Machine Learning with Python - A Practical Introduction, Certificate | Part time online | edX - online learning platform | United States
2 months
Duration
99 USD/full
99 USD/full
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Tuition fee
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About

EdX is an online learning platform trusted by over 12 million users offering the Machine Learning with Python - A Practical Introduction in collaboration with IBMx. Machine Learning can be an incredibly beneficial tool to uncover hidden insights and predict future trends.

Visit the Visit programme website for more information

Overview

This Machine Learning with Python - A Practical Introduction course at IBMx 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.

This course is part of the Python Data Science Professional Certificate

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

  • Explain the difference between the two main types of machine learning methods: supervised and unsupervised

  • Describe Supervised learning algorithms, including classification and regression
  • Describe Unsupervised learning algorithms, including Clustering and Dimensionality Reduction
  • Explain how statistical modelling relates to machine learning and how to compare them
  • Discuss real-life examples of the different ways machine learning affects society
  • Build a prediction model using classification

Programme Structure

Courses include:

Module 1 - 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

Key information

Duration

  • Part-time
    • 2 months
    • 4 hrs/week

Start dates & application deadlines

You can apply for and start this programme anytime.

Language

English

Delivered

Online
  • Self-paced

Academic requirements

We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.

English requirements

We are not aware of any English requirements for this programme.

Other requirements

General requirements

Prerequisites:

  • Recommended: Python Basics for Data Science

Tuition Fee

To always see correct tuition fees
  • International

    99 USD/full
    Tuition Fee
    Based on the tuition of 99 USD for the full programme during 2 months.
  • National

    99 USD/full
    Tuition Fee
    Based on the tuition of 99 USD for the full programme during 2 months.

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