Machine Learning with Python

MACHINE LEARNING WITH PYTHON: A PRACTICAL INTRODUCTION

Machine Learning can be an incredibly beneficial tool to uncover hidden insights and predict future trends. This Machine Learning with Python course will give you all the tools you need to get started with supervised and unsupervised learning.

What you will learn

The difference between the two main types of machine learning methods: supervised and unsupervised
Supervised learning algorithms, including classification and regression
Unsupervised learning algorithms, including Clustering and Dimensionality Reduction
How statistical modeling relates to machine learning and how to compare them
Real-life examples of the different ways machine learning affects society

ABOUT THIS COURSE

This Machine Learning with Python course dives into the basics of machine learning using Python, an approachable and well-known programming language. You’ll learn about supervised vs. unsupervised learning, look into how statistical modeling relates to machine learning, and do a comparison of each.

We’ll explore many popular algorithms including Classification, Regression, Clustering, and Dimensional Reduction and popular models such as Train/Test Split, Root Mean Squared Error (RMSE), and Random Forests. Along the way, you’ll look at real-life examples of machine learning and see how it affects society in ways you may not have guessed!

Most importantly, you will transform your theoretical knowledge into practical skill using hands-on labs. Get ready to do more learning than your machine!

Mostimportantly, you will transform your theoretical knowledge into practical skill using hands-on labs. Get ready to do more learning than your machine!

SYLLABUS

Module 1 – Introduction to Machine Learning
Applications of Machine Learning
Supervised vs Unsupervised Learning
Python libraries suitable for Machine Learning

Module 2 – Regression
Linear Regression
Non-linear Regression
Model evaluation methods

Module 3 – Classification
K-Nearest Neighbour
Decision Trees
Logistic Regression
Support Vector Machines
Model Evaluation

Module 4 – Unsupervised Learning
K-Means Clustering
Hierarchical Clustering
Density-Based Clustering

Module 5 – Recommender Systems
Content-based recommender systems
Collaborative Filtering

Lenght: 5 Weeks

Effort: 4–6 hours per week

Price: FREE
Add a Verified Certificate for $99 USD

Institution: IBM

Subject: Data and Analysis and Statistics

Level: Introductory

Language: English

Video Transcript: English

Course Type: Self-paced on your time

Associated Programs:

Professional Certificate: Python Data Science

Professional Certificate: IBM Data Science

INSTRUCTOR

Machine Learning with Python: A Practical Introduction

Saeed Aghabozorgi

PhD, Sr. Data Scientist at IBM

Saeed Aghabozorgi, PhD is a Sr. Data Scientist in IBM with a track record of developing enterprise level applications that substantially increases clients’ ability to turn data into actionable knowledge. He is a researcher in data mining field and expert in developing advanced analytic methods like machine learning and statistical modelling on large datasets.

Pursue a Verified Certificate to highlight the knowledge and skills you gain
$99 USD

Saeed Aghabozorgi

PhD, Sr. Data Scientist at IBM

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Who can take this course?

Unfortunately, learners from one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.

Machine Learning with Python