Step-by-Step Learning Stages
Math, Exploratory Data Analysis (EDA) & SQL
Master Linear Algebra, Probability Distributions, Hypothesis Testing, SQL queries, Pandas, NumPy, and Seaborn for data visualization.
Classical Machine Learning Algorithms
Learn Linear/Logistic Regression, Decision Trees, Random Forests, Gradient Boosted Trees (XGBoost, LightGBM), K-Means Clustering, and PCA with Scikit-Learn.
Feature Engineering & Data Pipelines
Master target encoding, outlier detection, scaling, imputation, feature stores (Feast), and automated data pipelines using PySpark and dbt.
MLOps, Experiment Tracking & Model Registry
Track metrics and model versions with MLflow and Weights & Biases. Version large dataset artifacts using DVC (Data Version Control).
Deployment, Serving & Drift Monitoring
Package models into containerized FastAPI services with Docker. Detect data and concept drift using Evidently AI, Prometheus, and Grafana.
Recommended Portfolio Projects
3 hands-on projects to demonstrate production ML capabilities.
Real Estate Price Prediction Service
Clean property dataset, train XGBoost model with cross-validation, package into a FastAPI REST endpoint, and containerize with Docker.
E-Commerce Customer Churn Pipeline
Track hyperparameter tuning runs with MLflow, build automated Scikit-learn preprocessing pipelines, and render predictions via Streamlit.
End-to-End Automated Credit Scoring MLOps Platform
Combine Feast feature store, DVC data versioning, MLflow model registry, Airflow automated retraining, and Evidently AI drift alerts.