This repository is a step-by-step journey of learning Python from scratch, tailored for a seasoned developer with 9 years of experience in web development, backend systems, microservices, and design patterns, as well as expertise in Azure Cloud. With a focus on building a solid foundation in Python, this project is dedicated to exploring its applications in artificial intelligence (AI). Follow along as I document my progress, insights, and practical examples, moving from Python basics to advanced concepts in AI development.
This plan spans 30 days, dedicating 2 hours daily, progressing from Python fundamentals to AI-specific topics.
Day 1:
- Install Python and set up IDE (VS Code, PyCharm, or Jupyter).
- Write your first program:
Hello, World!. - Understand Python syntax, indentation, and comments.
Day 2:
- Variables and data types (int, float, string, boolean).
- Input and output functions.
- Practice basic arithmetic operations.
Day 3:
- Control flow:
if,elif,else. - Loops:
forandwhile. - Practice writing simple conditional and iterative programs.
Day 4:
- Lists, tuples, and sets: Creation, indexing, slicing, and methods.
- Practice operations on these data structures.
Day 5:
- Dictionaries: Keys, values, and common operations.
- Learn list comprehensions for concise code.
Day 6:
- Functions: Define and call functions, arguments, and return values.
- Explore built-in functions like
len(),sum(), andmap().
Day 7:
- Error handling: Try-except blocks.
- Practice debugging simple programs.
- Project: Create a basic calculator app.
Day 8:
- Object-Oriented Programming (OOP): Classes, objects, and methods.
- Learn
__init__()andself.
Day 9:
- Advanced OOP: Inheritance, polymorphism, and encapsulation.
- Practice building class hierarchies.
Day 10:
- File handling: Read, write, and append files.
- Explore working with CSV and JSON files.
Day 11:
- Modules and packages: Learn
import, built-in modules (math,os). - Explore
pipto install third-party libraries.
Day 12:
- Practice regular expressions using the
remodule. - Explore string formatting and manipulation techniques.
Day 13:
- Explore Python’s standard libraries (
datetime,random). - Learn how to write modular and reusable code.
Day 14:
- Project: Create a simple file organizer script using file handling and OOP.
Day 15:
- Introduction to NumPy: Arrays, indexing, and basic operations.
Day 16:
- Introduction to Pandas: Series, DataFrames, and basic data manipulation.
Day 17:
- Data visualization with Matplotlib: Line plots, bar charts, and histograms.
Day 18:
- Advanced visualization with Seaborn: Heatmaps, pair plots, and distributions.
Day 19:
- Exploratory Data Analysis (EDA): Using Pandas and visualization libraries.
Day 20:
- Project: Perform EDA on a sample dataset (e.g., Titanic dataset).
Day 21:
- Introduction to AI and Machine Learning concepts.
- Set up Scikit-learn and explore its structure.
Day 22:
- Supervised learning: Linear regression with Scikit-learn.
- Practice splitting datasets and training models.
Day 23:
- Classification: Logistic regression and decision trees.
- Evaluate models using metrics like accuracy and confusion matrix.
Day 24:
- Unsupervised learning: Clustering with K-Means.
- Practice visualizing clusters.
Day 25:
- Introduction to Neural Networks and TensorFlow.
- Build a simple feedforward neural network.
Day 26:
- Deep dive into TensorFlow/Keras: Layers, activation functions, and optimizers.
Day 27:
- Build a basic image classifier using a preloaded dataset in Keras.
Day 28:
- Natural Language Processing (NLP): Tokenization and sentiment analysis with NLTK.
Day 29:
- Project: Train a simple AI model (e.g., predicting house prices or classifying images).
Day 30:
- Review your progress and consolidate your learning.
- Plan further learning paths based on interests (e.g., deep learning, reinforcement learning).
This learning path balances foundational skills and AI-focused Python concepts while encouraging hands-on practice through small projects.