You may have seen elsewhere on our site a list of what we think are the top 5 online AI courses available right now. In that article, we mentioned machine learning online courses and stated that we would come back to the topic in a future article. Well, that future article is here. Machine learning in the AI field is a specific method of data analysis that automates model building. It works on the idea that systems can learn from data to identify patterns and eventually make decisions without human interaction.
Machine learning is at the cutting edge of AI technology and has become an incredibly popular field. The question is, how do you learn machine learning techniques so that you have the skills to use it professionally. Well, you’re in luck. We have scoured the internet to find the best machine learning online courses available right now, so you don’t have to.
Machine Learning Online Courses Overview
Machine Learning by Stanford University
This machine learning online course provides a broad introduction to machine learning, including data mining and statistical pattern recognition. The course covers:
- Discussion of the most effective machine learning techniques.
- Practice implementing the most effective techniques to benefit your own endeavors.
- Theory and practice of parametric/non-parametric algorithms, support vector machines, kernels, neural networks.
- Analysis of case studies.
- How learning algorithms can be applied to build smart robots and text understanding programs for web search and anti-spam programs.
Structuring Machine Learning Projects by deeplearning.ai
This short online module is part of the “Deep Learning” specialization and takes about 2 weeks to complete. In the course, you will learn Machine Learning, Deep Learning, Inductive Transfer, Multi-Task Learning and many more aspects of AI and machine learning. The course features:
- Understand how to spot errors in machine learning systems.
- Be able to see what the best direction is for reducing error.
- Understand complex machine learning settings, including things like mismatched training and test sets.
- Understand how to apply end-to-end learning, multi-task learning, and transfer learning.
Applied Machine Learning in Python by University of Michigan
This module is part of the “Applied Data Science with Python” specialization and is only recommended to be taken after you’ve completed the Data Science in Python and Applied Plotting, and the Charting & Data Representation in Python courses. It is also recommended to be taken before the Applied Text Mining in Python and Applied Social Analysis in Python modules. The course will allow you to gain skills in Python Programming, Machine Learning, as well as Machine Learning Algorithms. The course covers the following topics:
- Be able to build features that meet your and others’ analysis needs.
- Be able to create and evaluate data clusters effectively.
- Be able to describe how machine learning is different to other statistical methods.
- Be able to explain different approaches to creating predictive models.
Become a Machine Learning Engineer Nanodegree by Udacity
This is another short, Nanodegree program that takes about 3 months to complete. It aims to teach students advanced machine learning techniques and algorithms, including how to deploy them in a production environment. The course covers:
- The fundamentals of being a software Engineering.
- How to write a production-level code.
- The practice of object-oriented programming.
- How to deploy machine learning into a production environment using software like Amazon SageMaker.
- How to apply machine learning techniques in real-world problems.
- How to use machine learning to create a plagiarism checker.
Intro to Machine Learning Nanodegree by Udacity
This Nanodegree is another short course that takes about three months to complete. It teaches you how to use supervised and unsupervised learning techniques alongside Deep Learning and Machine Learning. A good understanding of Python Programming is required as well as a decent level of mathematics. The course covers:
- How supervised learning works for model construction.
- The foundations of neural network design and training using PyTorch.
- How to implement unsupervised learning methods for different problems in different domains.