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5 Free Machine Learning Courses You Should Enroll in the First Month of LinkedIn Learning!

Machine Learning with Python
Data Science

5 Free Machine Learning Courses You Should Enroll in the First Month of LinkedIn Learning!

LinkedIn Learning has introduced a free trial month plan for new students where more than 15,000+ courses on its platform can be accessed and learned from for free.

In this article, we’ve listed the 5 free Machine Learning courses you should enroll and learn from in your LinkedIn Learning trial month.

1. Machine Learning with Python – Try the course for free!

Machine Learning with Python: Foundations

You’ve probably heard about machine learning before, but have you ever wondered what that term really means? How does a machine learn? Have you thought about building a machine learning model, but didn’t know where to start?

In this course, Frederick Nwanganga introduces machine learning in an approachable way and provides step-by-step guidance on how to get started with machine learning via the most in-demand language in use today, Python. Frederick starts with exactly what it means for machines to learn and the different ways they learn, then gets into how to collect, understand, and prepare data for machine learning.

He also provides guided examples of how to accomplish each step using Python. Finally, he brings it all together to build, evaluate, and interpret the results of a machine learning model in Python.

You can enroll in the course (and 15,000+ other courses) for FREE by starting your free trial month at LinkedIn Learning! Click here to enroll in Machine Learning with Python: Foundations.

2. Applied Machine Learning: Foundations – Try the course for free!

Applied Machine Learning: Foundations

Anyone who can write basic Python is capable of fitting a simple machine learning model on a clean dataset. The competitive edge comes in the ability to customize and optimize those models for specific problems. The workflow used to build effective machine learning models and the methods used to optimize those models are typically not algorithm or problem specific.

In this course, the first installment in the two-part Applied Machine Learning series, instructor Derek Jedamski digs into the foundations of machine learning, from exploratory data analysis to evaluating a model to ensure it generalizes to unseen examples. Instead of zeroing in on any specific machine learning algorithm, Derek focuses on giving you the tools to efficiently solve nearly any kind of machine learning problem.

You can enroll in the course (and 15,000+ other courses) for FREE by starting your free trial month at LinkedIn Learning! Click here to enroll in Applied Machine Learning: Foundations.

3. Machine Learning with Scikit-Learn – Try the course for free!

Machine Learning with Scikit-Learn

The ability to apply machine learning algorithms is an important part of a data scientist’s skill set. scikit-learn is a popular open-source Python library that offers user-friendly and efficient versions of common machine learning algorithms.

In this course, data scientist Michael Galarnyk explains how to use scikit-learn for supervised and unsupervised machine learning. Michael reviews the benefits of this easy-to-use API and then quickly segues to practical techniques, starting with linear and logistic regression, decision trees, and random forest models. In chapter three, he covers unsupervised learning techniques such as K-means clustering and principal component analysis (PCA). Plus, learn how to create scikit-learn pipelines to make your code cleaner and more resilient to bugs. By the end of the course, you’ll be able to understand the strengths and weaknesses of each scikit-learn algorithm and build better, more efficient machine learning models.

You can enroll in the course (and 15,000+ other courses) for FREE by starting your free trial month at LinkedIn Learning! Click here to enroll in Machine Learning with Scikit-Learn.

4. Machine Learning and AI Foundations: Decision Trees – Try the course for free!

Machine Learning and AI Foundations: Decision Trees

Many data science specialists are looking to pivot toward focusing on machine learning. This course covers the essentials of machine learning, including predictive analytics and working with decision trees.

Explore several popular tree algorithms and learn how to use reverse engineering to identify specific variables. Demonstrations of using the IBM SPSS Modeler are included so you can understand how decisions trees work. This course is designed to give you a solid foundation on which to build more advanced data science skills.

You can enroll in the course (and 15,000+ other courses) for FREE by starting your free trial month at LinkedIn Learning! Click here to enroll in Machine Learning and AI Foundations: Decision Trees.

5. Advanced NLP with Python for Machine Learning – Try the course for free!

Advanced NLP with Python for Machine Learning

An incredible amount of unstructured text data is generated every day by social media, web pages, and a variety of other sources. But without the ability to tame and harness that data, you’ll be unable to glean any value from it.

In this course, learn how to translate messy text data into powerful insights using Python. Instructor Derek Jedamski begins with a quick review of foundational NLP concepts, including how to clean text data and build a model on top of vectorized text. He then jumps into more complex topics such as word2vec, doc2vec, and recurrent neural networks. To wrap up the course, he lends these concepts a real-world context by applying them to a machine learning problem.

You can enroll in the course (and 15,000+ other courses) for FREE by starting your free trial month at LinkedIn Learning! Click here to enroll in Advanced NLP with Python for Machine Learning.

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