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: Prerequisites
Python
Strong Python fundamentals including OOP, list comprehensions, and the scientific stack.
Linear Algebra
Vectors, matrices, matrix multiplication, eigenvalues. These are 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: 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: 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: 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.
Keep exploring
Languages in this roadmap