The Complete Intro to Machine Learning

Rohit reviews the different variable types in Python.

Rohit goes over lists and functions in Python.

Rohit implements the Python skills that were reviewed.

Hayden explains the critical concepts behind Numpy.

Hayden implements multiple Numpy techniques.

Hayden walks through the basics of Pandas.

Hayden uses Python to show what using Pandas looks like.

Rohit explains distribution and matrix plots in Seaborn.

Rohit explains other types of plots and styling in Seaborn.

Rohit combines the Seaborn skills discussed in an example.

Arjun explains the theory behind linear regression.

Michael introduces the fundamental concepts of machine learning.

Arjun explains OLS and its role in choosing the line of best fit.

Arjun walks through an implementation of linear regression.

Arjun walks through an implementation of linear regression.

Sam reviews classification methods and explains how logistic regression works.

Sam explains metrics used to evaluate performance and walks through an implementation of logistic regression.

Joseph explains the key terms to know when learning decision trees.

Joseph describes the main splitting algorithms used in decision trees such as Gini Impurity and Information Gain.

Joseph explains how random forests, an ensemble of decision trees, can result in improved performance.

Joseph implements both a decision tree and a random forest.

Aadi explains the origins of neural networks.

Aswin describes the functions of neural networks as multilayer perceptron models.

Aswin goes over activation functions and their role in getting the output of a node.

Aadi explains gradient descent and how it is used as an optimization algorithm for training.

Aadi explains how the weights of neural networks are fine-tuned with backpropagation.

Aadi walks through an implementation of neural networks using the skills and concepts explained by Aswin.

Aadi gives a brief introduction to the content of this section.