### Course Progress

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### Lessons

#### Introduction

- [1 - Introduction](/content/machine-learning/crash-course/introduction/index.html) FREE
- [2 - Prerequisites](/content/machine-learning/crash-course/prerequisites/index.html) FREE

#### Supervised Learning

- [3 - Naive Bayes](/content/machine-learning/crash-course/naive-bayes/index.html)
- [4 - Performance](/content/machine-learning/crash-course/performance/index.html)
- [5 - Naive Bayes Optimizations](/content/machine-learning/crash-course/naive-bayes-optimizations/index.html)
- [6 - K Nearest Neighbors](/content/machine-learning/crash-course/k-nearest-neighbors/index.html) FREE
- [7 - Decision Trees](/content/machine-learning/crash-course/decision-trees/index.html)
- [8 - Linear Regression](/content/machine-learning/crash-course/linear-regression/index.html)
- [9 - Logistic Regression](/content/machine-learning/crash-course/logistic-regression/index.html)
- [10 - Support Vector Machine](/content/machine-learning/crash-course/support-vector-machine/index.html)

#### Unsupervised Learning

- [11 - K-means](/content/machine-learning/crash-course/k-means/index.html)
- [12 - Singular Value Decomposition](/content/machine-learning/crash-course/singular-value-decomposition/index.html)

#### Deep Learning

- [13 - Neural Networks](/content/machine-learning/crash-course/neural-networks/index.html)
- [14 - Convolutional Neural Networks](/content/machine-learning/crash-course/convolutional-neural-networks/index.html) FREE
- [15 - Recurrent Neural Networks](/content/machine-learning/crash-course/recurrent-neural-networks/index.html)
- [16 - Generative Adversarial Neural Networks](/content/machine-learning/crash-course/generative-adversarial-neural-networks/index.html)

#### Recommender Systems

- [17 - Collaborative and Content Based Filtering](/content/machine-learning/crash-course/collaborative-and-content-based-filtering/index.html)

#### Ranking

- [18 - Learning To Rank](/content/machine-learning/crash-course/learning-to-rank/index.html)

## 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.