Hands-on ML start - You will build your first machine learning models with scikit-learn and TensorFlow, so you can move from theory to working code faster and apply ML in real development tasks.
Models for real data - You will practice classification and regression on realistic business cases, making it easier to design models that solve specific product, customer, or operational problems.
Ready-to-use setup - You will configure a full ML workspace in Jupyter and learn a practical workflow with NumPy, Pandas, and Scikit-learn to explore data and validate ideas more efficiently.
Broader algorithm toolkit - You will work with decision trees, random forests, and neural networks, helping you choose the right approach for your data, project scale, and expected prediction quality.
Stronger data preparation - You will master cleaning, scaling, normalization, and handling missing or outlier values, so you can build more reliable pipelines and reduce model issues caused by poor data.
Better model tuning - You will use cross-validation, hyperparameter tuning, and dimensionality reduction to improve model quality in a structured way instead of relying on guesswork and random trials.
Production deployment skills - You will learn how to expose models through APIs and microservices, monitor their behavior, and manage model and data versions in a production-ready ML environment.
Testing and maintenance - You will learn practical ways to test, validate, and debug ML solutions, so you can catch issues earlier, reduce technical debt, and keep your project easier to evolve.
Machine learning starts here from a developer’s perspective, with scikit-learn and TensorFlow used on realistic business cases rather than abstract theory. Different learning approaches are implemented in practice, culminating in a first customer classification model built hands-on.
NumPy, Pandas, Scikit-learn, and Jupyter form the working base for the exercises and implementation tasks. The section focuses on configuring a usable ML environment and applying these tools in a way that supports analysis, experimentation, and repeatable project work.
Building core machine learning methods in code helps clarify how training logic and model behavior actually work. Participants implement regression, classification, decision trees, random forests, and basic neural networks, while also addressing optimization choices during development.
Data quality issues often decide whether a model is usable at all. This part works through cleaning and transformation techniques, pipeline design, scaling and normalization, and practical handling of missing values and outliers before the modeling stage begins.
Cross-validation, dimensionality reduction, and performance tuning are treated as coding techniques, not only modeling concepts. The section shows how to implement these methods in practice so models can be compared consistently, optimized, and adapted to more demanding datasets.
Production ML relies on an operating model that links development, release, and maintenance. This section explains system architecture, CI/CD practices adapted to machine learning, model and data versioning, plus monitoring and update workflows after deployment.
Reliable ML behavior depends on checking both code and model assumptions at the right stages. Here the focus is on testing strategies for machine learning solutions, along with validation and verification methods used to detect errors, instability, and incorrect outputs.
When models must improve without exhausting infrastructure, tuning and resource control need to work together. Participants examine hyperparameter optimization techniques and ways to allocate computational capacity so experiments, training, and scaling remain efficient.
Connecting a model to live business processes requires more than exposing predictions. The section covers APIs and microservices, integration with databases and enterprise systems, real-time request handling, and the operational safeguards needed for security and production monitoring.
Long-term maintainability becomes a design concern when ML code evolves into shared production assets. This part addresses technical debt, decisions that support future changes, and patterns for building services and components that remain scalable as data volume and business usage grow.
Basic Python skills - You should be comfortable writing simple Python scripts, using functions, loops, and conditionals, and reading existing code so you can focus on ML rather than language basics.
Working with data and code - You should understand how to load data, run code, and use development tools so you can complete hands-on exercises efficiently in Jupyter and ML libraries.
Math fundamentals - You should know the basics of linear algebra, statistics, and chart interpretation, because this will help you understand models, metrics, and data transformations.
Developer workflow experience - You should have experience building and running applications or scripts, so you can more easily follow model deployment, testing, and integration with other systems.
