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Best Machine Learning courses on Pluralsight

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Welcome to our comprehensive guide on Machine Learning courses available on Pluralsight! In today’s rapidly evolving technological landscape, mastering Machine Learning is not just an advantage; it’s become a necessity. Whether you’re a seasoned professional looking to upskill or someone venturing into the world of AI and data science for the first time, Pluralsight offers a diverse range of courses tailored to suit every learning style and skill level.

From foundational concepts to advanced algorithms, our curated selection of Machine Learning courses will equip you with the knowledge and practical skills needed to thrive in this exciting field. Join us as we delve into the wealth of resources awaiting you on Pluralsight.

Designing a Machine Learning Model (get this course for free)

This course covers the important differences between various canonical problems in machine learning, as well as the considerations in choosing the right solution techniques, based on the specifics of the problem you are trying to solve and the data that you have available.

In this course, Designing a Machine Learning Model you will gain the ability to appropriately frame your use-case and then choose the right solution technique to model it.

First, you will learn how rule-based systems and ML systems differ and how traditional and deep learning models work. Next, you will discover how supervised, unsupervised, and reinforcement learning techniques differ from each other. You will learn how classic supervised learning techniques such as regression and classification complement classic unsupervised techniques such as clustering and dimensionality reduction. You will then understand the assumptions and outcomes of these four classes of techniques and how solutions can be evaluated.

Finally, you will round out your knowledge by designing end-to-end ML workflows for canonical ML problems, ensemble learning, and neural networks.

Course Duration: 3.25hrs | Best for: Intermediate level | Course Pricing: Free

Info: Visit this course and get a free course

Understanding Machine Learning

Need a short, clear introduction to machine learning? Watch this. David Chappel is the author of Understanding Machine Learning.

Have you ever wondered what machine learning is? That’s what this course is designed to teach you. You’ll explore the open source programming language R, learn about training and testing a model as well as using a model. By the time you’re done, you’ll have a clear understanding of exactly what machine learning is all about.

It’s all ready and waiting for you – jump in whenever you’re ready, and thanks for visiting me here at Pluralsight.

Course Duration: 43minutes | Best for: Beginners | Course Pricing: Free

Info: Visit this free course and learn the basics of ML

Preparing Data for Machine Learning

This course covers important techniques in data preparation, data cleaning and feature selection that are needed to set your machine learning model up for success. You will also learn how to use imputation to deal with missing data and strategies for identifying and coping with outliers.

In this course, Preparing Data for Machine Learning* you will gain the ability to explore, clean, and structure your data in ways that get the best out of your machine learning model.

First, you will learn why data cleaning and data preparation are so important, and how missing data, outliers, and other data-related problems can be solved. Next, you will discover how models that read too much into data suffer from a problem called overfitting, in which models perform well under test conditions but struggle in live deployments. You will also understand how models that are trained with insufficient or unrepresentative data suffer from a different set of problems, and how these problems can be mitigated.

Finally, you will round out your knowledge by applying different methods for feature selection, dealing with missing data using imputation, and building your models using the most relevant features.

When you’re finished with this course, you will have the skills and knowledge to identify the right data procedures for data cleaning and data preparation to set your model up for success.

Course Duration: 3hrs 24minutes | Best for: Beginners | Course Pricing: Free

Info: Visit this free course and learn how to prepare the data for Machine Learning

Understanding Machine Learning with Python 3

Use your data to predict future events with the help of machine learning. This course will walk you through creating a machine learning prediction solution and will introduce Python, the scikit-learn library, and the Jupyter Notebook environment.

In this course, you will gain an understanding of how to use Python for Machine Learning. You will get there by covering major topics like:

  • How to format your problem to be solvable
  • How to prepare your data for use in a prediction
  • How to combine that data with algorithms to create models that can predict the future

By the end of this course, you will be able to use Python and the scikit-learn library to create Machine Learning solutions. And you will understand how to evaluate and improve the performance of the solutions you create.

Before you begin, make sure you are already familiar with software development and basic statistics. However, your software experience does not have to be in Python, since you will learn the basics in this course.

When you use Python together with scikit-learn, you will see why this is the preferred development environment for many Machine Learning practitioners. You will do all the demos using the Jupyter Notebook environment. This environment combines live code with narrative text to create a document with can be executed and presented as a web page.

Course Duration: 1hrs 54minutes | Best for: Beginners | Course Pricing: Free

Info: Visit this free course and learn Understanding ML with Python3

Fundamentals of Machine Learning on AWS

This course will teach you how to get started solving business problems with AWS machine learning technologies.

You’ve probably heard about how machine learning is shaping our world–from facial recognition to package delivery, speech recognition to self-driving cars. But how do you get started in this exciting field?

In this course, Fundamentals of Machine Learning on AWS, you’ll learn how to solve business problems with AWS machine learning technologies. First, you’ll explore what ML is and how it relates to artificial intelligence and deep learning. Next, you’ll learn how to identify and frame opportunities for machine learning.

Then, you’ll discover the end-to-end machine learning process: fetching, cleaning and preparing data, training and evaluating models, and deploying and monitoring models. Finally, you’ll learn the AWS artificial intelligence and machine learning technologies that enable this process, and see them in action with Amazon SageMaker Studio.

When you’re finished with this course, you’ll have the skills and knowledge of AWS machine learning technologies needed to solve real-world problems. This course will also lay the foundation for the AWS Machine Learning Specialty certification.

Course Duration: 2hrs 18minutes | Best for: Intermediates | Course Pricing: Free

Info: Visit this free course and learn about the Fundamentals of ML on AWS

Understanding Algorithms for Reinforcement Learning

Reinforcement learning is a type of machine learning which allows decision makers to operate in an unknown environment. In the world of self-driving cars and exploring robots, RL is an important field of study for any student of machine learning.

You’ll learn basic principles of reinforcement learning algorithms, RL taxonomy, and specific policy search techniques such as Q-learning and SARSA. First, you’ll discover the objective of reinforcement learning; to find an optimal policy which allows agents to make the right decisions to maximize long-term rewards.

You’ll study how to model the environment so that RL algorithms are computationally tractable. Next, you’ll explore dynamic programming, an important technique used to cache intermediate results which simplify the computation of complex problems.

You’ll understand and implement policy search techniques such as temporal difference learning (Q-learning) and SARSA which help converge on to an optimal policy for your RL algorithm.

Finally, you’ll build reinforcement learning platforms which allow study, prototyping, and development of policies, as well as work with both Q-learning and SARSA techniques on OpenAI Gym.

By the end of this course, you should have a solid understanding of reinforcement learning techniques, Q-learning and SARSA and be able to implement basic RL algorithms.

Course Duration: 2hrs 7minutes | Best for: Beginners | Course Pricing: Free

Info: Visit this free course and learn about Understanding Algorithms for Reinforcement Learning

Machine Learning for Healthcare:

This course will explore the conceptual aspects of applying machine learning to problems in the healthcare industry, discuss case studies of machine learning used in healthcare, and explore practical implementations of techniques on real-world data from that industry.

In this course, Machine Learning for Healthcare, you’ll explore machine learning techniques currently applied in the healthcare industry. First, you’ll explore a few specific use cases such as the use of ML techniques for epidemic control, AI-assisted robotic surgery, patient diagnosis, and the automation of administrative tasks. You will also get an intuitive understanding of how convolutional neural networks work and how they are used in medical imaging.

Next, you will understand the steps involved in applying machine learning techniques to chronic disease prediction. You will study a case from a research paper that uses natural language processing and text extraction techniques on medical notes to diagnose chronic diseases for hospital patients. Another case study will discuss the use of medical imaging and image preprocessing techniques to detect leukemia from microscopic blood cell images.

Finally, you will get hands-on coding and see how you can use regression models to predict blood pressure and classification models to predict liver disease.

Course Duration: 1hrs 48minutes | Best for: Beginners | Course Pricing: Free

Info: Visit this free course and learn more skills on ML for Healthcare

Conclusion:

In conclusion, the Machine Learning courses on Pluralsight stand as a beacon of opportunity for individuals seeking to embark on a journey of discovery or further their expertise in this transformative field. With accessible, expert-led instruction and hands-on learning experiences, Pluralsight empowers learners to unlock their full potential and become proficient in Machine Learning. Start your learning journey today and chart a course towards a future filled with innovation and endless possibilities.

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  1. Pingback: Best free Machine Learning courses on Pluralsight for Beginners to Intermediate - AI Story Sphere

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