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
The libraries used for this exercise are Pandas, Seaborn, Matplotlib, and Pyplot (a MATLAB-like plotting framework that combines Pyplot with NumPy), as well as `Scikit-learn.`


#1. Import libraries
import pandas as pd 
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split 
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, classification_report

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

# 2) Import dataset
The next important step is to import the CSV dataset into Jupyter Notebook as a Pandas data frame after downloading it from [the Kickstarter dataset](https://www.kaggle.com/tayoaki/kickstarter-dataset).


This lesson will start to introduce the implementation steps (1-4) of logistic regression.

Logistic Regression Steps: 1 to 4

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

Logistic Regression Steps: 1 to 4

1) Import libraries