Machine Learning Roadmap

  • Step 1

    Python Fundamentals

    Grasp syntax, control flow, functions, and essential libraries like NumPy.

  • Step 2

    Math Foundations

    Review linear algebra, calculus, probability, and statistics for data modeling.

  • Step 3

    Data Handling & Visualization

    Work with data using Pandas and visualize insights with Matplotlib or Seaborn.

  • Step 4

    Exploratory Data Analysis

    Clean data, engineer features, and explore patterns before modeling.

  • Step 5

    Machine Learning Algorithms

    Implement supervised and unsupervised techniques using scikit-learn.

  • Step 6

    Model Evaluation & Tuning

    Use cross-validation, metrics, and hyperparameter search to improve models.

  • Step 7

    Deep Learning

    Build neural networks with TensorFlow or PyTorch for complex tasks.

  • Step 8

    Deployment & MLOps

    Serve models via APIs, monitor performance, and maintain data pipelines.