Due to a technical glitch, the audio for the video Alec made to introduce himself and relevant information about projects didn't work in the orientation lecture. You can watch it now!

A second boosting algorithm that is loosely associated with gradients.

Feed forward networks in theory and in sklearn.

We introduce the keras package for neural network construction in python.

A brief review of the theory of basic recurrent neural networks.

Another dimension reduction used primarily for data visualization.

Our second clustering algorithm.

All of your favorite classification algorithms back for regression purposes.

This video should show you how to navigate the team formation process on the Erdos website.

This video will walk you through cloning the GitHub repo. It also addresses how to troubleshoot some common pitfalls.

We cover a plethora of data source websites you can use.

Your data is stuck in a database, can you get it out? Learn how in this video.

We introduce data splits including:
1. Train/Test splits
2. Validation sets
3. k-fold cross validation.

We introduce multiple linear regression.

Having features with very different scales can impact the performance of some models. In this video we show how to use sklearn's StandardScaler object to standardize our features.

The expected generalization error of a learning algorithm can be decomposed into two terms: the bias and the variance.

A cool PCA example where the components have an interesting interpretation.

Since time series are ordered, we need a slight adjustment to our cross-validation strategy.

Our first time series forecast works by taking local averages of previous observations.

An autoregressive model expresses the value of a time series as a linear combination of lags plus an error term.

SARIMA stands for Seasonal Autoregressive Integrated Moving Average. We will learn how these models are defined and see an example of how to use such a model in practice.

And you thought things couldn't get any more confusing.

The first in a two part series about Bayes' rule based classification algorithms.

A class of support vector machine for linear problems.

Putting lots of models together to make a better one (hopefully).

We describe the general idea behind all boosting algorithms.

We discuss Maximum Likelihood Estimation (MLE) and Maximum A Posteriori (MAP) Estimation of model parameters. We address estimating the parameters of binomial and normal distributions from a sample.

Extra Gradient Boost!

The neurons of neural networks.

We introduce the basic theory behind convolutional neural networks, NNs designed for grid-based data.

How you can load a model you have saved after training it for a long time.

We take a moment to define clustering problems.

When you are missing data, try imputing!

Taking advantage of the gradient to minimize cost functions.

Some general tips for making a good presentation from Matt.

We present two options for organizing the work you do in this course:

1. Copying the notebooks are working on to a new folder.
2. Creating a local branch of the repo where you do your work.

We give a brief introduction into web scraping with BeautifulSoup

We sure have learned a lot of ways to collect data with python. Let's summarize and make some final conclusions on this topic.

We introduce the simple linear regression model.

Adjusting our regression set up to accommodate categorical variables.

Pipelines are a nice way to put all modeling steps into one neat package. Here we introduce the most basic pipeline creation method.

Regularization adds a term to our loss function which penalizes large parameters. This can help prevent overfitting.

We review a few different approaches to choosing what features to include in a linear regression model.

A brief aside on how to handle time and dates in python.

Exponential Smoothing forecast methods are rolling averages with weights which decreases exponentially backwards through time. We introduce four such methods.

A Moving Average model expresses the value of a time series as a linear combination of lagged independent normally distributed errors.

You gotta stratify pal.

Just when we thought we were done with regression, it pulls us back in.

The second of a two part series on Bayes' rule based classification.

A type of support vector machine that can deal with nonlinear problems.

What do you get when you put a whole bunch of random decision trees together?

Our first boosting algorithm.

An orientation to the Fall 2024 Data Science Bootcamp.

All the models get together and take a vote.

We do a more formal introduction of the MNIST data set.

The basics of fitting a convolutional neural network in keras.

Directions you may pursue if you want to learn more about neural networks, theory and implementation.

Our first clustering algorithm.

We demonstrate some more advanced pipeline techniques in sklearn.

We introduce a sklearn object that makes hyper parameter tuning a tad easier.

Some tips on making good presentation plots from Matt.

In this video we give an eagle's eye view of what we will cover in our data science content.

How can we use python to collect data from APIs?

We introduce a statistical framework for supervised learning problems.

We review a typical workflow for a predictive modeling project with some baseball data.

We can make polynomial and nonlinear transformations of our features to make additional regression model types.

Residual plots can help us better understand our linear regression model.

We introduce the theory behind principal components analysis and demonstrate how it is implemented in python.

We introduce the notions of time series data and forecasting.

Some good baseline models for various flavors of time series data.

We learn what a stationary time series is and see how we can assess whether a time series is clearly non-stationary.

Where to go in order to keep learning about time series and forecasting.

It's a beautiful day in the neighborhood, a beautiful day for k neighbors.

Here we examine a few different curves with which we can examine classifier performance.

What are some ways we can evaluate classification models for problems with more than two possible classes.

Let's branch out and learn about decision trees.

A more general version of the random forest algorithm.