Applied Machine Learning

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

Curriculum

Applied Machine Learning

Requirements

Basic knowledge of Python programming
Familiarity with fundamental statistics and probability
Access to a computer with internet for programming and using libraries like Scikit-learn