IBM: 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.
There is one session available:
Machine Learning with Python: A Practical Introduction
About this courseSkip About this course
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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!
At a glance
- Language: English
- Video Transcript: English
- Associated programs:
- Associated skills:Unsupervised Learning, Statistical Modeling, Machine Learning, Python (Programming Language), Algorithms, Random Forest Algorithm
What you'll learnSkip What you'll 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
Module 1 - Introduction to Machine Learning
Applications of Machine Learning
Supervised vs Unsupervised Learning
Python libraries suitable for Machine Learning
Module 2 - Regression
Model evaluation methods
Module 3 - Classification
Support Vector Machines
Module 4 - Unsupervised Learning
Module 5 - Recommender Systems
Content-based recommender systems