Machine Learning Roadmap

Source: Dev.to

Foundations of Machine Learning

Machine Learning stands on three fundamental pillars:

1. Mathematics
2. Statistics
3. Programming

Without these foundations, ML becomes just black‑box code. With them, you understand how models work — and how to optimize them.

Mathematics

Linear Algebra — Matrix Manipulation

• Vectors
• Matrices
• Dot products
• Eigenvalues
• PCA & SVD

Calculus — Optimization & Gradients

• Derivatives
• Partial derivatives
• Chain rule
• Gradient descent

Probability — Modeling Uncertainty

• Distributions
• Random variables
• Bayes’ theorem

Statistics — Understanding Data

• Mean, variance
• Hypothesis testing
• Confidence intervals
• Correlation

Data Manipulation Skills

NumPy

• Vectorization
• Broadcasting
• Matrix operations

Pandas

• Cleaning data
• Merging
• Grouping
• Time‑series operations

Matplotlib

• Histograms
• 2D/3D plots

Seaborn

• Heatmaps
• Pairplots
• Correlations

Core Machine Learning Branches

1. Supervised Learning – regression, classification, neural networks.
2. Unsupervised Learning – clustering, dimensionality reduction, anomaly detection.
3. Reinforcement Learning – agents, robotics, decision‑making.

Project‑Based Learning

Build small projects to apply concepts:

• Classifiers
• Clustering visualizations
• NLP pipelines
• Recommender systems

Stay Updated with Hugging Face Research

Keep up with the latest:

• New models
• Tutorials
• Research summaries
• Benchmarks

Final Thoughts

Machine Learning is built on math, statistics, programming, data skills, and real‑world projects. Master the foundations and you become a strong ML engineer.