine-tune massive models while keeping GPU and memory usage in check. https://lnkd.in/dxAfNJjP https://lnkd.in/dcfhk4YB

2️⃣ Reinforcement Learning from Human Feedback (RLHF) Understand how to align model behavior with human expectations using curated reward signals. https://lnkd.in/da4qf5JV https://lnkd.in/drEbQ4x2

3️⃣ Reinforcement Learning from Verifiable Rewards (RLVR) Use simulated or programmatic reward models when human feedback is too expensive. https://lnkd.in/dFBgDAyd

4️⃣ RAG Fundamentals Learn how to build Retrieval-Augmented Generation systems- ingest documents, create embeddings, stitch context, and prompt LLMs. https://lnkd.in/dFkgD6RQ https://lnkd.in/d7v6DzEG

5️⃣ Advanced RAG Architectures Master multi-hop retrieval, hybrid sparse-dense search, and complex reranking for multi-document use cases. https://lnkd.in/dUrAz2D3

6️⃣ Embedding Models & Vector Databases Benchmark sentence encoders, tune indexes (FAISS/Pinecone), and understand latency vs recall trade-offs. https://lnkd.in/d8zeWbmn https://learn.pinecone.io/

7️⃣ Prompt Engineering & Chain-of-Thought Design prompts that guide reasoning step-by-step, manage templates, and dynamically inject context. https://lnkd.in/djbJD578 https://lnkd.in/d9M75H_i

8️⃣ MCP & Agent-to-Agent Integration Use Model Context Protocol (MCP) to coordinate LLMs with APIs and other agents for seamless A2A handoffs. https://lnkd.in/dfjvxhvP https://lnkd.in/dJ5NWuaK

9️⃣ Long-Context Window Strategies Handle 100k+ token sequences using smart chunking, hierarchical retrieval, and memory-augmented architectures. https://lnkd.in/djgkXGaZ

🔟 Model Efficiency: Quantization & Distillation Shrink models with post-training quantization (4/8-bit), pruning, and knowledge distillation for faster, cheaper inference. https://lnkd.in/d2iurEyv

1️⃣1️⃣ LLMOps & Model Monitoring Build CI/CD pipelines for LLM deployment, track drift, monitor hallucinations, and set up alerting for cost or performance issues. https://lnkd.in/dxik45e6

1️⃣2️⃣ Evaluation & Benchmarking Measure with perplexity, ROUGE, F1, and human evaluations. Automate benchmarks to compare models at scale. https://lnkd.in/dP_NJXg9

Other Resources

MCP with Anthropic: https://tinyurl.com/2tjtatkc

Building Vector Databases with Pinecone: https://tinyurl.com/mra8tpda

Vector Databases: https://tinyurl.com/56bjv6yw

Agent Memory: https://tinyurl.com/mk9rdefz

Deep Learning AI's RAG course: https://tinyurl.com/2s4bs3fd

Building and Evaluating RAG apps: https://tinyurl.com/mryh4bez

Building Browser Agents: https://tinyurl.com/5mtrk33t

Evaluating AI Agents: https://tinyurl.com/2kb6h3wz

Computer Use with Anthropic: https://tinyurl.com/5n6jsrhy

Multi-Agent Use: https://tinyurl.com/5n756x9k

Improving LLM Accuracy: https://tinyurl.com/43be9h9p

Agent Design Patterns: https://tinyurl.com/mrxx7f6h

Multi Agent Systems: https://tinyurl.com/2s37f3sn

Berkeley Agent MOOC: https://tinyurl.com/4fwbj3n5

Berkeley Advanced Agents MOOC: https://tinyurl.com/3em94nyw

19. Module 19: Personal /Professional Projects

    • Professional Project (In Progress)

      • Build SOPHISTICATED (ADVANCED) AI SYSTEMS For See-Docs & Thenavigo, LLC

    • Personal Projects (Repository)

20. Module 20: Bonus: Robotics

    • Robotics

21. Module 21: Security

    • Python For Security - Security is not a feature
    • Cyber Security Expert - Step by step guide to becoming a Cyber Security Expert in 2025
    • What is Offensive Security? The Complete Guide
    • Offensive Security 101: Everything You Need to Know
    • Tackling cybersecurity vulnerabilities through Secure by Design
    • Secure-by-design is not the same as safe-by-design
    • Best Practices - For AI Security Deployment
    • Python Books for Security - Python-Books-for-Security

Author

- Patrick Biyaga

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