Title: Introduction to Machine Learning with SciKit Learn

Location: US

Company: CodeSignal

About this course:
This intermediate path introduces the core workflow for building machine learning models with scikit-learn. You will learn how to prepare data, train baseline models, make predictions, and evaluate results using appropriate metrics. The path covers both regression and classification, including linear and regularized regression, logistic regression, decision trees, and k-nearest neighbors. You will also explore ensemble methods such as bagging, boosting, and stacking to combine models for stronger performance. As you progress, you will practice model selection techniques including cross-validation, grid search, and random search. The path is designed for learners who have some programming experience and want a structured introduction to applied machine learning in Python.

Preprocess datasets by handling missing values, encoding categories, scaling features, and creating train-test splits

Train regression and classification models using scikit-learn

Evaluate model performance with metrics for regression and classification tasks

Apply regularization techniques such as ridge, lasso, and elastic net regression

Compare classifiers including logistic regression, decision trees, and k-nearest neighbors

Optimize models with cross-validation, grid search, and random search

Build ensemble models using bagging, boosting, and stacking techniques

Build a practical foundation in machine learning with scikit-learn, from data preprocessing to model training and evaluation. Explore regression, classification, ensembles, cross-validation, and hyperparameter tuning.

Duration: 6 weeks

Skills:
- Applications Of Artificial Intelligence
- Applied Machine Learning
- Boosting
- Data Preprocessing
- Decision Tree Learning
- Ensemble Methods
- Forecasting
- Logistic Regression
- Machine Learning
- Scikit-Learn (Python Package)
- Workflow Management

Curriculum:
- Preprocess datasets by handling missing values, encoding categories, scaling features, and creating train-test splits
- Train regression and classification models using scikit-learn
- Evaluate model performance with metrics for regression and classification tasks
- Apply regularization techniques such as ridge, lasso, and elastic net regression
- Compare classifiers including logistic regression, decision trees, and k-nearest neighbors
- Optimize models with cross-validation, grid search, and random search

- Build ensemble models using bagging, boosting, and stacking techniques