The twelfth live lecture of our May 2023 Data Science Boot Camp. We provide an introduction into neural networks.

The ninth live lecture of our May 2023 Data Science Boot Camp. We cover day 2 of our classification content with a brief move into dimension reduction.

The sixth live lecture of our May 2023 Data Science Boot Camp. We started our two day time series unit.

The third live lecture of our May 2023 Data Science Boot Camp. We introduced fundamental concepts to supervised learning and began our regression unit.

We introduce the Facebook Prophet model.

We give a brief introduction into web scraping with BeautifulSoup

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

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

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

The neurons of neural networks.

We take a moment to define clustering problems.

We review how to explain all that variance.

We demonstrate some more advanced pipeline techniques in sklearn.

Sometimes you have data with different scales, here we show you how to change that.

Extra Gradient Boost!

We describe the general idea behind all boosting algorithms.

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

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

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

We introduce the notions of time series data and forecasting.

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

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

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

You gotta stratify pal.

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

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

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

Sometimes it is a good thing to be a little biased, statistically that is.

The first video in a three part series discussing various data splits that are made for predictive modeling.

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

Some tips on making good presentation plots from Matt.

The eleventh live lecture of our May 2023 Data Science Boot Camp. We cover boosting and voter models.

The eighth live lecture of our May 2023 Data Science Boot Camp. We dive into classification.

The fifth live lecture of our May 2023 Data Science Boot Camp. We wrapped up our live lecture coverage of regression.

The second live lecture of our May 2023 Data Science Boot Camp. We discussed methods of finding data for your data science project.

Using decision trees and random forests to make forecasts.

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

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

The basics of fitting a convolutional neural network in keras.

Feed forward networks in theory and in sklearn.

Our second clustering algorithm.

Another dimension reduction used primarily for data visualization.

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

When you are missing data, try imputing!

An introduction to our Cleaning section of notebooks.

A second boosting algorithm that is loosely associated with gradients.

A more general version of the random forest algorithm.

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

The second in a two part series on averaging based models.

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

Taking advantage of the gradient to minimize cost functions.

A class of support vector machine for linear problems.

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

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

All of your favorite classification algorithms back for regression purposes.

How can we interpret the results of a fitted linear regression model? Let's find out.

How can we adjust our regression set up to accommodate categorical variables. Also what the heck is an interaction?

Our first model! Come learn about simple linear regression.

In part three of this three part series we discuss the final data splitting technique, cross-validation.

We introduce a statistical framework for supervised learning problems.

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

In this video we welcome you to our data science content.

The tenth live lecture of our May 2023 Data Science Boot Camp. We wrap up classification and move over to ensemble learning. We also cover the rest of PCA that we were unable to finish in lecture 9.

The seventh live lecture of our May 2023 Data Science Boot Camp. We wrap up our time series coverage.

The fourth live lecture of our May 2023 Data Science Boot Camp. We continued to learn about linear regression.

The first live lecture of our May 2023 Data Science Boot Camp provided an introduction to the boot camp and covered participant setup.

How can we use python to collect data from APIs?

Some general tips for making a good presentation from Matt.

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

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

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

Our first clustering algorithm.

We end our three part series on PCA by demonstrating how you can interpret the results of a PCA fit.

We introduce the field of unsupervised learning.

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

All the models get together and take a vote.

Our first boosting algorithm.

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

The final time series model we cover in lecture.

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

We have to preserve the space time continuum, great Scott!

Let's branch out and learn about decision trees.

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

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

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

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

A constrained optimization approach to regression.

Regression, but this time with more than one feature. Woah.

In this video we comment on how data splits can be used to assess, but not truly "combat" overfitting.

In part two of this three part data split series we discuss validation sets.

In this video we introduce the concepts we will be covering in our supervised learning materials.

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