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IBM: Machine Learning with Python: A Practical Introduction

4.5 stars
415 ratings

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.

Machine Learning with Python: A Practical Introduction
5 weeks
4–6 hours per week
Self-paced
Progress at your own speed
Free
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Machine Learning with Python: A Practical Introduction

About this course

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Please Note: Learners who successfully complete this IBM course can earn a skill badge — a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in this course. Enroll to learn more, complete the course and claim your badge!

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!

We'll explore many popular algorithms including Classification, Regression, Clustering, and Dimensional Reduction and popular models such asTrain/Test Split, Root Mean Squared Error and Random Forests.

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

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Machine Learning with Python: A Practical Introduction

At a glance

  • Language: English
  • Video Transcript: English
  • Associated programs:
  • Associated skills:Statistical Modeling, Random Forest Algorithm, Python (Programming Language), Machine Learning, Algorithms, Unsupervised Learning

What you'll learn

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  • 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

Syllabus

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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

Who can take this course?

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This course is part of IBM Data Science Professional Certificate Program

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Expert instruction
10 skill-building courses
Self-paced
Progress at your own speed
1 year
3 - 6 hours per week

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Price

$99 USD

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Expires on Dec 20

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