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ABOUT THIS PODCAST 🔗
Machine Learning with Coffee is a podcast where we are going to be sharing ideas about Machine Learning and related areas such as: artificial intelligence, business intelligence, business analytics, data mining and Big data. The objective is to promote a healthy discussion on the current state of this fascinating world of Machine Learning. We will be sharing our experience, sharing tricks, talking about latest developments and interviewing experts, all these on a very laid back, friendly manner. So, what are you waiting for? Grab a coffee and join us.
Update frequency:
monthly
Average audio length:
21 minutes
English
United States
20 episodes
since Jan. 26, 2020
episodic
LATEST EPISODE 🔗
We introduce the concept of a perceptron as the basic component of a neural network. We talk about how important is to understand the concept of backpropagation applied to a single neuron.
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PREVIOUS EPISODES 🔗
We discuss Independent Component Analysis as one of the most popular and robust techniques to decompose mixed signals. ICA has important applications in audio processing, video, EEG and in many datasets, which present very high multicollinearity.
We discuss Principal Component Analysis as one of the most popular techniques to reduce the dimensionality of a dataset. PCA helps us be more efficient in terms of the number of variables we feed to our machine learning models.
We present 3 clustering algorithms which will help us detect anomalies: DBSCAN, Gaussian Mixture Models and K-means. These 3 algorithms are very popular and basic but have passed the test of time. All these algorithms have many variations which try to overcome some of the disadvantages of the origi…
Anomaly detection is not something recent, techniques have been around for decades. Control charts are graphs with solid mathematical and statistical foundations which monitor how a process changes over time. They implement control limits which automatically flag anomalies in a process in real-time…
Adaboost is one of the classic machine learning algorithms. Just like Random Forest and XGBoost, Adaboost belongs to the ensemble models, in other words, it aggregates the results of simpler classifiers to make robust predictions. The main different of Adaboost is that it is an adaptive algorithm, …
XGBoost is an open-source software library which has won several Machine Learning competitions in Kaggle. It is based on the principles of gradient boosting, which is based on the ideas of the Leo Breiman, the creator of Random Forest. The theory behind gradient boosting was later formalized by Jer…
Random Forest is one of the best out-of-the-shelf algorithms. In this episode we try to understand the intuition behind the Random Forest and how it tries to leverage the capabilities of Decision Trees by aggregating them using a very smart trick called “bagging”. Variable Importance and out-of-bag…
We talk about Decision Trees as one of the most basic statistical learning algorithms out there that all Data Scientist should know. Decision Trees are one of a few machine learning models which are easy to interpret which makes them a favorite when it is desired to understand the logic behind a ce…
We talk about the importance of inferential statistics in Data Science. Inferential statistics are a set of techniques used to make generalizations about a population from a sample. One of the tools used in inferential statistics is hypothesis testing. In this episode we provide a couple of example…
Disclaimer: The podcast and artwork embedded on this page are from Gustavo Lujan, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
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‐ Albert "Llamacorn" Einstein
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