When starting out with Data Science, there is so much to learn it can become quite overwhelming. This guide will help aspiring data scientists and machine learning engineers gain better knowledge and experience. I will list different types of machine learning algorithms, which can be used with both Python and R. Linear Regression is the simplest Machine learning algorithm that branches off from Supervised Learning. It is primarily used to solve regression problems and make predictions on continuous dependent variables with the knowledge from independent variables. The goal of Linear Regression is to find the line of best fit, which can help predict the output for continuous dependent variables.

Hierarchical clustering means creating a tree of clusters by iteratively grouping or separating data points. There are two types of hierarchical clustering named Agglomerative clustering and Divisive clustering. Agglomerative clustering is the bottom-up approach. It merges the two points that are the most similar until all points have been merged into a single cluster. Divisive clustering is the top-down approach.

Each step, the Decision Tree algorithm attempts to find a method to build the tree such that the entropy is minimized. Think of entropy more formally as the amount of'disorder' or'confusion' a certain divider (the conditions) has, and its opposite as'information gain' -- how much a divider adds information and insight to the model. Feature splits that have the highest information gain (as well as a lowest entropy) are placed at the top. Note that condition 1 has clean separation, and therefore low entropy and high information gain. The same cannot be said for condition 3, which is why it is placed near the bottom of the Decision Tree.

In this course, you'll learn about Machine Learning and where it fits within the wider Artificial Intelligence (AI) field.