Trees, SVM and Unsupervised Learning (Coursera)

Additionally, you will gain valuable experience in generating data representations through PCA and clustering. With a focus on practical, real-world applications, this course is a valuable asset for anyone looking to upskill or move into the field of data science.
Course 3 of 3 in the Statistical Learning for Data Science Specialization.

What You Will Learn

• Describe the advantages and disadvantages of trees, and how and when to use them.
• Apply SVMs for binary classification or K > 2 classes.
• Analyze the strengths and weaknesses of neural networks compared to other machine learning algorithms, such as SVMs.

Syllabus

WEEK 1
Welcome!
The module provides an introductory overview of the course and introduces the course instructor.

WEEK 2
Support Vector Machines (SVMs)
To begin the course, we will learn about support vector machines (SVMs). SVMs have become a popular method in the field of statistical learning due to their ability to handle non-linear and high-dimensional data. SVMs seek to maximize the margin, or distance between the decision boundary and the closest data points, to improve generalization performance. Throughout the week, you will learn how to apply SVMs to classify or predict outcomes in a given dataset, select appropriate kernel functions and parameters, and evaluate model performance

WEEK 3
Introduction to Neural Networks
Neural Networks have become increasingly popular in the field of statistical learning due to their ability to model complex relationships in data. In this module, we will cover introductory concepts of neural networks, such as activation functions and backpropagation. You will have the opportunity to apply Neural Networks to classify or predict outcomes in a given dataset and evaluate model performance in the labs for this module.

WEEK 4
Decision Trees-Bagging-Random Forests
Welcome to the final module for the course. This module will focus on the ensemble methods decision trees, bagging, and random forests, which combine multiple models to improve prediction accuracy and reduce overfitting. Decision Trees are a popular machine learning method that partitions the feature space into smaller regions and models the response variable in each region using simple rules. However, Decision Trees can suffer from high variance and instability, which can be addressed by Bagging and Random Forests. Bagging involves generating multiple trees on bootstrapped samples of the data and averaging their predictions, while Random Forests further decorrelate the trees by randomly selecting subsets of features for each tree.