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Lessons
Introduction
- 1 - Introduction FREE
- 2 - Prerequisites FREE
Supervised Learning
- 3 - Naive Bayes
- 4 - Performance
- 5 - Naive Bayes Optimizations
- 6 - K Nearest Neighbors FREE
- 7 - Decision Trees
- 8 - Linear Regression
- 9 - Logistic Regression
- 10 - Support Vector Machine
Unsupervised Learning
Deep Learning
- 13 - Neural Networks
- 14 - Convolutional Neural Networks FREE
- 15 - Recurrent Neural Networks
- 16 - Generative Adversarial Neural Networks
Recommender Systems
Ranking
1 - Introduction
6 min
Welcome to our Machine Learning Crash Course!
The purpose of this course is simple and singular in nature: to equip you with the foundational knowledge you need to ace any machine-learning interview and land a job in the industry. Let's get to work!
Key Terms
Ranking
Optimizing machine learning models to rank candidates, such as music, articles, or products. Typically, the goal is to order the candidates such that the candidates which are most likely to be interacted with (purchased, viewed, liked, etc.) are above other candidates that aren't as likely to be interacted with.
Supervised Learning
Optimizing machine learning models based on previously observed features and labels. Typically, the goal is to attach the most likely label to some provided features.
Unsupervised Learning
An approach within machine learning that takes in unlabeled examples and produces patterns from the provided data. Typically, the goal is to discover something previously unknown about the unlabeled examples.
Deep Learning
Optimizing neural networks, often with many hidden layers, to perform unsupervised or supervised learning.
Recommendation Systems
Systems with the goal of presenting an item to a user such that the user will most likely purchase, view, or like the recommended item. Items can take many forms, such as music, movies, or products. Also called recommender systems.