Introduction to Machine Learning with Python

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Introduction to Machine Learning with Python

This course is part of Python: A Guided Journey from Introduction to Application Specialization

Instructor: Adwith Malpe

3,220 already enrolled

Included with

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4 modules

Gain insight into a topic and learn the fundamentals.

24 reviews

Beginner level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

24 reviews

Beginner level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Students will be able to apply advanced python coding skills in the real world by creating machine learning models.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

9 assignments

Taught in English

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This course is part of the Python: A Guided Journey from Introduction to Application Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 4 modules in this course

This course will give you an introduction to machine learning with the Python programming language. You will learn about supervised learning, unsupervised learning, deep learning, image processing, and generative adversarial networks. You will implement machine learning models using Python and will learn about the many applications of machine learning used in industry today. You will also learn about and use different machine learning algorithms to create your models. You do not need a programming or computer science background to learn the material in this course. This course is open to anyone who is interested in learning how to code and write programs in Python. We are very excited that you will be learning with us and hope you enjoy the course!

What's included

1 video1 reading

In this module you will learn about machine learning and how each branch of machine learning works in Python.

What's included

6 videos12 readings3 assignments

6 videos Total 51 minutes

Overview of Machine Learning 6 minutes
Introduction to Supervised Learning 4 minutes
Introduction to Unsupervised Learning 4 minutes
How to Load and Process Data Sets 10 minutes
What is a Perceptron 14 minutes
What is Linear Regression? 14 minutes

12 readings Total 124 minutes

Lesson 1 Overview 1 minute
Weekly Lesson PowerPoint 10 minutes
Supervised Learning Example 10 minutes
Unsupervised Learning Example 10 minutes
Lesson 2 Overview 1 minute
Weekly Lesson PowerPoint 10 minutes
Lesson 3 Overview 1 minute
Weekly Lesson PowerPoint 10 minutes
Perceptron Code Example 30 minutes
Lesson 4 Overview 1 minute
Weekly Lesson PowerPoint 10 minutes
Linear Regression Code Example 30 minutes

3 assignments Total 90 minutes

Machine Learning Quiz 30 minutes
Datasets Quiz 30 minutes
Formative Assessment: Perceptrons and Linear Regression 30 minutes

In this module, you will learn about two other supervised machine learning models: k-nearest neighbors (kNN) and support vector machines (SVM). You will learn under which conditions you’d use these two models. You will also learn about unsupervised machine learning models and how they work.

What's included

4 videos11 readings3 assignments1 discussion prompt

4 videos Total 42 minutes

What is K-Nearest Neighbors 14 minutes
What are Support Vector Machines? 12 minutes
What is Regression Analysis? 4 minutes
What is Cluster Analysis? (K-Means and DBSCAN) 13 minutes

11 readings Total 134 minutes

Lesson 1 Overview 1 minute
Weekly Lesson PowerPoint 10 minutes
KNN Code Example 30 minutes
Lesson 2 Overview 1 minute
Weekly Lesson PowerPoint 10 minutes
Support Vector Machines Code Example 30 minutes
Lesson 3 Overview 1 minute
Weekly Lesson PowerPoint 10 minutes
Lesson 4 Overview 1 minute
Weekly Lesson PowerPoint 10 minutes
K-Means Clustering Code Example 30 minutes

3 assignments Total 90 minutes

Supervised Learning Quiz 30 minutes
Unsupervised Learning Quiz 30 minutes
Formative Assessment: Supervised Learning 30 minutes

1 discussion prompt Total 10 minutes

Unsupervised Learning 10 minutes

In this module, you will gain an overview of advanced machine learning topics, including deep learning, image processing, and generative adversarial networks (GANs).

What's included

4 videos6 readings3 assignments1 peer review1 discussion prompt

4 videos Total 14 minutes

Overview of Deep Learning 4 minutes
Overview of Image Processing 4 minutes
Overview of GANs 4 minutes
Course Review 1 minute

6 readings Total 33 minutes

Lesson 1 Overview 1 minute
Weekly Lesson PowerPoint 10 minutes
Lesson 2 Overview 1 minute
Weekly Lesson PowerPoint 10 minutes
Lesson 3 Overview 1 minute
Weekly Lesson PowerPoint 10 minutes

3 assignments Total 90 minutes

Deep Learning Quiz 30 minutes
Image Processing Code 30 minutes
Generative Adversarial Networks 30 minutes

1 peer review Total 60 minutes

Make a Classifier 60 minutes

1 discussion prompt Total 10 minutes

Generative Adversarial Networks 10 minutes

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Instructor

Instructor ratings

(9 ratings)

Adwith Malpe

Arizona State University

4 Courses 10,850 learners

Offered by

Arizona State University

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