Python & Machine Learning Roadmap
1. Getting Started
- Syntax & comments
- Variables
- Data types (int, float, str, bool)
- User input (
input()) - Basic operations &
print()
2. Data Structures & Functions
- Loops (
for,while) - Lists (create, access, modify)
- Tuples, sets, dictionaries (intro)
- Functions (
def, parameters, return) - Practice: mini programs using loops and functions
3. Introduction to Data Science Libraries
- NumPy: arrays, basic operations
- pandas: DataFrame, data loading, filtering
- matplotlib: basic plotting (line, bar, scatter)
- Data visualization basics
- Simple data manipulation examples
4. Introduction to AI & Machine Learning
- What is AI? Basic concepts
- What is Machine Learning?
- Types: supervised vs unsupervised learning
- ML workflow: data, training, testing
- Simple ML example with scikit-learnNumPy: arrays, basic operations
5. Simple Machine Learning Application
- Collect and prepare data
- Choose a simple model (e.g., decision tree, linear regression)
- Train the model with training data
- Test and evaluate model accuracy
- Make predictions on new data
6. Mini AI Project Steps
- Define the problem and collect data
- Clean and preprocess the data
- Choose and train a simple ML model
- Evaluate the model performance
- Deploy or demonstrate the model with new inputs