Python

Python Zero-to-Hero

A seven-phase mastery path — from 'what is programming?' to production services, data science, machine learning and the interview — every lesson hands-on.

Beginner → Intermediate → Advanced → Expert → Specialist ~180 hours 71 of 71 lessons ready PCEP · PCAP (Python Institute) · Microsoft/AWS/Google data & ML certifications — plus real backend, data and MLOps interview readiness
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A complete, beginner-friendly yet in-depth path through Python, built for people who learn by doing. Phase 1 starts at absolute zero — algorithms and problem solving, installing Python, syntax, control flow, functions, data structures, files and virtual environments. Phase 2 is intermediate Python and full object-oriented programming. Phase 3 is what separates a scripter from an engineer: Git, packaging, pytest, APIs, databases, CLIs, concurrency and design patterns. Phase 4 is data science with NumPy, Pandas, Matplotlib/Seaborn and statistics. Phase 5 is machine learning and AI end to end — scikit-learn, deep learning, computer vision, NLP, transformers and deployment. Phase 6 puts Python to work in web, automation, cloud/DevOps and specialized domains. Phase 7 is mastery and career: system design, open source, DSA interviews and a portfolio that gets you hired.

What you’ll be able to do

  • Write correct, idiomatic Python from first principles — syntax, control flow, functions, data structures, files and error handling
  • Design software with object-oriented and functional techniques: classes, inheritance, dataclasses, decorators, generators and clean domain models
  • Ship professional Python: Git workflow, packaging, pytest, HTTP APIs, databases, CLIs, concurrency (threads/processes/asyncio) and design patterns
  • Analyse real data with NumPy, Pandas, Matplotlib/Seaborn and the statistics that underpin sound conclusions
  • Build, evaluate and deploy machine-learning and deep-learning models — scikit-learn, PyTorch/Keras, computer vision, NLP and MLOps serving
  • Apply Python to web APIs, automation, the AWS/Azure/GCP SDKs, CI/CD and specialized domains like IoT and data engineering
  • Pass technical interviews and present a portfolio for cloud, DevOps, backend and AI/ML roles

Prerequisites

  • No programming experience required — Phase 1 starts with what an algorithm is
  • A computer where you can install Python 3.12+ (Windows, macOS or Linux) — every lesson's lab runs locally in a virtual environment

Who it’s for

Complete beginners who have never written a line of code, engineers from other languages who want real Python fluency, cloud/DevOps engineers who need Python for automation and tooling, and anyone moving into data science, machine learning or backend development.

Curriculum

Phase 1 · Foundations

Start at absolute zero — what programming and an algorithm even are — then install Python and build the core: syntax and types, control flow, functions, lists/tuples/dicts/sets, strings, file I/O, modules, pip and virtual environments. Every lesson ends with a runnable lab.

  1. 1 What Is Programming? Algorithms, Problem Solving & How a Computer Thinks 45 min read
  2. 2 Installing Python & Choosing Your Environment: CLI, IDEs, Jupyter & Your First Script 41 min read
  3. 3 Python Syntax & Data Types: Variables, int/float/bool/str/None, Conversion & Scope 48 min read
  4. 4 Control Flow: if/elif/else, for & while Loops, Logical Operators & Short-Circuiting 51 min read
  5. 5 Functions: Parameters, Return Values, Default & Keyword Arguments 42 min read
  6. 6 Lambda Functions & Higher-Order Concepts: Functions as First-Class Objects 45 min read
  7. 7 Lists & Tuples: Indexing, Slicing, Methods & Immutability 55 min read
  8. 8 Dictionaries & Sets: Key-Value Storage, Iteration, Membership & Uniqueness 44 min read
  9. 9 Strings In Depth: Methods, Formatting & f-strings 48 min read
  10. 10 File I/O & Basic Error Handling: Text vs Binary, Context Managers, try/except 51 min read
  11. 11 Modules & Packages: Imports, Writing Your Own & the Standard Library Tour 58 min read
  12. 12 pip & Virtual Environments: Isolating Dependencies the Right Way 46 min read

Phase 2 · Intermediate & OOP

Think like a Python programmer: comprehensions and generators, algorithmic thinking and Big-O, then object-oriented programming in four parts (classes, inheritance/polymorphism, encapsulation, domain models with dataclasses), exceptions and logging, functional Python (closures, decorators, iterators), and real data formats (JSON/CSV, datetime, regex).

  1. 13 Comprehensions & Generator Expressions: Filtering, Mapping & Accumulating 72 min read
  2. 14 Algorithmic Thinking: Search, Sort & Big-O in Python 71 min read
  3. 15 OOP Part 1 — Classes, Objects, Attributes & Methods 58 min read
  4. 16 OOP Part 2 — Inheritance, Method Overriding, Polymorphism & Abstraction 60 min read
  5. 17 OOP Part 3 — Encapsulation, Class vs Instance Variables, Static & Class Methods 61 min read
  6. 18 OOP Part 4 — Designing Domain Models: Dunder Methods, dataclasses & Composition 76 min read
  7. 19 Exceptions In Depth: Hierarchy, Custom Exceptions & try/except/else/finally 68 min read
  8. 20 Logging & Debugging: The logging Module, pdb & Debugging Strategy 74 min read
  9. 21 Functional Python: map/filter/reduce, Closures & Decorators 77 min read
  10. 22 Iterators & Generators: The Iterator Protocol, yield & Lazy Pipelines 69 min read
  11. 23 Working with Data: JSON, CSV & Serialization 72 min read
  12. 24 datetime & Regular Expressions: Time Handling and Text Processing 75 min read

Phase 3 · Professional Python

The difference between someone who writes scripts and someone who ships software: Git and GitHub, project structure and packaging, testing with pytest, consuming HTTP APIs, databases and SQLAlchemy, CLI tools, concurrency (threads/processes/asyncio and the GIL), profiling, and design patterns that make code modular and testable.

  1. 25 Development Workflow: Git & GitHub for Python Projects 82 min read
  2. 26 Project Structure, Packaging & Documentation: src layout, pyproject.toml & READMEs 70 min read
  3. 27 Testing Python: unittest, pytest, Fixtures, Mocking & Coverage 85 min read
  4. 28 Consuming HTTP APIs with requests: Sessions, Auth, Retries & Errors 61 min read
  5. 29 Databases with Python: SQLite, PostgreSQL & SQLAlchemy 85 min read
  6. 30 Building CLI Tools: argparse, click & Packaging a Command 76 min read
  7. 31 Concurrency: Threading vs Multiprocessing and the GIL 59 min read
  8. 32 async/await with asyncio — and Profiling & Performance Optimization 63 min read
  9. 33 Software Design Patterns in Python: Factory, Strategy, Observer & Friends 64 min read
  10. 34 Modular, Testable Code: SOLID, Layering & Dependency Injection in Real Projects 60 min read

Phase 4 · Data Science

Turn Python into an analysis engine: NumPy vectorization, Pandas for real data wrangling (indexing, groupby, merges, missing data), Matplotlib and Seaborn for charts that communicate, the statistics you actually need, and two end-to-end data projects from raw CSV/API to a report and pipeline.

  1. 35 NumPy: Arrays, Broadcasting, Vectorization & Basic Linear Algebra 50 min read
  2. 36 Pandas Part 1 — Series, DataFrames, Indexing & Selection 42 min read
  3. 37 Pandas Part 2 — GroupBy, Aggregation, Merging & Missing Data 55 min read
  4. 38 Data Visualization with Matplotlib: Figures, Axes & Real Charts 47 min read
  5. 39 Seaborn: Statistical Plots & Charts That Actually Communicate 41 min read
  6. 40 Statistics for Data Science Part 1 — Descriptive Statistics & Distributions 53 min read
  7. 41 Statistics Part 2 — Hypothesis Testing, Correlation & Regression 68 min read
  8. 42 Data Project: Analyzing a Real Dataset End-to-End (CSV + API) 55 min read
  9. 43 Building a Data Pipeline & Reports/Dashboards 57 min read

Phase 5 · ML & AI

Machine learning from fundamentals to deployment: the ML taxonomy, splits and evaluation metrics, scikit-learn end-to-end (preprocessing pipelines, the core algorithms, cross-validation and tuning), deep learning with PyTorch/Keras, computer vision, NLP and transformers, advanced architectures and RL, MLOps serving — capped with a full data→model→evaluation→deployment project.

  1. 44 ML Fundamentals: Supervised vs Unsupervised, Regression, Classification & Clustering 57 min read
  2. 45 Training, Validation & Test Splits — and Model Evaluation Metrics 59 min read
  3. 46 scikit-learn Part 1 — Preprocessing: Scaling, Encoding & Pipelines 59 min read
  4. 47 scikit-learn Part 2 — The Core Algorithms: Linear Models, SVM, Trees, Ensembles & k-NN 65 min read
  5. 48 scikit-learn Part 3 — Cross-Validation & Hyperparameter Tuning 55 min read
  6. 49 Deep Learning Basics: Neural Network Intuition, Backprop & Training 59 min read
  7. 50 Your First Deep Learning Models with PyTorch (and Keras) 55 min read
  8. 51 Computer Vision Part 1 — Image Processing with OpenCV & Pillow 52 min read
  9. 52 Computer Vision Part 2 — CNNs for Image Classification & Object-Detection Basics 55 min read
  10. 53 NLP Part 1 — Text Preprocessing & Tokenization with NLTK and spaCy 56 min read
  11. 54 NLP Part 2 — Text Classification, Sentiment & an Intro to Transformers 52 min read
  12. 55 Advanced Architectures: CNN, RNN/LSTM, Transformers & Reinforcement Learning Basics 66 min read
  13. 56 Deploying Models: Serving APIs, Containers & MLOps Basics 65 min read
  14. 57 AI Project: End-to-End — Data → Model → Evaluation → Deployment 64 min read

Phase 6 · Other Domains

Where Python actually earns its living: web APIs with FastAPI/Flask (plus databases and auth), automation and scraping, internal ops tooling, the cloud SDKs (boto3, azure-sdk, google-cloud), infrastructure automation, CI/CD integration, and specialized domains — IoT/MQTT, ROS 2 robotics, and batch/streaming data engineering.

  1. 58 Web Development: Flask & FastAPI — Building REST APIs 61 min read
  2. 59 Web Part 2 — Databases, Authentication & Taking an API to Production 58 min read
  3. 60 Automation & Scripting: Files, Excel, PDFs & Email 50 min read
  4. 61 Web Scraping & Building Internal Tools for Ops/DevOps 57 min read
  5. 62 Python with Cloud SDKs: boto3 (AWS), azure-sdk & google-cloud 58 min read
  6. 63 Infrastructure Automation with Python: Real Ops Scripts 62 min read
  7. 64 Python in CI/CD: GitHub Actions, GitLab CI & Jenkins Integration 59 min read
  8. 65 Specialized Domains: IoT with MQTT & Robotics with ROS 2 66 min read
  9. 66 Data Engineering with Python: Batch & Streaming Pipelines 68 min read

Phase 7 · Mastery & Career

Become the engineer who gets hired: system design with Python, production performance/reliability/maintainability, contributing to open source and writing reviewable code, DSA patterns for coding interviews, and building a portfolio that lands cloud/DevOps/AI roles.

  1. 67 System Design with Python: Designing Scalable Services 77 min read
  2. 68 Performance, Reliability & Maintainability in Production Python 64 min read
  3. 69 Open Source & Community: Contributing, Clean Code & Code Review 76 min read
  4. 70 Coding Interviews: DSA Patterns & Solving Problems in Python 72 min read
  5. 71 Building a Portfolio & Passing Technical Interviews (Cloud/DevOps/AI Roles) 60 min read
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