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Rating

4.8

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6 reviews

Description

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.

Podcast episodes

Check latest episodes from Machine Learning with Coffee podcast

20 Perceptron: Machine Learning Begins

2021/03/15

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.

19 ICA: Independent Component Analysis

2021/01/24

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.

18 PCA: Principal Component Analysis

2021/01/10

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.

17 Anomaly Detection: Clustering

2020/12/22

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 original implementation.

16 Anomaly Detection: Control Charts

2020/10/19

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. Depending on the problem at hand, control charts might be a better alternative to more sophisticated machine learning approaches for anomaly detection.

15 Adaboost: Adaptive Boosting

2020/09/28

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, which means that it learns from the misclassified instances of previous models, assigning more weights to those errors and focusing its attention on those instances in the next round.

14 XGBoost: The Winner of Many Competitions

2020/07/26

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 Jerome H. Friedman. Gradient boosting combines weak learners just as Random Forest. XGBoost is an engineering implementation which includes a clever penalization of trees and a proportional shrinking of leaf nodes.

13 Random Forest

2020/07/12

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 error are two of the nice capabilities of Random Forest which allow us to find the most important predictors and compute a good generalization error, respectively.

12 Decision Trees

2020/05/31

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 certain decision. Decision Trees naturally handle all types of variables without the need to create dummy variables, no need to scale or normalize and they are also very robust against outliers.

11 Inferential Statistics

2020/05/10

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 examples on when and why to use 1-sample t-tests and 2-sample t-tests. We also argue that the mean or average of a sample means nothing if we do not also consider the variation of the data.

Max_T_W 2020/09/02

Great intro

I’m about to start a data science immersive so I can get back I mathematics now that I’m done with the Army. This is a perfect level of detail and len...

BBB1982BBB_ 2020/07/07

Excellent podcast!

Fun to listen to, great pace, and a ton of interesting material covered.

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