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Machine Learning with Python: A Practical Introduction

, , Prof. Saeed Aghabozorgi 0.0 ( Reviews) 24659 Students Enrolled

Updated On 02 Feb, 19

Overview

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.

Course Description

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.

You'll look at real-life examples of Machine Learning and how it affects society in ways you may not have guessed!

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 and Random Forests.

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

What you will learn


  • Supervised vs Unsupervised Machine Learning

  • How Statistical Modeling relates to Machine Learning, and how to do a comparison of each.

  • Different ways machine learning affects society 

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

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Sam

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