Key Concepts Machine Learning from Pluralsight

This course will introduce you to the concepts needed to identify use-cases for Machine Learning, formulate an ML problem, enumerate the canonical problems that ML is used to solve, and detail how ML is applied to complex data such as text, images and speech.

Machine Learning algorithms have the ability to adapt and learn from past experiences. Machine learning is important because of its wide range of applications and its incredible ability to adapt and provide solutions to complex problems.

In this course, Key Concepts Machine Learning, you will learn to identify use-cases where ML can provide an appropriate solution, and recognize common structures in ML-based solutions.

First, you will explore the limitations of rule-based approaches and how ML mitigates them. Then, you will discover the different types of ML models such as traditional models and representation models and see how you can develop the ML mindset to find solutions to meet your use case.

Next, you will explore common ML use cases such as regression, classification, clustering, and dimensionality reduction and learn the differences between supervised and unsupervised learning. You will also study specialized use cases such as recommendation systems, association rules learning, and reinforcement learning, as well as learning to apply ML to complex data such as text, images, and speech data.

Finally, you will learn how to formulate your use-case into one of these problem types so that it can then be solved with a commonly used ML-based approach.

When you are finished with this course, you will have the skills and knowledge of the conceptual underpinnings of Machine Learning needed to recognize use-cases for ML, and adopt common ML approaches.

What's inside

Syllabus

Course Overview

Introducing Machine Learning Concept

Identifying Problems Solved Using Machine Learning

Applying Machine Learning to Complex Data

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Begins by establishing the limits of rule-based approaches and gains an understanding of ML's role in this context

Covers common ML use cases such as regression, classification, clustering, and dimensionality reduction, which are essential topics in the field

Examines specialized use cases such as recommendation systems, association rules learning, and reinforcement learning, providing valuable insights for practical applications

Incorporates hands-on learning through the use of labs and interactive materials, enhancing practical skills

Provides a comprehensive overview of the conceptual underpinnings of ML, catering to absolute beginners

Taught by Janani Ravi, an experienced instructor whose expertise in the field adds credibility to the course

Reviews summary

Foundational machine learning concepts

According to learners, this course provides a largely positive and excellent foundational understanding of Machine Learning concepts, making it perfect for anyone starting out in the field. Students particularly appreciate the course's clear, concise explanations and its strong focus on identifying ML use-cases and applying ML to complex data. Many find it highly beneficial for non-technical roles or those seeking to understand the 'what' and 'why' of ML without diving into coding. However, some reviews indicate it might be too superficial for those with prior ML exposure or those expecting deep dives into practical algorithm implementation, which is a key distinction for prospective students.

Well-suited for non-technical audiences and newcomers.

"It's perfect for anyone starting out in the field. The explanations are clear, concise, and the examples are very helpful."

"It covers a broad range of key concepts without getting bogged down in too much math, which was great for me as a business professional."

"A great starting point for anyone interested in the field without needing to code or for non-technical roles."

Helps in recognizing and formulating ML problems effectively.

"I particularly appreciated the modules on identifying use-cases and applying ML to complex data like text and images."

"The course excelled at explaining different types of ML problems and how they relate to real-world scenarios."

"The focus on identifying use-cases is very practical and helped clarify misconceptions about what ML can and cannot do."

Excellent for building a solid understanding of ML principles.

"This course provides an excellent foundational understanding of Machine Learning concepts. It's perfect for anyone starting out in the field."

"Absolutely fantastic for a conceptual understanding! I gained the language and frameworks to discuss ML effectively."

"For foundational knowledge, this course is brilliant. It clearly outlines the core concepts and various applications of ML."

May be too basic for those with existing ML exposure.

"The course is okay for a very basic overview. If you already have some exposure to ML, you might find it too superficial."

"While good for beginners, I found it too high-level and wished for more depth on certain topics."

"I have some background in ML, and this felt more like a review of terms rather than new learning."

Does not cover hands-on coding or deep technical application.

"If you want to actually *do* machine learning, this isn't for you. It's more for managers or people who just want to 'talk the talk.'"

"It stays very high-level and doesn't dive into the practical implementation of algorithms, which I was hoping for."

"I expected more practical application or at least a deeper dive into how models work. This is purely conceptual and very theoretical."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Key Concepts Machine Learning with these activities:

Review Linear Algebra and Calculus

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Refreshes mathematical foundations essential for machine learning.

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Review linear algebra concepts
Review calculus concepts

Watch Video Tutorials on Machine Learning

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Provides additional visual and interactive resources to enhance understanding.

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Watch tutorials on YouTube
Watch tutorials on Coursera
Watch tutorials on edX

Connect with Machine Learning Mentors

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Offers guidance and support from experienced individuals.

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Attend industry events
Join online communities
Reach out to potential mentors

Six other activities

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Read 'An Introduction to Statistical Learning'

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Provides a strong foundation in the fundamentals of machine learning algorithms and their applications.

View An Introduction to Statistical Learning: with... on Amazon

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Read Chapter 1: Introduction
Read Chapter 2: Supervised Learning
Read Chapter 3: Unsupervised Learning

Participate in Study Groups

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Offers opportunities for collaboration, sharing knowledge, and support.

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Join a study group
Attend study group meetings
Participate in discussions

Solve Machine Learning Coding Problems

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Improves coding skills and reinforces understanding of machine learning algorithms.

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Solve coding problems on LeetCode
Solve coding problems on HackerRank
Solve coding problems on Kaggle

Build a Machine Learning Model

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Provides practical experience in developing, evaluating, and deploying machine learning models.

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Choose a dataset
Preprocess the data
Train a machine learning model
Evaluate the model
Deploy the model

Attend Machine Learning Workshops

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Provides opportunities to learn from experts and engage in hands-on activities.

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Find machine learning workshops
Register for workshops
Attend workshops

Contribute to Open Source Machine Learning Projects

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Provides hands-on experience and encourages collaboration in the machine learning community.

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Find an open source machine learning project
Contribute code
Participate in discussions
Report bugs

Career center

Learners who complete Key Concepts Machine Learning will develop knowledge and skills that may be useful to these careers:

Machine Learning Researcher

A Machine Learning Researcher develops new machine learning algorithms and techniques. This course may be useful in developing the theoretical understanding of machine learning needed to succeed in this role. The course covers supervised and unsupervised learning, as well as dimensionality reduction and working with complex data such as text, images, and speech data. This knowledge can help individuals build a strong foundation in machine learning research.

See salaries and explore the career path for Machine Learning Researcher

Data Scientist

A Data Scientist uses machine learning and statistical techniques to extract insights from data. This course may be useful in developing the conceptual understanding of machine learning needed to succeed in this role. The course covers supervised and unsupervised learning, as well as dimensionality reduction and working with complex data such as text, images, and speech data. This knowledge can help individuals build a solid foundation in the field of data science.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

A Machine Learning Engineer applies machine learning algorithms to solve real-world problems. This course may be useful in developing foundational knowledge of the concepts of machine learning and their applications in various domains. The course covers supervised and unsupervised learning, dimensionality reduction, and working with complex data such as text, images, and speech data. By learning these concepts, individuals can build a foundation for a career in machine learning engineering.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

A Data Analyst analyzes data to identify trends and patterns. This course may be useful in developing the data analysis skills needed to succeed in this role. The course covers supervised and unsupervised learning, as well as dimensionality reduction and working with complex data such as text, images, and speech data. This knowledge can help individuals build a foundation for a career in data analysis.

See salaries and explore the career path for Data Analyst

Software Engineer

A Software Engineer designs, develops, and maintains software systems. This course may be useful in developing the programming skills needed to implement machine learning algorithms. The course covers supervised and unsupervised learning, as well as dimensionality reduction and working with complex data such as text, images, and speech data. This knowledge can help individuals build a foundation for a career in software engineering.

See salaries and explore the career path for Software Engineer

Product Manager

A Product Manager develops and manages products. This course may be useful in developing the understanding of machine learning needed to build data-driven products. The course covers supervised and unsupervised learning, as well as dimensionality reduction and working with complex data such as text, images, and speech data. This knowledge can help individuals build a foundation for a career in product management.

See salaries and explore the career path for Product Manager

Business Analyst

A Business Analyst analyzes business processes and identifies areas for improvement. This course may be useful in developing the understanding of machine learning needed to automate business processes. The course covers supervised and unsupervised learning, as well as dimensionality reduction and working with complex data such as text, images, and speech data. This knowledge can help individuals build a foundation for a career in business analysis.

See salaries and explore the career path for Business Analyst

Consultant

A Consultant provides advice and guidance to clients. This course may be useful in developing the understanding of machine learning needed to advise clients on how to use machine learning to solve business problems. The course covers supervised and unsupervised learning, as well as dimensionality reduction and working with complex data such as text, images, and speech data. This knowledge can help individuals build a foundation for a career in consulting.

See salaries and explore the career path for Consultant

Quantitative Analyst

A Quantitative Analyst uses mathematical and statistical techniques to analyze financial data. This course may be useful in developing the understanding of machine learning needed to develop trading strategies. The course covers supervised and unsupervised learning, as well as dimensionality reduction and working with complex data such as text, images, and speech data. This knowledge can help individuals build a foundation for a career in quantitative analysis.

See salaries and explore the career path for Quantitative Analyst

Actuary

An Actuary uses mathematical and statistical techniques to assess risk. This course may be useful in developing the understanding of machine learning needed to develop risk management models. The course covers supervised and unsupervised learning, as well as dimensionality reduction and working with complex data such as text, images, and speech data. This knowledge can help individuals build a foundation for a career in actuarial science.

See salaries and explore the career path for Actuary

Statistician

A Statistician uses mathematical and statistical techniques to analyze data. This course may be useful in developing the understanding of machine learning needed to develop statistical models. The course covers supervised and unsupervised learning, as well as dimensionality reduction and working with complex data such as text, images, and speech data. This knowledge can help individuals build a foundation for a career in statistics.

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Operations Research Analyst

An Operations Research Analyst uses mathematical and statistical techniques to improve the efficiency of business operations. This course may be useful in developing the understanding of machine learning needed to develop optimization models. The course covers supervised and unsupervised learning, as well as dimensionality reduction and working with complex data such as text, images, and speech data. This knowledge can help individuals build a foundation for a career in operations research.

See salaries and explore the career path for Operations Research Analyst

Market Research Analyst

A Market Research Analyst analyzes market data to identify trends and opportunities. This course may be useful in developing the understanding of machine learning needed to develop predictive models. The course covers supervised and unsupervised learning, as well as dimensionality reduction and working with complex data such as text, images, and speech data. This knowledge can help individuals build a foundation for a career in market research.

See salaries and explore the career path for Market Research Analyst

Financial Analyst

A Financial Analyst analyzes financial data to make investment recommendations. This course may be useful in developing the understanding of machine learning needed to develop trading strategies. The course covers supervised and unsupervised learning, as well as dimensionality reduction and working with complex data such as text, images, and speech data. This knowledge can help individuals build a foundation for a career in financial analysis.

See salaries and explore the career path for Financial Analyst

Risk Manager

A Risk Manager assesses and manages risk. This course may be useful in developing the understanding of machine learning needed to develop risk management models. The course covers supervised and unsupervised learning, as well as dimensionality reduction and working with complex data such as text, images, and speech data. This knowledge can help individuals build a foundation for a career in risk management.

See salaries and explore the career path for Risk Manager