Python AI & ML Roadmap

Python Basics

Syntax, variables, data types, operators, input/output, program structure.

Control Flow

Conditionals, loops, functions, recursion, basic problem solving.

Checkpoint — Python Foundations

Data Structures

Lists, tuples, sets, dictionaries, comprehensions.

OOP in Python

Classes, objects, inheritance, encapsulation, polymorphism.

Checkpoint — Python Programming

NumPy

Arrays, vectorization, numerical operations, broadcasting.

Pandas

DataFrames, data cleaning, transformation, analysis.

Checkpoint — Data Handling

Data Visualization

Matplotlib, Seaborn, plotting techniques, dashboards.

Statistics for ML

Probability, distributions, hypothesis testing, correlations.

Checkpoint — Analytical Foundations

Machine Learning

ML Fundamentals

Supervised vs unsupervised learning, ML workflow.

ML Algorithms

Linear & logistic regression, KNN, SVM, decision trees.

Checkpoint — Core Machine Learning

Ensemble Models

Random Forest, Gradient Boosting, XGBoost.

Model Evaluation

Cross-validation, metrics, bias-variance tradeoff.

Checkpoint — Model Optimization

ML Tools

Scikit-learn, Jupyter, Google Colab.

ML Deployment Basics

Flask/FastAPI, REST APIs, model serving concepts.

Checkpoint — Industry Ready ML Engineer