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

# 1) Import libraries
You will be using **SVC** (Support Vector Classifier) from the Scikit-learn library to implement this model and evaluating the predictions using a classification report and confusion matrix. You will later optimize the model using grid search.


#1- Import libraries
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.model_selection import GridSearchCV

> __Note__: Codes of further steps won't include codes of previous steps. They're already appended at the backend for you.

# 2) Import dataset
Use `pd.read_csv` to import the Advertising dataset.


This lesson will cover the implementation steps (1-7) of support vector machines.

SVM Implementation Steps: 1 to 7

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

SVM Implementation Steps: 1 to 7

1) Import libraries