Machine Learning Engineer with Python

Welcome to LSET’s Machine Learning Engineer with Python Course, designed to help you build real-world ML solutions using powerful Python tools. From data preparation and model building to deployment and MLOps basics, this hands-on course equips software developers and data analysts with in-demand machine learning skills. Whether you’re starting out or upskilling, get ready to transform data into intelligent applications.

Machine Learning Engineering with Python offers a hands-on, project-driven path to mastering ML techniques, data manipulation, and model deployment. Tailored for software developers and data analysts, this course combines theoretical foundations with practical experience, empowering you to build, evaluate, and serve production-ready machine learning models.

Topics Covered in This Course

Supervised Learning
Learn algorithms like linear regression, logistic regression, decision trees, and random forests. Understand how to train, validate, and tune predictive models for classification and regression tasks.

Unsupervised Learning
Explore clustering (K-means, hierarchical clustering) and dimensionality reduction (PCA, t-SNE) techniques to discover hidden patterns in data without labels.

Scikit-learn
Gain hands-on experience with Python’s premier ML library, implement pipelines, cross-validation, hyperparameter tuning, and feature engineering using scikit-learn’s rich API.

pandas
Master data manipulation and analysis: loading datasets, cleaning missing values, merging DataFrames, and preparing data for machine learning workflows.

NumPy
Understand numerical computing with NumPy arrays. vectorised operations, broadcasting rules, and efficient matrix computations for high-performance ML tasks.

Model Evaluation
Learn metrics such as accuracy, precision, recall, F1-score, ROC-AUC, and mean squared error. Implement k-fold cross-validation and learning curves to ensure robust model performance.

Deployment Basics
Explore how to serialize models with joblib or pickle, containerise with Docker, and expose ML models as RESTful APIs using Flask or FastAPI.

Introduction to MLOps
Get an overview of version control, continuous integration/continuous deployment (CI/CD) pipelines, monitoring, and model retraining, bridging the gap between development and production.

Capstone Project: Deploy an ML Model as a REST API
Apply end-to-end skills by building a complete pipeline: data preprocessing, model training, evaluation, and deployment of a machine learning model as a scalable REST API.