Course Description

The Applied Machine Learning course offers a comprehensive guide to building machine learning models and deploying them in real-world scenarios. Covering key topics like regression, classification, decision trees, and neural networks, the course focuses on practical applications with step-by-step guidance.

Throughout the course, you will explore:

• Supervised learning techniques, including linear regression, logistic regression, and support vector machines
• Advanced algorithms like Random Forests and Gradient Boosted Decision Trees
• Concepts like cross-validation, model selection, and overfitting/underfitting
• Metrics for evaluating model performance such as confusion matrices, ROC curves, and precision-recall
• Techniques for improving model generalization, including regularization and ridge regression

By the end of the course, you will have a solid understanding of machine learning fundamentals and will be able to implement, fine-tune, and evaluate models effectively using Python.

What will i learn?

• Understand and implement key machine learning algorithms like decision trees, logistic regression, and neural networks
• Perform model evaluation using various metrics and cross-validation techniques
• Optimize models through regularization techniques (Ridge, Lasso) and hyperparameter tuning
• Build and deploy machine learning models for regression and classification tasks
• Identify and address common issues such as overfitting, data leakage, and underfitting

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