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
Get more details
Visit programme websiteProgramme 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
Check out the full curriculum
Visit programme websiteKey information
Duration
- Part-time
- 2 months
- 4 hrs/week
Start dates & application deadlines
Language
Delivered
- Self-paced
Disciplines
Software Engineering Machine Learning View 239 other Short Courses in Machine Learning in United StatesExplore more key information
Visit programme websiteAcademic 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
Make sure you meet all requirements
Visit programme websiteTuition Fee
-
International
99 USD/fullTuition FeeBased on the tuition of 99 USD for the full programme during 2 months. -
National
99 USD/fullTuition FeeBased on the tuition of 99 USD for the full programme during 2 months.
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