Machine Learning with Python Training in Copenhagen, Denmark

This course provides a comprehensive introduction to Machine Learning using Python, focusing on both theory and practical implementation. Participants will learn how to process data, build predictive models, evaluate their performance, and deploy machine learning applications using widely adopted Python libraries such as Scikit-learn, Pandas, NumPy, and Matplotlib.

Course Agenda

Introduction to Machine Learning

• What is Machine Learning?

• Categories of ML: Supervised, Unsupervised, Reinforcement

• ML workflow: Data to Deployment

• Overview of ML libraries in Python

Python for Machine Learning

• Essential Python syntax

• Data structures, functions, and control flow

• Introduction to NumPy and Pandas

Data Preprocessing and Exploration

• Handling missing values and outliers

• Data normalization and encoding

• Exploratory Data Analysis (EDA)

• Visualization with Matplotlib and Seaborn

Supervised Learning Techniques

• Linear and Logistic Regression

• Decision Trees and Random Forests

• Support Vector Machines (SVM)

• K-Nearest Neighbors (KNN)

Unsupervised Learning

• K-means Clustering

• Hierarchical Clustering

• Dimensionality Reduction (PCA)

Model Evaluation and Tuning

• Confusion Matrix, Precision, Recall, F1 Score

• ROC-AUC and Cross-Validation

• GridSearchCV and Hyperparameter tuning

• Avoiding overfitting/underfitting

Working with Real-World Datasets

• Feature engineering

• Building end-to-end workflows

• Case studies (e.g., house price prediction, customer segmentation)

Introduction to Model Deployment

• Saving models with Pickle and Joblib

• Using Flask for simple API-based deployment

• Overview of tools like Streamlit and FastAPI

Hands-on exercises