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A Practical Guide to Machine Learning with Python

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

Beginner

57 Lessons

72h 30min

Certificate of Completion

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

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This course includes

108 Playgrounds

12 Quizzes

Course Overview

What You'll Learn

Course Content

Apply Your Skills

Course Overview

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.

What You'll Learn

Learn fundamental principles and techniques of machine learning.

Understand the benefits and drawbacks of a variety of common machine learning methods.

The key premise of the course is to teach you how to code basic machine learning models.

Develop skills with using machine learning tools to solve real-world issues.

Learn the fundamentals of different learning paradigms (supervised, unsupervised, etc.).

Learn fundamental principles and techniques of machine learning.

Course Content

Introduction to Course

2 Lessons

Get familiar with coding basic machine learning models using Python and its historical importance.

Getting Started

Why Learn Machine Learning

Introduction to Machine Learning

4 Lessons

Look at the essentials of machine learning types, key datasets, and core libraries.

Types of Self Learning

Introduction to Datasets

Machine Learning Libraries

Quiz

Exploratory Data Analysis

3 Lessons

Break apart Exploratory Data Analysis techniques for importing datasets, using data frame functions, and practical quizzes.

Import Dataset

Data Frame Functions

Quiz

Data Scrubbing

6 Lessons

Break down complex ideas in data scrubbing, variable removal, one-hot encoding, and dimension reduction.

Introduction to Data Scrubbing

Data Scrubbing Operation: Removing Variables

Data Scrubbing Operation: One-Hot Encoding

Data Scrubbing Operation: Drop Missing Values

Data Scrubbing Operation: Dimension Reduction

Quiz

Pre-Model Algorithms

5 Lessons

Solve problems in PCA and K-means clustering for dimensionality reduction and data simplification.

PCA Implementation Steps: 1 to 3

PCA Implementation Steps: 4 to 6

K-Means Clustering Implementation Steps: 1 to 3

K-Means Clustering Implementation Steps: 4 to 6

Quiz

Split Validation

2 Lessons

Investigate how split validation partitions data, optimizes models, and ensures unbiased assessments.

Quick Overview of Split Validation

Quiz

Model Design

4 Lessons

Master the steps to design, implement, evaluate, and optimize machine learning models effectively.

Introduction to Model Design

MD Implementation Steps: 3 to 8

MD Implementation Steps: 9 and 10

Quiz

Linear Regression

5 Lessons

Get familiar with implementing linear regression, handling data, and evaluating prediction accuracy.

Implementation of Linear Regression

LR Implementation Steps: 1 to 3

LR Implementation Steps: 4 to 7

LR Implementation Steps: 8 and 9

Quiz

Logistic Regression

5 Lessons

Get started with logistic regression for classification, handling data, and evaluating predictions.

Implementation of Logistic Regression

Logistic Regression Steps: 1 to 4

Logistic Regression Steps: 5 to 7

Logistic Regression Steps: 8 and 9

Quiz

10.

Support Vector Machines

4 Lessons

Go hands-on with implementing and optimizing Support Vector Machines for robust classification.

Implementation of Support Vector Machines

SVM Implementation Steps: 1 to 7

SVM Implementation Steps: 8 and 9

Quiz

11.

K-Nearest Neighbors

4 Lessons

Apply your skills to implement and optimize k-NN models using Python for classification tasks.

Introduction to K-Nearest Neighbors

k-NN Implementation Steps: 1 to 5

k-NN Implementation Steps: 6 to 9

Quiz

12.

Tree-Based Methods

10 Lessons

Dig into core tree-based methods, including decision trees, random forests, and gradient boosting.

Introduction to Tree-Based Methods

1- Decision Trees

DT Implementation Steps: 1 to 4

DT Implementation Steps: 5 to 7

2- Random Forests

Implementation of Random Forest

3- Gradient Boosting

Implementation of Gradient Boosting Classifier

Implementation of Gradient Boosting Regressor

Quiz

13.

Conclusion

1 Lessons

Investigate future growth opportunities in machine learning and stay motivated for continuous learning.

What's Next?

14.

Appendix

2 Lessons

Master Python basics and set up Jupyter Notebook for effective machine learning practice.

Basics of Python Programming

Development Environment

Course Author

Oliver Theobald

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