Applied Machine Learning
Take machine learning out of the notebook and into production. This hands-on companion to Machine Learning Fundamentals walks you through the full applied workflow — framing a problem, building data pipelines, training and tuning models with scikit-learn, interpreting results, and deploying a model as a monitored API.
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This course includes:
- 52 hours on-demand video
- 64 articles and resources
- 40 downloadable notebooks & datasets
- 30 hands-on coding labs
- Certificate of completion
- Lifetime access
- Access on mobile and TV
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About This Course
Applied Machine Learning is the hands-on, project-based companion to our Machine Learning Fundamentals course. Where the fundamentals course focuses on theory and the math behind algorithms, this course is about doing the work: taking a messy, real-world dataset and turning it into a model that performs reliably and runs in production.
Across ten weeks you'll work through the complete applied lifecycle — framing a business question as a measurable ML problem, building reproducible data and feature pipelines, training and tuning models with scikit-learn, guarding against leakage and class imbalance, explaining model behavior with SHAP, tracking experiments, and finally packaging your model as a deployed API with basic monitoring. You'll finish with an end-to-end capstone that mirrors how ML is actually built on a working team.
What You'll Learn
- Translate a vague business goal into a precise ML problem with the right evaluation metric
- Build reproducible data and feature engineering pipelines with scikit-learn Pipelines and ColumnTransformer
- Train, compare, and tune models using proper cross-validation and hyperparameter search
- Detect and prevent data leakage and handle imbalanced classes correctly
- Explain model predictions with SHAP values and feature importance
- Track experiments and make results reproducible across runs and machines
- Package a trained model as a REST API and monitor it for drift and degradation in production
Requirements
- Completion of Machine Learning Fundamentals or equivalent understanding of core ML concepts (train/test splits, common algorithms, overfitting)
- Comfortable writing Python and working with NumPy, pandas, and scikit-learn
- Familiarity with Jupyter notebooks or a Python IDE such as VS Code
- A computer able to run Python 3.10+ (Windows, Mac, or Linux); no GPU required
- Basic command-line comfort for installing packages and running scripts
Who This Course Is For
- Learners who finished an introductory ML course and want practical, production-oriented experience
- Data analysts moving into machine learning and applied modeling roles
- Software engineers who want to build and ship ML features, not just prototype them
- Aspiring data scientists building a portfolio of end-to-end ML projects
- Working professionals who need to take models past the notebook stage into deployment
Most ML failures happen before a single model is trained. This module teaches you to turn a business question into a well-posed prediction task and choose a metric that actually reflects success.
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1.1 From Business Goal to Prediction Task
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1.2 Choosing the Right Metric: Accuracy, Precision/Recall, AUC, MAE
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1.3 Baselines and Cost-Sensitive Trade-offs
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1.4 Defining Success and Acceptance Criteria
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1.5 Lab: Framing a Churn Prediction Problem
Build feature pipelines that are clean, reusable, and safe to apply to new data. You'll use scikit-learn Pipelines and ColumnTransformer to encapsulate preprocessing so it never leaks between train and test.
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2.1 Building Pipelines with Pipeline and ColumnTransformer
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2.2 Encoding Categorical and Numeric Features
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2.3 Handling Missing Data and Outliers
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2.4 Feature Transformations, Scaling, and Text/Date Features
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2.5 Custom Transformers and Reusable Feature Code
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2.6 Lab: A Full Preprocessing Pipeline on Tabular Data
Fit a family of models — linear models, tree ensembles, and gradient boosting — and learn to compare them fairly and read the diagnostics that tell you what to fix next.
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3.1 The Estimator API and a Reproducible Training Loop
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3.2 Linear Models, Random Forests, and Gradient Boosting
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3.3 Diagnosing Bias, Variance, and Learning Curves
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3.4 Comparing Models with XGBoost and LightGBM
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3.5 Lab: Benchmarking Models on a Real Dataset
Tune models the right way. Learn the cross-validation strategies that produce trustworthy estimates and the search methods that find good hyperparameters without overfitting your validation set.
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4.1 K-Fold, Stratified, and Grouped Cross-Validation
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4.2 Grid Search vs. Randomized Search
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4.3 Bayesian Optimization with Optuna
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4.4 Nested CV and Avoiding Validation Leakage
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4.5 Lab: Tuning a Gradient Boosting Model with Optuna
The two most common reasons real models look great offline and fail in production. Learn to spot leakage in features and pipelines and to handle rare-class problems honestly.
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5.1 Sources of Data Leakage and How to Detect Them
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5.2 Resampling: Class Weights, Undersampling, SMOTE
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5.3 Threshold Tuning and Precision-Recall Trade-offs
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5.4 Temporal Splits for Time-Dependent Data
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5.5 Lab: Fraud Detection on an Imbalanced Dataset
A model you can't explain is hard to trust and hard to ship. Use SHAP, permutation importance, and partial dependence to understand what your model learned and to communicate it to stakeholders.
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6.1 Global vs. Local Interpretability
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6.2 SHAP Values: Theory and Practice
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6.3 Permutation Importance and Partial Dependence
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6.4 Communicating Model Behavior to Stakeholders
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6.5 Lab: Explaining a Credit-Scoring Model with SHAP
Make your work reproducible and your results comparable. Track parameters, metrics, and artifacts so you always know which model came from which code, data, and configuration.
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7.1 Why Reproducibility Breaks and How to Fix It
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7.2 Tracking Experiments with MLflow
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7.3 Versioning Data, Code, and Random Seeds
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7.4 Model Registry and Run Comparison
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7.5 Lab: Tracking a Tuning Sweep End to End
Turn a trained model into a service. Package it, expose it behind a REST API, containerize it, and put the basic monitoring in place to catch drift and performance degradation in production.
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8.1 Serializing Models and Packaging Pipelines
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8.2 Serving Predictions with a FastAPI Endpoint
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8.3 Containerizing the Service with Docker
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8.4 Monitoring: Latency, Data Drift, and Model Decay
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8.5 Retraining Triggers and Basic CI/CD for ML
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8.6 Lab: Deploy and Monitor a Prediction API
Bring every skill together. Starting from a raw dataset and a business goal, you'll frame the problem, build pipelines, tune and interpret a model, and ship it as a monitored API — then present your results.
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9.1 Capstone Brief and Dataset Selection
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9.2 Problem Framing and Baseline
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9.3 Pipeline, Modeling, and Tuning
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9.4 Interpretation and Error Analysis
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9.5 Deployment and Monitoring of Your Model
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9.6 Final Write-up and Presentation
Dr. Priya Nair
Senior Machine Learning Engineer & Educator
About the Instructor
Dr. Priya Nair is a senior machine learning engineer with more than 11 years of experience building and deploying ML systems in production. She currently leads an applied ML team working on fraud detection and risk modeling, where she has shipped dozens of models that serve millions of predictions per day.
Before moving into industry, Priya earned a Ph.D. in Computer Science with a focus on applied statistical learning, and she has published peer-reviewed work on model calibration and interpretability. She is a regular contributor to open-source ML tooling and has mentored engineers transitioning from analytics and software roles into machine learning.
Her teaching style is deliberately practical: every concept is paired with code on a real dataset, and every model is taken all the way through to a deployed, monitored service — because that is where machine learning actually delivers value.
Other Courses by Dr. Priya Nair
Marcus Lee
I'd finished the fundamentals course but had no idea how to take a model past a notebook. This course filled exactly that gap. The modules on leakage and the FastAPI deployment lab were worth the price on their own — I deployed my first real prediction service the week after.
Elena Rodriguez
Genuinely practical. The hyperparameter tuning with Optuna and the SHAP interpretability module changed how I work day to day. Docking one star only because the Docker section assumes a little more setup comfort than the prerequisites suggest — budget extra time if containers are new to you.
James Okafor
The capstone is the real selling point. You start from a raw dataset and finish with a tuned, explained, deployed and monitored model — exactly what interviewers want to see. Dr. Nair's emphasis on choosing the right metric up front saved me from a mistake I'd made on every prior project.
Machine Learning Fundamentals focuses on the concepts and math behind algorithms. Applied Machine Learning is the hands-on companion: it assumes you already understand the basics and concentrates on the practical workflow of building, tuning, interpreting, and deploying real models. If you have finished the fundamentals course, this is the natural next step.
You should have completed Machine Learning Fundamentals or have equivalent understanding of core ML concepts such as train/test splits, common algorithms, and overfitting. You also need to be comfortable writing Python and using NumPy, pandas, and scikit-learn. No advanced math or deep learning background is required.
The course is structured over 10 weeks at roughly 6 hours per week, which includes about 52 hours of video plus hands-on labs and the capstone. Because you have lifetime access, you can move faster or slower; most working professionals finish comfortably within 10 to 14 weeks.
No. The course works entirely with classical, CPU-friendly models in scikit-learn and gradient boosting libraries, so any modern laptop is sufficient. The deployment module uses free, open-source tools such as FastAPI, Docker, and MLflow, all of which run locally.
Yes. You will receive a certificate of completion once you finish all the lessons, labs, and the end-to-end capstone. You can add it to your LinkedIn profile or resume to demonstrate practical, production-oriented machine learning skills to employers.
Yes. The course includes a Q&A discussion board where you can ask questions and get help from the instructor and fellow students. Dr. Nair and the teaching assistants typically respond within 24-48 hours, and there is a community Discord server for peer support and sharing capstone projects.
In the capstone you take a raw, real-world dataset all the way through the applied lifecycle:
- Frame the problem and choose an appropriate evaluation metric and baseline
- Build a reproducible data and feature engineering pipeline
- Train, tune, and compare models with proper cross-validation
- Interpret predictions with SHAP and perform error analysis
- Deploy the model as a monitored REST API and write up your results
The finished project is portfolio-ready and mirrors how machine learning is delivered on a real team.
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