# Data Science & Analytics

**18,895 learners**

## Introduction to Supervised Machine Learning

An overview course into supervised machine learning techniques, focusing particularly on linear and logistic regression. By working with real-world datasets, you will implement both models to predict outputs and analyze the most predictive features.

### Tools Used

- Numpy  
    
- Pandas  
    
- Python  
    
- Scikit-learn

### Course Details

**Start learning**: [See path](https://codesignal.com/learn/paths/journey-into-data-science-with-python)

- 6 lessons  
- 28 practices  
- 4 hours

## Course Units

### Unit 1

- 4 practices  
- 32 min  
- Diving into the Wine Quality Dataset: An In-depth Overview  
  [Preview](https://codesignal.com/learn/courses/introduction-to-supervised-machine-learning/lessons/diving-into-the-wine-quality-dataset-an-in-depth-overview)  
- Discovering the Wine Quality Dataset  
- Explore and Debug the Martian Wine Festival Data  
- Adding the Quality Distribution Histogram to the Red Wine Exploration  
- Examining Quality Distribution and Missing Values in Red Wine Dataset

### Unit 2

- 5 practices  
- 38 min

### Unit 3

- 5 practices  
- 38 min

### Unit 4

- 5 practices  
- 38 min

### Unit 5

- 4 practices  
- 32 min

### Unit 6

- 5 practices  
- 38 min

### Course Navigation

[Previous Course](https://codesignal.com/learn/courses/deep-dive-into-numpy-and-pandas-with-housing-data)  
[Next Course: Intro to Unsupervised Machine Learning](https://codesignal.com/learn/courses/intro-to-unsupervised-machine-learning)

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