In this comprehensive class, you’ll learn to apply Scikit-learn to solve real-world machine learning problems. Scikit-learn is one of the most widely used libraries for building machine learning models in Python, and this class will take you through each step of the process—from data preparation to model deployment.

We will cover essential topics such as:

• Data Preprocessing: Learn to handle missing data, normalize, standardize, and encode categorical variables to make your data ready for modeling.

• Supervised Learning Algorithms: Understand key models like linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), and k-nearest neighbors (KNN). Learn how to choose the best model for your problem and tune its parameters.

• Unsupervised Learning Algorithms: Dive into clustering techniques like k-means and hierarchical clustering, and explore dimensionality reduction methods like PCA.

• Model Evaluation and Selection: Learn how to evaluate your models using metrics like accuracy, precision, recall, F1-score, and cross-validation to ensure they generalize well to unseen data.

• Hyperparameter Tuning: Learn how to optimize your models using techniques like grid search and randomized search to achieve the best performance.

By the end of this course, you'll be proficient in building, evaluating, and fine-tuning machine learning models using Scikit-learn, ready to tackle real-world challenges with confidence.