Machine Learning Roadmap 2026
6 phases · 16 weeks · build one project in every phase.
Step-by-step plan
- Python basics and functions
- NumPy arrays
- pandas data wrangling
- Matplotlib and Seaborn
- Jupyter and Git
Project: Clean and analyse a public sales dataset and publish findings as a notebook on GitHub
- Descriptive statistics and distributions
- Hypothesis testing
- Bias-variance and overfitting
- Train-test splits
- Gradient descent
Project: Implement linear regression with gradient descent in NumPy and compare it with scikit-learn
- Missing values and encoding
- scikit-learn pipelines
- Linear and logistic regression
- k-NN, Naive Bayes and SVM
- Classification metrics
Project: Build a leak-proof churn prediction pipeline with ColumnTransformer and a tuned threshold
- Decision trees and random forests
- XGBoost and LightGBM
- Cross-validation strategies
- Optuna tuning
- SHAP explanations
Project: Predict loan default with LightGBM, tune with Optuna and explain predictions using SHAP
- k-means and DBSCAN
- PCA and UMAP
- Isolation Forest anomalies
- RFM segmentation
- Cluster profiling
Project: Segment e-commerce customers using RFM features and present named segments with campaign ideas
- Saving pipelines with joblib
- FastAPI model serving
- Docker containers
- MLflow tracking and registry
- Drift monitoring with PSI
Project: Deploy the churn model as a Dockerised FastAPI service with MLflow tracking and drift checks
Practise each topic
Frequently asked questions
How long does it take to learn Machine Learning?
Following this roadmap takes about 16 weeks with regular practice. Working professionals usually need a little longer.
What should I learn first in Machine Learning?
Start with phase 1: Python basics and functions, NumPy arrays, pandas data wrangling. Build the phase project before moving on.
How do I know I am job-ready in Machine Learning?
When you can finish all phase projects on your own, solve the medium questions in the topic sheets and clear the mock interview comfortably.