A Practical Guide to Machine Learning with Python

Takeaway Skills

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.

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.).

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, ... See More

How You'll Learn

Hands-on coding environments

You don’t get better at swimming by watching others. Coding is no different. Practice as you learn with live code environments inside your browser.

Faster than videos

Videos are holding you back. The average video tutorial is spoken at 150 words per minute, while you can read at 250. That‘s why our courses are text-based.

No set-up required

Start learning immediately instead of fiddling with SDKs and IDEs. It‘s all on the cloud.

Progress you can show

Built in assessments let you test your skills. Completion certificates let you show them off.

Hands-on coding environments

You don’t get better at swimming by watching others. Coding is no different. Practice as you learn with live code environments inside your browser.

Faster than videos

Videos are holding you back. The average video tutorial is spoken at 150 words per minute, while you can read at 250. That‘s why our courses are text-based.

No set-up required

Start learning immediately instead of fiddling with SDKs and IDEs. It‘s all on the cloud.

Progress you can show

Built in assessments let you test your skills. Completion certificates let you show them off.

Course Contents

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1. Introduction to Course

Getting Started

Why Learn Machine Learning

2. Introduction to Machine Learning

Types of Self Learning

Introduction to Datasets

Machine Learning Libraries

Quiz

3. Exploratory Data Analysis

Import Dataset

Data Frame Functions

Quiz

4. Data Scrubbing

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

5. Pre-Model Algorithms

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

6. Split Validation

Quick Overview of Split Validation

Quiz

7. Model Design

Introduction to Model Design

MD Implementation Steps: 3 to 8

MD Implementation Steps: 9 and 10

Quiz

8. Linear Regression

Implementation of Linear Regression

LR Implementation Steps: 1 to 3

LR Implementation Steps: 4 to 7

LR Implementation Steps: 8 and 9

Quiz

9. Logistic Regression

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

Implementation of Support Vector Machines

SVM Implementation Steps: 1 to 7

SVM Implementation Steps: 8 and 9

Quiz

11. K-Nearest Neighbors

Introduction to K-Nearest Neighbors

k-NN Implementation Steps: 1 to 5

k-NN Implementation Steps: 6 to 9

Quiz

12. Tree-Based Methods

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

What's Next?

14. Appendix

Basics of Python Programming

Development Environment