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IntermediateMachine Learning8 weeks
Certificate IncludedCertificate EligibleUcanly certificate

Machine Learning: Intermediate

Go from coder to machine learning engineer: advanced feature engineering, ensemble models, deep learning, and Generative AI for ML work.

Duration
8 weeks
Level
Intermediate
Content
47 lessons
8 modules · 15 h 8 min
Certificate
Included
On completion
Access
Fee by email
Or take the free aptitude test

What you'll learn

Frame business problems as ML problems and build reproducible pipelines with proper experiment tracking
Apply advanced feature engineering, encoding, and time based validation to avoid data leakage
Master ensemble models including XGBoost, LightGBM, and CatBoost, tuned systematically with Optuna
Build and train neural networks from scratch in PyTorch, understanding backpropagation deeply
Apply transfer learning and Hugging Face transformers to computer vision and NLP problems
Choose the right evaluation metric, run robust validation, and explain models with SHAP
Build embeddings, vector search, and RAG systems, and fine tune models with LoRA
Serve models as production APIs with FastAPI and Docker
Ship an end to end machine learning product with an engineered pipeline, a tuned model, a live API, and a Generative AI layer

Who this course is for

  • Beginner level graduates ready to move into professional ML engineering
  • Self taught practitioners who know basic ML and want structured, production level skills
  • Junior ML engineers and data scientists preparing for mid level interviews
  • Anyone who wants hands on experience building deep learning and Generative AI powered ML systems

Skills you'll gain

Machine Learning EngineerFeature EngineeringXGBoostLightGBMCatBoostEnsemble LearningHyperparameter TuningOptunaDeep LearningPyTorchCNNTransfer LearningNLPTransformersHugging FaceSHAPModel InterpretabilityMLflowFastAPIRAGFine TuningApplied Machine Learning

This course includes

Live mentor led Ucanly sessions plus self paced recorded content
Production style projects in every module
Ucanly portfolio review, mock ML technical interviews, and a personalized roadmap to Advanced
Lifetime access to course materials and future updates
Certificate of Completion from Ucanly

Course content

8 modules · 47 lessons · 15 h 8 min

Machine learning engineering in the real world, advanced feature engineering, advanced algorithms and ensemble mastery, deep learning foundations with PyTorch, applied deep learning in vision and language, evaluation and trustworthy ML, Generative AI for ML engineers, and a capstone end to end ML product.

Requirements

  • Completion of the Beginner level, or working knowledge of Python, Pandas, Scikit learn, and basic supervised learning
  • A computer capable of running Python and common machine learning libraries
  • Comfort training and evaluating a simple machine learning model

About this course

Level up from writing ML code to engineering real machine learning products. This intermediate program from Ucanly takes practitioners with working knowledge of Python, Pandas, Scikit learn, and basic supervised learning into professional ML engineering: reproducible pipelines, advanced feature engineering, and ensemble mastery with XGBoost, LightGBM, and CatBoost tuned with Optuna. You will build deep learning foundations in PyTorch and apply them to computer vision and NLP with transfer learning and Hugging Face transformers. On evaluation, you will master SHAP based interpretability, calibration, and responsible ML. On the Generative AI side, you will build embeddings, RAG systems, and fine tune models with LoRA to accelerate your ML work. You will graduate having shipped an end to end machine learning product with an engineered pipeline, a tuned and explained model, a live API, and a Generative AI powered layer.

Earn a Ucanly certificate

Complete this course to earn a Ucanly certificate with its own page and QR code. Save it as a PDF, share its link, or add it to the certifications on your LinkedIn profile.

Certificate IncludedCertificate EligibleUcanly certificate

Frequently asked questions

It is recommended, but working knowledge of Python, Pandas, Scikit learn, and basic supervised learning is sufficient to start.

Yes, you will build neural networks from scratch in PyTorch and apply transfer learning and Hugging Face transformers to vision and language problems.

You will build embeddings and vector search, RAG systems over your own data, and fine tune models with LoRA to accelerate ML work.

An end to end machine learning product with an engineered pipeline, a tuned and explained model, a live API, and a Generative AI powered layer, mentored by Ucanly.