Referencia
Conceptos básicos
MFML 000 — Welcome1 
Las reglas del juego
MFML 001 — What is machine learning? Etiquetador de cosas Un cambio de paradigma
MFML 002 — Why use machine learning? Generalizar
MFML 003 — How does machine learning work? Modelo de clasificación
MFML 004 — How to test ML Validación del modelo
MFML 005 — What’s inside the black box? Algoritmos
MFML 006 — Simple linear regression Regresión lineal
MFML 007 — Multiple linear regression Regresión lineal múltiple
MFML 008 — Feature engineering
MFML 009 — What is AI? ¿Qué es entonces la IA?
MFML 010 — Why did we wait so long for AI? ¿Y por qué ahora?
MFML 011 —Algorithms, data, and compute
In Practice
MFML 012 — Real applications
MFML 013 — How to find good AI use cases
MFML 014 — Human creativity in AI
MFML 015 — How Do GANs work?
MFML 016 — Why trust AI?
MFML 017 — Explainability and AI
MFML 018 — Intro to training, validation, and testing
MFML 019 — How to avoid machine learning pitfalls
MFML 020 — Decision Intelligence
MFML 021 — Why do businesses fail at machine learning?
MFML 022 — Skilled decision-makers
MFML 023 — Reliable or unreliable?
MFML 024 — Preventable disasters
MFML 025 — Wish responsibly
MFML 026 — AI is a team sport!
MFML 027 — Our AI future
The 12 Steps of AI
Step 0: Find an application where ML is useful
MFML 028 — The 12 steps of AI
MFML 029 — Where to start with applied AI?
MFML 030 — Classification vs regression
MFML 031 — Instances, features, and targets
MFML 032 — Supervised learning
MFML 033 — Unsupervised learning
MFML 034 — Semi-supervised learning
MFML 035 — Reinforcement learning
MFML 036 — What on earth is data science?
MFML 037 — Data science flowchart
MFML 038 — Don’t forget data!
Step 1: Define your objective
MFML 039 — What is “good behavior” for AI?
MFML 040 — False positives and true negatives
MFML 041 — Confusion matrix
MFML 042 — Performance metrics
MFML 043 — Ground truth
MFML 044 — Precision vs recall
MFML 045 — What is optimization?
MFML 046 — Loss functions
MFML 047 — Setting launch criteria
Step 2: Get your data
MFML 048 — Data engineering
Step 3: Split your data
MFML 049 — The danger of overfitting
MFML 050 — Should you care about underfitting?
MFML 051 — The importance of data splitting
Step 4: Explore some data
MFML 052 — Exploratory data analysis (EDA)
Step 5: Prepare your tools
MFML 053 — How to select an AI algorithm
MFML Part 4 — Guide to AI algorithms
Step 6: Train your models
MFML 054 — Is training an AI system easy?
MFML 055 — A dataset’s idea shape
MFML 056 — How to speed up your ML/AI training phase
MFML 057 — Statistics versus “statistics”
MFML 058 — When your machine learning project takes forever
MFML 059 — Regularization
MFML 060 — Features you should never use in AI
MFML 061 — Can you skip the training phase in AI?
Step 7: Tune and debug your models
MFML 062 — Debugging your machine learning model
MFML 063 — Hyperparameter tuning
MFML 064 — What is a holdout set and how do you use it?
MFML 065 — Understanding k-fold cross-validation
MFML 066 — Advanced AI debuggin
MFML 067 — What if you skip debugging?
Step 8: Validate your model
MFML 068 — What to do when model validation fails
MFML 069 — Model validation done right
MFML 070 — Validation roulette
Step 9: Test your model
MFML 071 — What’s the difference between testing and validation
MFML 072 — The 12 steps of statistics
MFML 073 — Interpreting AI test output
MFML 074 — Understanding p-values
MFML 075 — Statistical significance
MFML 076 — What should you do if testing fails
MFML 077 — The importance of testing
Step 10: Build your ML system
MFML 078 — Productionization
MFML 079 — Repurposing data safely
MFML 080 — Solving AI latency problems
MFML 081 — How often should you retrain your AI system?
MFML 082 — The training-serving skew
MFML 083 — Be careful with chained models
MFML 084 — Making tiny changes to AI code
MFML 085 — When your AI model fails retesting
MFML 086 — The danger of the long tail in AI
MFML 087 — How to catch outliers and AI failures
MFML 088 — AI safety and policy layers
Step 11: Make launch decision
MFML 089 — Live traffic experiments
Step 12: Monitor and maintain
MFML 090 — Monitoring your AI system
MFML 091 — AI system maintenance
Introduction to Algorithms
Menu
MFML 092 — Opening the black box
Unsupervised Learning
MFML 093 — Clustering and k-means
Lazy Learning
MFML 094 — Lazy learning and k-NN
MFML 095 — The curse of dimensionality
Linear Classifiers
MFML 096 — What are perceptrons?
MFML 097 — Maximal margin classifiers
MFML 098 — What are support vectors?
MFML 099 — SVMs and the kernel trick
Decision Trees and Random Forests
MFML 100 — What is a decision tree?
MFML 101 — XAI and the interpretability debate
MFML 102 — Decision trees and SVMs compared
MFML 103 — Boosted Aggregation a.k.a. Bagging
MFML 104 — What is a random forest?
Ensembles
MFML 105 — Ensemble models
Naive Bayes
MFML 106 — Introduction to Bayes’ Rule
MFML 107 — What’s so naive about Naive Bayes?
Regression Models
MFML 006 — Simple linear regression 
MFML 007 — Multiple linear regression
MFML 008 — Feature engineering
MFML 108 — What do all regression models have in common?
MFML 109 — When should you use linear regression?
MFML 110 — Logistic regression
MFML 111 — What are all those sigmoid functions for?
MFML 112 — How to do ranking at scale
Deep Learning
MFML 113 — Introduction to deep learning
MFML 114 — What’s inside a neural network?
MFML 115 — Using AI for automatic feature extraction
MFML 116 — Understanding the components of your neural network
MFML 117 — Backpropagation
MFML 118 — Gotchas, pros, and cons of deep learning
MFML 119 — Neural network architecture
MFML 120 — When to use neural networks
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