Machine Learning Roadmap
The roadmap from programming beginner to machine learning practitioner — covering the math, Python, classical ML, and deep learning needed for real ML work.
Phase 1: Phase 1 — Prerequisites
Python
Strong Python fundamentals including OOP, list comprehensions, and the scientific stack.
Linear Algebra
Vectors, matrices, matrix multiplication, eigenvalues — the math behind neural networks.
Calculus
Derivatives, chain rule, and gradients — the math behind backpropagation.
Statistics & Probability
Probability distributions, Bayes' theorem, maximum likelihood estimation.
Phase 2: Phase 2 — Classical Machine Learning
Supervised Learning
Linear regression, logistic regression, decision trees, random forests, and SVMs.
Unsupervised Learning
K-means clustering, PCA, dimensionality reduction, and anomaly detection.
Model Evaluation
Train/validation/test splits, cross-validation, and performance metrics.
Feature Engineering
Encoding, scaling, imputation, and creating informative features from raw data.
Phase 3: Phase 3 — Deep Learning
Neural Networks
Perceptrons, activation functions, backpropagation, and gradient descent.
CNNs
Convolutional networks for image recognition and computer vision.
RNNs & Transformers
Sequence models, attention mechanisms, and the architecture behind GPT and BERT.
TensorFlow or PyTorch
Build and train neural networks using a major deep learning framework.
Phase 4: Phase 4 — Applied ML
MLOps
Experiment tracking, model versioning, deployment pipelines, and monitoring.
LLMs & Fine-tuning
Work with large language models, prompt engineering, and fine-tuning techniques.
Capstone Project
Build an end-to-end ML system from data collection to deployed API.