Explore practical coding of basic machine learning models using Python. Gain insights into algorithms like linear regression, logistic regression, SVM, KNN, and decision trees.

Machine Learning with Python A Practical Beginners’ Guide by Oliver Theobald-01.png

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Mlxtend

This course teaches you how to code basic machine learning models. The content is designed for beginners with general knowledge of machine learning, including common algorithms such as linear regression, logistic regression, SVM, KNN, decision trees, and more. If you need a refresher, we have summarized key concepts from machine learning, and there are overviews of specific algorithms dispersed throughout the course.

A Practical Guide to Machine Learning with Python

## Quick overview
The core of this course is to teach you how to code basic #key# ML: Machine Learning #key# models. The content is designed for beginners with general knowledge of machine learning, including common algorithms like [linear regression](https://www.educative.io/courses/guide-to-machine-learning-python/implementation-of-linear-regression), [logistic regression](https://www.educative.io/courses/guide-to-machine-learning-python/implementation-of-logistic-regression), [support vector machines](https://www.educative.io/courses/guide-to-machine-learning-python/implementation-of-support-vector-machines) and [decision trees](https://www.educative.io/courses/guide-to-machine-learning-python/1-decision-trees). You will get a solid foundation in machine learning algorithms through this course, even if you are totally new to machine learning or just starting in this field.



## Prerequisites

The ideal skill set for a machine learning developer includes: 

* Coding ([Python](https://www.python.org/), [R](https://www.r-project.org/), [SPSS](https://www.ibm.com/analytics/spss-statistics-software), [SAS](https://www.sas.com/en_us/home.html), etc.)
* Data management
* Statistics
* Mathematics

Optional areas of expertise include:
* Data visualization
* Big data management
* Distributed computing architecture



> **Note:** This course covers the vital coding part of machine learning using Python. Therefore, the only prerequisite is foundational knowledge of [Python programming](https://www.educative.io/courses/guide-to-machine-learning-python/basics-of-python-programming).

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# Quick overview
The core of this course is to teach you how to code basic #key# ML: Machine Learning #key# models. The content is designed for beginners with general knowledge of machine learning, including common algorithms like [linear regression](https://www.educative.io/courses/guide-to-machine-learning-python/implementation-of-linear-regression), [logistic regression](https://www.educative.io/courses/guide-to-machine-learning-python/implementation-of-logistic-regression), [support vector machines](https://www.educative.io/courses/guide-to-machine-learning-python/implementation-of-support-vector-machines) and [decision trees](https://www.educative.io/courses/guide-to-machine-learning-python/1-decision-trees). You will get a solid foundation in machine learning algorithms through this course, even if you are totally new to machine learning or just starting in this field.



# Prerequisites

The ideal skill set for a machine learning developer includes: 

* Coding ([Python](https://www.python.org/), [R](https://www.r-project.org/), [SPSS](https://www.ibm.com/analytics/spss-statistics-software), [SAS](https://www.sas.com/en_us/home.html), etc.)
* Data management
* Statistics
* Mathematics

Optional areas of expertise include:
* Data visualization
* Big data management
* Distributed computing architecture



This lesson will introduce the course and its prerequisites.

Getting Started

Introduction to Course

Introduction to Machine Learning

Exploratory Data Analysis

Data Scrubbing

Pre-Model Algorithms

Split Validation

Model Design

Linear Regression

Logistic Regression

Support Vector Machines

K-Nearest Neighbors

Tree-Based Methods

Conclusion

Appendix

Getting Started

Quick overview

Prerequisites