Machine Learning Engineer
Become an ML engineer — ML workflows, linear algebra, regression, classification, tree models, deep learning, NLP, and production model deployment. 18 phases from problem framing to ML system design interviews.
Your Progress
Complete all 18 phases to master this track
Become an ML engineer — ML workflows, linear algebra, regression, classification, tree models, deep learning, NLP, and production model deployment. 18 phases from problem framing to ML system design interviews.
Phase 1: ML Problem Types and Project Workflow
Frame a business question as an ML problem and set up a reproducible training workflow.
Supervised vs Unsupervised Learning
Contrast label-driven supervised learning with structure-finding unsupervised methods.
Regression vs Classification Targets
Decide whether a task predicts continuous values or discrete class labels.
Train, Validation, and Test Splits
Partition data so tuning and final evaluation stay statistically honest.
ML Project Lifecycle
Run a project from problem framing through evaluation with clear checkpoints.
Python ML Data Stack
Set up NumPy, pandas, and scikit-learn for day-to-day modeling work.
Phase 2: Linear Algebra for Machine Learning
Apply the vectors, matrices, and decompositions that models rely on internally.
Scalars, Vectors, and Matrices
Recognize the core array structures used to represent features and parameters.
Matrix Multiplication and Transpose
Manipulate matrix shapes correctly when deriving and implementing models.
Dot Products, Norms, and Projections
Measure similarity and length with the operations distance models depend on.
Eigenvalues and SVD
Use spectral decompositions that power PCA and low-rank approximations.
Phase 3: Calculus and Optimization Foundations
Use derivatives and gradient descent to understand how models minimize loss.
Derivatives and Gradients
Compute rates of change and gradient vectors for scalar loss functions.
Chain Rule for Backpropagation
Differentiate composed functions to derive how gradients flow through networks.
Gradient Descent Fundamentals
Step down the loss surface by updating parameters against the gradient.
SGD and Minibatches
Estimate gradients from subsets of data to train large models efficiently.
Optimization Landscape Challenges
Diagnose saddle points, local minima, and momentum-driven convergence.
Phase 4: Probability and Statistics for ML
Reason about uncertainty with probability distributions, Bayes' theorem, and hypothesis tests.
Probability Foundations
Apply axioms, independence, and conditional probability to model events.
Descriptive Statistics and EDA
Summarize and visualize datasets before committing to a modeling approach.
Key Distributions for ML
Match normal, Bernoulli, binomial, and Poisson models to real data generating processes.
Bayesian Thinking for Models
Update beliefs with Bayes' theorem and understand probabilistic classifiers.
Hypothesis Tests and Confidence Intervals
Judge whether experiment results are real with p-values and intervals.
Phase 5: Data Preparation and Feature Engineering
Clean raw data, engineer features, and prevent leakage with reproducible pipelines.
Missing Values and Outliers
Impute or drop missing data and detect points that would skew training.
Categorical Encoding
Convert category columns into numeric features without inventing false order.
Feature Scaling and Normalization
Bring features onto comparable scales for distance- and gradient-based models.
Feature Engineering Recipes
Transform raw columns into signal-rich inputs with domain-aware features.
Data Leakage Prevention
Spot features that cheat by encoding information unavailable at prediction time.
Sklearn Pipelines and Reproducibility
Chain transforms and estimators so experiments rerun identically anywhere.
Phase 6: Linear and Polynomial Regression
Fit and evaluate linear models while diagnosing bias from correlated or nonlinear features.
Simple Linear Regression
Fit a line to one predictor and interpret its slope and intercept.
Multiple Regression and Coefficients
Model several predictors at once and read their adjusted effects on the target.
Polynomial Regression and Underfitting
Raise feature degrees to fit curvature while spotting overfitting early.
Regression Evaluation Metrics
Choose among MAE, RMSE, and R-squared to match the cost of errors.
Ridge, Lasso, and Elastic Net
Shrink or zero out coefficients to stabilize correlated-feature models.
Phase 7: Logistic Regression and Classification Metrics
Train probabilistic classifiers and select metrics that match business error costs.
Logistic Regression Fundamentals
Model class probabilities with log-odds, a sigmoid, and cross-entropy loss.
Multi-class Strategies
Extend binary classifiers to many classes with one-vs-rest or softmax.
Confusion Matrix, Precision, and Recall
Break predictions into four outcomes and pick the metric your errors demand.
ROC Curve and Threshold Selection
Evaluate ranking quality and tune decision cutoffs without retraining.
Class Imbalance Remedies
Handle skewed labels with weighting, resampling, and threshold calibration.
Phase 8: Decision Trees and Ensemble Methods
Build tree models and ensembles that set strong baselines on structured data.
Decision Tree Learning
Grow trees with purity-based split criteria and understand their bias.
Tree Pruning and Hyperparameters
Control tree growth with pre-pruning stops or cost-complexity trimming.
Random Forests
Average decorrelated trees over bootstrap samples for robust predictions.
Gradient Boosting Machines
Chain shallow trees that each correct the residual errors of the ensemble.
Tree-Based Feature Importance
Rank input features with impurity and permutation importance measures.
Phase 9: Support Vector Machines and Nearest Neighbors
Classify with distance-based and margin-based models and know when each struggles.
Distance and Similarity Metrics
Compare feature vectors with Euclidean, Manhattan, and cosine measures.
k-Nearest Neighbors Classification
Classify points by voting among their closest labeled training examples.
Support Vector Max-Margin Classifiers
Find the decision boundary that maximizes separation between classes.
The Kernel Trick in SVMs
Reach non-linear decision boundaries without computing high-dimensional maps.
Phase 10: Clustering Algorithms
Segment unlabeled data with centroid, hierarchical, and density clustering methods.
k-Means Clustering
Partition data into k groups by iteratively updating centroid assignments.
Choosing the Cluster Count
Pick k with elbow curves, silhouette scores, and business interpretability.
Hierarchical Clustering
Merge clusters progressively and read groupings from a dendrogram.
DBSCAN Density Clustering
Group dense regions, mark noise points, and find arbitrarily shaped clusters.
Cluster Evaluation and Profiling
Validate segments and profile them into findings stakeholders can act on.
Phase 11: Dimensionality Reduction and Feature Selection
Compress features with PCA and select inputs that keep models fast and generalizable.
Curse of Dimensionality
Understand why adding features thins data and breaks distance-based intuition.
Principal Component Analysis
Project features onto orthogonal axes ranked by explained variance.
Feature Selection Methods
Filter, wrap, or embed feature choices to drop noise before training.
t-SNE and UMAP Visualization
Collapse high-dimensional data into 2D maps for exploratory analysis.
Phase 12: Neural Network Fundamentals
Train multilayer networks with backpropagation and a modern framework workflow.
Perceptron and Multilayer Networks
Compose simple units into hidden layers that model non-linear relationships.
Activation Functions
Choose nonlinearities that keep gradients flowing through deep stacks.
Output Layers and Loss Pairings
Match final-layer activations and loss functions to each task type.
Backpropagation and the Training Loop
Run forward and backward passes and step the optimizer on each batch.
Building a Network in PyTorch
Define modules, manage train/eval modes, and checkpoint model weights.
Phase 13: Convolutional Networks for Vision
Build convolutional networks for image tasks with augmentation and transfer learning.
Convolution and Pooling
Slide learnable filters over images and downsample feature maps.
CNN Architecture Patterns
Stack conv, activation, and pooling blocks into effective vision models.
Data Augmentation and Transfer Learning
Stretch small image datasets and reuse pretrained backbones efficiently.
Vision Model Error Analysis
Inspect misclassified images with domain experts to guide the next iteration.
Phase 14: Sequence Models and Transformers Overview
Understand how recurrent models, attention, and transformers process sequences.
Sequence Modeling Basics
Frame tasks where outputs depend on ordered historical inputs.
RNNs, LSTMs, and GRUs
Carry hidden state through time and gate it to learn long dependencies.
Attention Mechanisms
Weight every input by its relevance to the current output position.
Transformer Architecture Overview
See how self-attention and positional encodings replace recurrence.
Phase 15: Model Evaluation and Experiment Tracking
Evaluate models rigorously and track experiments with artifacts reviewers can audit.
Cross-Validation Strategies
Rotate validation splits to estimate generalization on limited data.
Bias-Variance and Error Analysis
Read training curves to tell underfitting from overfitting and next moves.
Experiment Tracking Tools
Log parameters, metrics, and artifacts so runs stay comparable over time.
Model Cards and Documentation
Document intended use, limits, and subgroup results for reviewers.
Communicating Results to Stakeholders
Translate offline metrics into business impact leaders can act on.
Phase 16: Hyperparameter Tuning and Regularization
Tune hyperparameters and regularize models to close generalization gaps.
Grid and Random Search
Sweep parameter spaces systematically or stochastically under a budget.
Early Stopping and Learning Rate Schedules
Cap training at peak validation performance and decay step sizes over time.
Regularization with L1, L2, and Dropout
Constrain model capacity so trained networks generalize beyond training data.
Bayesian Hyperparameter Optimization
Guide searches with a surrogate objective and prune unpromising trials.
Diagnosing the Generalization Gap
Read train-versus-validation curves and choose the right corrective lever.
Phase 17: MLOps: Packaging, Serving, and Monitoring
Package, version, serve, and monitor models so they stay reliable after deployment.
Model Serialization and Packaging
Export trained models into portable artifacts with pinned dependencies.
Model Registry and Versioning
Track model versions, promotion stages, and their lineage metadata.
Online and Batch Serving
Choose between request-time endpoints and offline scoring pipelines.
CI/CD for ML Pipelines
Automate tests, training checks, and gated promotion into production.
Production Monitoring and Drift Detection
Watch inputs, predictions, and latency for degradation after release.
Phase 18: Production Capstone and ML Interview Preparation
Ship an end-to-end capstone and answer ML design and interview questions with confidence.
End-to-End Capstone Project
Deliver a full pipeline from raw data to a monitored serving endpoint.
Serving Trade-offs: Latency and Cost
Balance inference speed, throughput, and spend when deploying models.
ML System Design Interviews
Structure ML design answers around data, features, training, and serving.
ML Interview Question Patterns
Practice derivations, metric choices, and project narratives interviewers probe.
Portfolio and Reproducibility
Publish repositories that reviewers can run and evaluate quickly.