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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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 of model design

Before exploring specific supervised learning algorithms, it’s useful to pause and take a high-level look at the full procedure of building a machine learning model. This involves reviewing several steps examined in the preceding chapters and introducing new methods, including evaluating and predicting. 

These 10 steps take place inside your development environment and follow a relatively fixed sequence. Once you are familiar with this framework, you will find it easy to design your own machine learning models from start to finish.




The following steps are involved in the model design:

- 1) Import libraries
- 2) Import dataset
- 3) Exploratory data analysis
- 4) Data scrubbing
- 5) Pre-model algorithm
- 6) Split validation
- 7) Set algorithm
- 8) Predict
- 9) Evaluate
- 10) Optimize

# 1) Import libraries
Given that the Python interpreter works from top to bottom through your code, it’s vital to import libraries before calling any specific functions. For example, if you attempt to create a heatmap or pair plot without first importing Seaborn and Matplolib, the Python interpreter won’t be able to process your request.

The libraries don’t necessarily need to be placed in the top section of your notebook. Some data scientists, for instance, prefer to import specific algorithm-based libraries in sections where they are used as long as the import is made ahead of code references to that library.

# 2) Import dataset
Datasets are generally imported from your organization’s records or public repositories such as [Kaggle](https://www.kaggle.com/). While Kaggle has a fantastic offering of datasets, it’s worth mentioning that `Scikit-learn` offers several small built-in datasets that don’t necessitate an external download. 
> As noted by Scikit-learn, these datasets are useful for beginners to understand new algorithms. Scikit-learn’s datasets are summarized below and can be imported directly into your notebook.


This lesson will introduce you to the steps to design a machine learning model.

Introduction to Model Design

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

Introduction to Model Design

Quick overview of model design