Learning Path: Python for AI/ML
Duration: 8-10 weeks | Weekly Commitment: 15-20 hours | Target Audience: Beginners with no coding experience
Path Overview
This comprehensive path teaches you Python programming specifically for machine learning and AI applications. You’ll start with basic programming concepts and progress to data manipulation and visualization.
Phase 1: Python Fundamentals (Weeks 1-2)
Module 1.1: Python Basics
Topics: Variables, data types, operators, basic input/output
- Understanding Python and why it’s used in AI/ML
- Setting up Python environment (Anaconda, Jupyter)
- Writing your first Python program
- Variables and data types (int, float, string, bool)
- Basic operations and printing output
Practice: Write programs to solve 10 simple problems (calculations, conversions, etc.)
Module 1.2: Control Flow
Topics: If-else statements, loops, functions
- Conditional statements (if, elif, else)
- For loops and while loops
- Breaking and continuing loops
- Writing reusable functions
- Function parameters and return values
Practice Project: Build a simple calculator or number guessing game
Phase 2: Data Structures (Weeks 3-4)
Module 2.1: Lists, Tuples, and Sets
- Creating and manipulating lists
- List slicing and indexing
- Common list methods (append, remove, sort)
- Tuples (immutable lists) and when to use them
- Sets and their operations
- List comprehensions
Practice: Solve 15 problems using lists and data structures
Module 2.2: Dictionaries and Data Organization
- Creating and manipulating dictionaries
- Accessing and updating dictionary values
- Dictionary methods and operations
- Nested data structures
- Organizing code with classes (introduction)
Practice Project: Create a phonebook or inventory system using dictionaries
Phase 3: File Handling & Libraries (Weeks 5-6)
Module 3.1: Reading & Writing Files
- Opening and closing files
- Reading files (entire file, line by line)
- Writing and appending to files
- Handling file errors gracefully
- Working with different file formats (CSV, JSON)
Module 3.2: Working with Libraries
- What are libraries and why they matter
- Importing libraries (numpy, pandas intro)
- Using built-in Python libraries (os, math, random, datetime)
- Installing external libraries with pip
- Understanding documentation and finding help
Practice Project: Create a data analysis script that reads CSV files and generates reports
Phase 4: NumPy & Data Manipulation (Weeks 7-8)
Module 4.1: NumPy Arrays
- Creating numpy arrays
- Array indexing and slicing
- Array operations (add, multiply, etc.)
- Shape and reshaping arrays
- Mathematical operations on arrays
Module 4.2: Introduction to Pandas
- Creating DataFrames and Series
- Loading data from CSV files
- Exploring data (shape, columns, info)
- Basic data cleaning (handling missing values)
- Grouping and aggregating data
- Introduction to data visualization with matplotlib
Capstone Project: Load a real dataset, clean it, analyze it, and create visualizations
Optional Phase 5: Advanced Python (Week 9-10)
Topics:
- Object-oriented programming (classes and objects)
- Error handling and debugging
- Writing efficient and readable code
- Introduction to testing (unit tests)
- Virtual environments for project management
Resources for Each Phase
Phase 1-2: Foundations
Phase 3-4: Data Science Libraries
Learning Checkpoints
| Week | Checkpoint | Should Be Able To: |
|---|---|---|
| 2 | Python Fundamentals Quiz | Write loops, conditionals, functions |
| 4 | Data Structures Project | Use lists, dicts, and organize data |
| 6 | File Processing Assignment | Read/write files, use libraries |
| 8 | Data Analysis Project | Analyze datasets with pandas/numpy |
Next Steps After This Path
- Start the Deep Learning Progression path
- Or dive into AI Tools Mastery with TensorFlow and PyTorch
- Complete the Math Foundations for deeper understanding
- Build real projects from our Hands-On Projects section
