10 days can provide enough time to learn the basics of Machine Learning, and even allow a new practitioner to apply some of these skills to their own projects.
Are you new to Machine Learning? You're not alone. In this page you will find a set of useful articles, videos and blog posts from independent experts around the world that will gently introduce you to the basic concepts and techniques of Machine Learning.
Learn what you need to know to get started with Machine Learning in a practical, hands on manner without bogging you down with complex math or theory.
What is Machine Learning? See how it works with this animated data visualization.
The world’s easiest introduction to Machine Learning.
Developing successful Machine Learning applications requires some "black art" that is hard to find in textbooks. This article summarizes 12 key ML lessons.
There are only five questions Machine Learning can answer: Is this A or B? Is this weird? How much/how many? How is it organized? What should I do next?
A compilation of useful resources to learn about machine learning and deep learning (ML/DL)from scratch.
Short and to-the-point tutorials that cover each single, specific machine learning concept
Links to give you a glimpse of how to solve classification and regression problems starting with labeled data.
This post explains the Bias-Variance Dilemma that finds the balance between overfitting and underfitting.
This post gives insight and concrete advice on how to tackle imbalanced data.
The authors evaluate 179 classifiers for different problems to select the top performing algorithms.
Most supervised learning methods have a risk of overfitting, i.e., tailoring the model to fit the training data at the expense of generalization.
Overview of how decision tree models learn the patterns to predict categorical values (classification) and continuous numeric values (regression).
Short video that explains in a visual way how Bagging works for ensembles.
This paper reviews ensemble methods and explains why ensembles can often perform better than any single classifier.
Short video that explains in a visual way how Boosting works for ensembles.
Why the Random Decision Forest is usually the most effective algorithm to solve most cases?
Series of 6 videos introducing Logistic Regression: from the basics (what it is, when to use it, why we need it), the probabilities, the odds, the odds ratio and the logit formula.
Blog post that explores the differences between Decision Trees and Logistic Regression.
Blog post explaining how to interpret the Confidence and Expected Error in decision tree predictions.
If it’s easy, it’s probably wrong. An introduction of classification models evaluation.
An introduction of ROC graphs, commonly used for comparing classifiers and visualizing their performance.
Short video to introduce K-Fold Cross-Validation for models.
Teach yourself how you can discover the hidden patterns in your data without the need for labeled data.
Visual explanation of how the k-means cluster algorithm works.
Blog post to learn how the G-means cluster algorithm finds the optimal different groups in a dataset.
Video from BigML VP Data Science explaining how the Isolation Forests algorithm can effectively detect anomalies.
Paper about the state-of-the-art algorithm to detect anomalies: Isolation Forests.
Blog post explaining a use case to find Associations using movies metadata.
Video lecture to learn the basic concepts of Topic Modelling in general and Latent Dirichlet Allocation in particular.
Article explaining the basics of Association Discovery applied to market basket analysis.