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Published 18 weeks

Machine Learning Engineer

Machine Learning Engineer roles focused on data preparation, classical machine learning algorithms, feature engineering, evaluation, tuning, interpretation, and portfolio-ready projects.

Register interest All six tracks

Before you start

What this track assumes

Where you learned these does not matter. That you have them does, because the first weeks move quickly.

  • Python, SQL, Git, and Linux basics
  • Algebra, probability, statistics, and optimization fundamentals
  • Ability to complete weekly coding projects and maintain a GitHub portfolio
  • Comfort with notebooks, source-code files, documentation, and reproducible environments

The roadmap

5 phases across 18 weeks

Each phase ends in a checkpoint: a portfolio artifact, not a quiz.

Phase Focus Checkpoint
01 Weeks 1 to 3 Python, SQL, Git, notebooks, data cleaning Clean EDA repository
02 Weeks 4 to 6 Mathematics, statistics, probability, optimization basics From-scratch regression exercise
03 Weeks 7 to 11 Supervised learning and feature engineering Two business prediction projects
04 Weeks 12 to 15 Unsupervised learning, anomaly detection, evaluation Segmentation or anomaly project
05 Weeks 16 to 18 Portfolio polish, documentation, interview practice Final GitHub portfolio review

The syllabus

8 modules, in order

Every module names the skills it covers, the methods and tools it uses by name, the project that follows it, and what you hand in.

01

Python, SQL, and Data Handling Foundations

Skills
  • Python functions, OOP basics, type hints, virtual environments
  • NumPy, pandas, Polars basics, data cleaning and joins
  • SQL joins, aggregations, CTEs, window functions
  • Git workflow, Linux shell basics, pytest basics
Methods

Foundation for reproducible machine learning work

Project
Build a clean data ingestion, validation, and exploratory analysis pipeline for a messy customer, sales, or operations dataset.
Deliverables
  • Clean repository structure
  • Exploratory analysis notebook
  • Data validation checks
  • Unit-tested utility functions

02

Mathematics and Statistics for Machine Learning

Skills
  • Linear algebra: vectors, matrices, dot product, matrix multiplication
  • Probability: distributions, conditional probability, Bayes rule
  • Statistics: mean, standard deviation, correlation, sampling, confidence intervals
  • Optimization: gradients, loss functions, learning rate intuition
Methods and tools
  • Gradient Descent
  • Stochastic Gradient Descent
  • Mini-batch Gradient Descent
  • Adam optimizer concept
  • Maximum Likelihood Estimation
  • Bayesian inference basics
Project
Implement linear regression with gradient descent from scratch and validate the result against a library implementation.
Deliverables
  • From-scratch implementation
  • Loss curve
  • Metric summary
  • Short explanation of convergence

03

Data Preparation and Feature Engineering

Skills
  • Missing-value handling, outlier handling, duplicate checks
  • Encoding: one-hot, ordinal, target encoding awareness
  • Scaling: StandardScaler, MinMaxScaler, RobustScaler
  • Train, validation, and test split with leakage prevention
Methods and tools
  • Imputation: mean, median, mode, KNN imputation
  • Feature selection: mutual information, chi-square, ANOVA F-test
  • Dimensionality reduction: PCA
  • Sampling: random, stratified, SMOTE awareness
Project
Create a reusable preprocessing workflow for a tabular business dataset and document every transformation.
Deliverables
  • Preprocessing notebook
  • Feature dictionary
  • Data leakage checklist
  • Before-and-after metric summary

04

Supervised Learning: Regression

Skills
  • Regression problem framing and metric selection
  • Regularization and the bias-variance trade-off
  • Residual analysis and error analysis
  • Business interpretation of numeric predictions
Methods and tools
  • Linear Regression
  • Ridge Regression
  • Lasso Regression
  • ElasticNet
  • Decision Tree Regressor
  • Random Forest Regressor
  • Gradient Boosting Regressor
  • XGBoost Regressor
  • LightGBM Regressor
  • CatBoost Regressor
  • Support Vector Regression
  • KNN Regressor
Project
Predict price, cost, delivery time, or customer value using structured data.
Deliverables
  • Baseline model
  • Tuned final model
  • MAE, RMSE, and R2 summary
  • Residual analysis

05

Supervised Learning: Classification

Skills
  • Binary and multiclass classification workflows
  • Class imbalance, threshold selection, and calibration awareness
  • Confusion matrix, precision, recall, F1, ROC-AUC, PR-AUC
  • Feature importance and classification error analysis
Methods and tools
  • Logistic Regression
  • K-Nearest Neighbors
  • Naive Bayes
  • Decision Tree Classifier
  • Random Forest Classifier
  • Extra Trees Classifier
  • Support Vector Machine
  • Gradient Boosting Classifier
  • XGBoost Classifier
  • LightGBM Classifier
  • CatBoost Classifier
  • AdaBoost
Project
Build a churn, loan-risk, lead-scoring, fraud-risk, or support-ticket classification model.
Deliverables
  • Clean training notebook
  • Metric table
  • Confusion matrix
  • Error-analysis notes

06

Unsupervised Learning and Anomaly Detection

Skills
  • Distance metrics and clustering assumptions
  • Cluster validation and cluster profiling
  • Dimensionality reduction for visualization
  • Outlier detection for suspicious or unusual records
Methods and tools
  • K-Means
  • MiniBatch K-Means
  • Hierarchical Clustering
  • DBSCAN
  • HDBSCAN
  • Gaussian Mixture Models
  • PCA
  • t-SNE
  • UMAP
  • Isolation Forest
  • One-Class SVM
  • Local Outlier Factor
Project
Create customer segmentation and anomaly detection for transaction, usage, or operational data.
Deliverables
  • Cluster profile summary
  • Silhouette and Davies-Bouldin scores
  • Two-dimensional visualization
  • Outlier investigation notes

07

Model Evaluation, Tuning, and Interpretation

Skills
  • Cross-validation and validation strategy
  • Hyperparameter tuning without leakage
  • Metric selection by business objective
  • Interpreting important drivers and common failure cases
Methods and tools
  • Grid Search
  • Randomized Search
  • Bayesian Optimization
  • Optuna and TPE
  • Platt Scaling
  • Isotonic Regression
  • Permutation Importance
  • Partial Dependence Plot
  • SHAP basics
  • LIME basics
Project
Tune and interpret a tabular machine learning model for a real business-style decision problem.
Deliverables
  • Cross-validation results
  • Tuning configuration
  • Final metric summary
  • Failure-case notes

08

Portfolio, Communication, and Interview Readiness

Skills
  • Writing clear READMEs and project summaries
  • Explaining metrics to technical and non-technical audiences
  • Answering machine learning interview questions with trade-offs
  • Maintaining clean GitHub repositories
Methods

Communication and job readiness, no new algorithms

Project
Polish three machine learning repositories into a job-ready portfolio with clear problem statements and results.
Deliverables
  • Portfolio README
  • Project summary pages
  • Interview question notes
  • Resume project bullets

What you end up with

2 capstone projects

  • Business Prediction System

    A complete classical machine learning project for churn, risk, lead scoring, customer value, price, or cost prediction.

    • Exploratory analysis and data validation
    • Feature engineering workflow
    • Baseline and tuned final model
    • Metric summary and error analysis

  • Segmentation and Anomaly Analysis

    Unsupervised learning to segment users, products, or transactions and identify unusual records worth investigation.

    • Cluster profiles
    • Anomaly list with reasoning
    • Business recommendations
    • Visual summary

Read the full syllabus

Register interest and we will send the complete week-by-week document, then arrange a short call to work out whether this is the right track.

Register interest