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A Cloud Guru
Hello, Cloud Gurus! Have you recently been thrown into your first machine learning project and need to get up to speed? Perhaps you’re struggling with all the mathematical jargon in other machine learning introductory courses? Let machine learning Guru Scott Pletcher guide you through the sometimes intimidating world of machine learning in an entertaining and very non-scary way. Many “introductory” ML courses attempt to explain concepts using differential equations and cryptic Greek symbols--but not this course. This course is specifically designed for people without deep math backgrounds, and Scott cuts through the jargon with simile and metaphor to equip you with concepts and understanding you can put to work immediately. Machine learning has certainly garnered lots of attention in recent years as organizations struggle to remain competitive in the Information Age arms race. Start-ups, established companies, and cloud providers are rapidly releasing new features and services aimed at ML practitioners. Unfortunately, the ability to effectively use these services to create genuine and repeatable business value is somewhat limited by the availability of skilled practitioners. Compounding matters, much of the available ML training in the market presumes a level of advanced mathematics knowledge which can be intimidating to novices. In this course, you’ll learn: * Distinction between artificial intelligence, machine learning, data science, and statistical analysis. * The various types of machine learning with real-world examples such as regression, classification, decision trees, and deep learning. We’ll even train a self-driving car! * How to evaluate and frame business problems for potential machine learning applications. * Brief survey of available tools, datasets, and resources common in the machine learning space. * Limitations, potential pitfalls, and ethical considerations around machine learning. This is an introductory course so don’t expect to walk away solving math theorems like in Good Will Hunting. However, do expect to come away with much greater machine learning confidence and understanding, pulling back that complexity curtain to unveil how all this modern-day magic really works. (Spoiler alert: it’s really not magic at all…) So Cloud Gurus, let’s take that first step and get started on your machine learning journey.
Register for this course and see more details by visiting:
OpenCourser.com/course/ck7dyy/introduction
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Outlines the difference between related but distinct fields such as data science and artificial intelligence
Utilizes a conversational and engaging tone, making complex machine learning concepts more accessible
Applicable both in industry and academia, providing real-world examples to enhance understanding
Avoids using technical jargon and Greek symbols, catering specifically to beginners in machine learning
Designed for those with limited mathematical backgrounds, focusing on intuitive explanations and practical applications
Covers a broad range of machine learning topics, including regression, classification, and decision trees
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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 Introduction to Machine Learning with these
activities:
Read a book on machine learning for beginners
Show steps
Expand your knowledge and understanding of machine learning fundamentals by exploring a beginner-friendly book.
View
Data Analytics & Visualization All-in-One For...
on Amazon
Show steps
-
Acquire the book through purchase or borrowing.
-
Set aside dedicated time for reading and comprehension.
-
Take notes or highlight key concepts as you read.
Review machine learning basics
Show steps
Reinforce your understanding of the core concepts of machine learning, such as supervised and unsupervised learning, model training, and evaluation.
Browse courses on
Machine Learning
Show steps
-
Go through your lecture notes and textbooks
-
Review online tutorials and articles
-
Work through practice problems and exercises
Review Math Concepts
Show steps
Review basic mathematical concepts such as algebra, trigonometry, and calculus to strengthen your foundation for machine learning.
Show steps
-
Identify areas for improvement by taking a practice quiz or reviewing old notes.
-
Focus on reviewing the specific mathematical concepts that are most relevant to machine learning.
-
Use online resources, textbooks, or educational videos to refresh your understanding.
15 other activities
Expand to see all activities and additional details
Show all 18 activities
Review differential equations and Greek symbols
Show steps
Get ahead of the potential learning curve by reviewing essential mathematical concepts prior to starting the course.
Browse courses on
Differential Equations
Show steps
-
Review your notes from previous mathematics courses.
-
Seek out online resources or textbooks to supplement your understanding.
-
Take practice quizzes to test your comprehension.
Follow a tutorial on machine learning basics
Show steps
Supplement the course material by exploring additional resources that provide a gentle introduction to machine learning concepts.
Show steps
-
Identify reputable sources for tutorials, such as online courses or YouTube channels.
-
Choose a tutorial that aligns with your current understanding and interests.
-
Follow the tutorial step-by-step and take notes of key concepts.
Follow online tutorials on machine learning
Show steps
Expand your knowledge of machine learning by following structured tutorials and courses.
Browse courses on
Online Courses
Show steps
-
Search for online tutorials or courses on machine learning
-
Select a tutorial or course that aligns with your learning goals
-
Follow the instructions and complete the exercises
-
Apply what you learn to your own projects
Follow Guided Tutorials on Machine Learning Fundamentals
Show steps
Supplement your coursework with guided tutorials to reinforce your understanding of foundational machine learning principles.
Browse courses on
Machine Learning Fundamentals
Show steps
-
Identify reputable online platforms or courses that offer beginner-friendly tutorials.
-
Set aside dedicated time to work through the tutorials and complete the exercises.
-
Take notes and ask questions to clarify any concepts.
Solve machine learning practice problems
Show steps
Reinforce your understanding of machine learning concepts by applying them to practical exercises.
Show steps
-
Find practice problems online or in textbooks.
-
Attempt to solve the problems on your own.
-
Compare your solutions to provided answers or seek help from the course instructor or online forums.
Practice implementing machine learning algorithms
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Solidify your understanding of how machine learning algorithms work by implementing them yourself.
Browse courses on
Machine Learning Algorithms
Show steps
-
Choose a machine learning algorithm to implement
-
Gather and prepare your data
-
Implement the algorithm from scratch or use a library
-
Evaluate the performance of your implementation
Create a visual representation of a machine learning algorithm
Show steps
Enhance your understanding of machine learning algorithms by visualizing their steps and processes.
Show steps
-
Choose a machine learning algorithm to focus on.
-
Identify the key steps and processes involved in the algorithm.
-
Create a visual representation using tools such as diagrams, flowcharts, or mind maps.
Solve Practice Problems and Code Challenges
Show steps
Engage in regular practice by solving machine learning problems and completing code challenges to improve your problem-solving skills.
Browse courses on
Machine Learning Algorithms
Show steps
-
Join online coding platforms or forums to access practice problems.
-
Start with easier problems and gradually increase the difficulty level.
-
Break down complex problems into smaller steps.
-
Seek help from online communities or mentors when needed.
Join a study group or discussion forum for machine learning
Show steps
Engage with fellow learners to share knowledge, ask questions, and gain diverse perspectives on machine learning concepts.
Show steps
-
Identify or create a study group or online forum dedicated to machine learning.
-
Participate in discussions, ask questions, and share your insights.
-
Collaborate on projects or assignments to enhance your learning experience.
Join a machine learning study group or forum
Show steps
Connect with other learners and experts to discuss machine learning concepts, share knowledge, and get feedback on your work.
Show steps
-
Search for machine learning study groups or forums
-
Join a group or forum that aligns with your interests
-
Participate in discussions and ask questions
-
Share your knowledge and help others
Seek guidance from a mentor in the field of machine learning
Show steps
Accelerate your learning by connecting with experienced professionals who can provide personalized guidance and support.
Browse courses on
Mentoring
Show steps
-
Identify potential mentors who have expertise in machine learning.
-
Reach out to them and express your interest in mentorship.
-
Establish a regular communication channel for guidance and support.
Build a machine learning project
Show steps
Apply your machine learning skills to solve real-world problems and deepen your understanding through practical application.
Browse courses on
Machine Learning Projects
Show steps
-
Identify a problem that can be solved using machine learning
-
Gather and prepare your data
-
Choose and train a machine learning model
-
Deploy your model and evaluate its performance
Build a Simple Machine Learning Project
Show steps
Apply your knowledge by building a machine learning project from scratch, which will allow you to experience the end-to-end process.
Browse courses on
Machine Learning Projects
Show steps
-
Identify a problem or dataset that aligns with your interests.
-
Research and select appropriate machine learning algorithms.
-
Collect and preprocess the data.
-
Train and evaluate your model.
-
Deploy and monitor your project.
Build a small-scale machine learning model and present your findings
Show steps
Apply your knowledge and skills by developing a practical machine learning model and communicating your results effectively.
Show steps
-
Choose a specific problem or dataset to focus on.
-
Develop a machine learning model and train it on the data.
-
Evaluate the model's performance and present your findings in a clear and concise manner.
Contribute to an open-source machine learning project
Show steps
Deepen your understanding of machine learning by collaborating with others and contributing to the open-source community.
Show steps
-
Identify an open-source machine learning project that aligns with your interests.
-
Review the project's documentation and codebase.
-
Identify an area where you can contribute, such as bug fixes, feature additions, or documentation improvements.
Read a book on machine learning for beginners
Show steps
Expand your knowledge and understanding of machine learning fundamentals by exploring a beginner-friendly book.
View
Data Analytics & Visualization All-in-One For...
on Amazon
Show steps
Show steps
Show steps
- Acquire the book through purchase or borrowing.
- Set aside dedicated time for reading and comprehension.
- Take notes or highlight key concepts as you read.
Review machine learning basics
Show steps
Reinforce your understanding of the core concepts of machine learning, such as supervised and unsupervised learning, model training, and evaluation.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Go through your lecture notes and textbooks
- Review online tutorials and articles
- Work through practice problems and exercises
Review Math Concepts
Show steps
Review basic mathematical concepts such as algebra, trigonometry, and calculus to strengthen your foundation for machine learning.
Show steps
Show steps
Show steps
- Identify areas for improvement by taking a practice quiz or reviewing old notes.
- Focus on reviewing the specific mathematical concepts that are most relevant to machine learning.
- Use online resources, textbooks, or educational videos to refresh your understanding.
15 other activities
Expand to see all activities and additional details
Show all 18 activities
Review differential equations and Greek symbols
Show steps
Get ahead of the potential learning curve by reviewing essential mathematical concepts prior to starting the course.
Browse courses on
Differential Equations
Show steps
Show steps
Show steps
- Review your notes from previous mathematics courses.
- Seek out online resources or textbooks to supplement your understanding.
- Take practice quizzes to test your comprehension.
Follow a tutorial on machine learning basics
Show steps
Supplement the course material by exploring additional resources that provide a gentle introduction to machine learning concepts.
Show steps
Show steps
Show steps
- Identify reputable sources for tutorials, such as online courses or YouTube channels.
- Choose a tutorial that aligns with your current understanding and interests.
- Follow the tutorial step-by-step and take notes of key concepts.
Follow online tutorials on machine learning
Show steps
Expand your knowledge of machine learning by following structured tutorials and courses.
Browse courses on
Online Courses
Show steps
Show steps
Show steps
- Search for online tutorials or courses on machine learning
- Select a tutorial or course that aligns with your learning goals
- Follow the instructions and complete the exercises
- Apply what you learn to your own projects
Follow Guided Tutorials on Machine Learning Fundamentals
Show steps
Supplement your coursework with guided tutorials to reinforce your understanding of foundational machine learning principles.
Browse courses on
Machine Learning Fundamentals
Show steps
Show steps
Show steps
- Identify reputable online platforms or courses that offer beginner-friendly tutorials.
- Set aside dedicated time to work through the tutorials and complete the exercises.
- Take notes and ask questions to clarify any concepts.
Solve machine learning practice problems
Show steps
Reinforce your understanding of machine learning concepts by applying them to practical exercises.
Show steps
Show steps
Show steps
- Find practice problems online or in textbooks.
- Attempt to solve the problems on your own.
- Compare your solutions to provided answers or seek help from the course instructor or online forums.
Practice implementing machine learning algorithms
Show steps
Solidify your understanding of how machine learning algorithms work by implementing them yourself.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Choose a machine learning algorithm to implement
- Gather and prepare your data
- Implement the algorithm from scratch or use a library
- Evaluate the performance of your implementation
Create a visual representation of a machine learning algorithm
Show steps
Enhance your understanding of machine learning algorithms by visualizing their steps and processes.
Show steps
Show steps
Show steps
- Choose a machine learning algorithm to focus on.
- Identify the key steps and processes involved in the algorithm.
- Create a visual representation using tools such as diagrams, flowcharts, or mind maps.
Solve Practice Problems and Code Challenges
Show steps
Engage in regular practice by solving machine learning problems and completing code challenges to improve your problem-solving skills.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Join online coding platforms or forums to access practice problems.
- Start with easier problems and gradually increase the difficulty level.
- Break down complex problems into smaller steps.
- Seek help from online communities or mentors when needed.
Join a study group or discussion forum for machine learning
Show steps
Engage with fellow learners to share knowledge, ask questions, and gain diverse perspectives on machine learning concepts.
Show steps
Show steps
Show steps
- Identify or create a study group or online forum dedicated to machine learning.
- Participate in discussions, ask questions, and share your insights.
- Collaborate on projects or assignments to enhance your learning experience.
Join a machine learning study group or forum
Show steps
Connect with other learners and experts to discuss machine learning concepts, share knowledge, and get feedback on your work.
Show steps
Show steps
Show steps
- Search for machine learning study groups or forums
- Join a group or forum that aligns with your interests
- Participate in discussions and ask questions
- Share your knowledge and help others
Seek guidance from a mentor in the field of machine learning
Show steps
Accelerate your learning by connecting with experienced professionals who can provide personalized guidance and support.
Browse courses on
Mentoring
Show steps
Show steps
Show steps
- Identify potential mentors who have expertise in machine learning.
- Reach out to them and express your interest in mentorship.
- Establish a regular communication channel for guidance and support.
Build a machine learning project
Show steps
Apply your machine learning skills to solve real-world problems and deepen your understanding through practical application.
Browse courses on
Machine Learning Projects
Show steps
Show steps
Show steps
- Identify a problem that can be solved using machine learning
- Gather and prepare your data
- Choose and train a machine learning model
- Deploy your model and evaluate its performance
Build a Simple Machine Learning Project
Show steps
Apply your knowledge by building a machine learning project from scratch, which will allow you to experience the end-to-end process.
Browse courses on
Machine Learning Projects
Show steps
Show steps
Show steps
- Identify a problem or dataset that aligns with your interests.
- Research and select appropriate machine learning algorithms.
- Collect and preprocess the data.
- Train and evaluate your model.
- Deploy and monitor your project.
Build a small-scale machine learning model and present your findings
Show steps
Apply your knowledge and skills by developing a practical machine learning model and communicating your results effectively.
Show steps
Show steps
Show steps
- Choose a specific problem or dataset to focus on.
- Develop a machine learning model and train it on the data.
- Evaluate the model's performance and present your findings in a clear and concise manner.
Contribute to an open-source machine learning project
Show steps
Deepen your understanding of machine learning by collaborating with others and contributing to the open-source community.
Show steps
Show steps
Show steps
- Identify an open-source machine learning project that aligns with your interests.
- Review the project's documentation and codebase.
- Identify an area where you can contribute, such as bug fixes, feature additions, or documentation improvements.
Career center
Learners who complete Introduction to Machine Learning will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist takes raw data and transforms it into usable knowledge and insights. Their work has been critical for advancements in machine learning and data analysis. Relatedly, an understanding of machine learning principles is essential for modern Data Scientists. This course helps establish a strong foundation in machine learning and will prove helpful to aspiring Data Scientists.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer applies machine learning principles to solve real-world problems. This course introduces several common machine learning algorithms and discusses real-world applications for each. It also includes discussion of relevant tools and resources. As such, this course may be useful to those interested in a career as a Machine Learning Engineer.
Data Analyst
Data Analyst
A Data Analyst cleans and organizes data, and may also analyze it and present findings to stakeholders. Machine learning has become an essential tool for data analysis and visualization and is often used in a Data Analyst's work. Therefore, this foundational course in machine learning could be helpful to aspiring Data Analysts.
Software Engineer
Software Engineer
A Software Engineer designs, builds, and tests software systems. Machine learning is increasingly incorporated into software solutions, meaning that foundational knowledge of the principles and applications of machine learning is essential for Software Engineers in this day and age. This course helps establish that foundation.
Data Engineer
Data Engineer
A Data Engineer prepares and processes large sets of data. For that data to be usable for machine learning and other purposes, the Data Engineer must be familiar with machine learning principles. This course offers an excellent foundation. Previous exposure to concepts such as regression and deep learning is not required.
Statistician
Statistician
A Statistician collects, analyzes, interprets, and presents data. While statistics and machine learning are distinct fields, they intersect in many ways. As a result, machine learning knowledge is indispensable for many statisticians. This beginner-friendly course lays the groundwork for those interested in exploring both fields.
Research Scientist
Research Scientist
A Research Scientist conducts research and develops new technologies. Many Research Scientists work in fields such as machine learning and artificial intelligence. For those with advanced degrees, this course is an excellent primer on the fundamentals of machine learning.
Business Analyst
Business Analyst
A Business Analyst analyzes an organization's needs and recommends solutions to business problems. Machine learning is quickly revolutionizing the way business problems are solved. As a result, an understanding of machine learning has become beneficial for Business Analysts. This course is a solid introduction to machine learning concepts for Business Analysts.
Financial Analyst
Financial Analyst
A Financial Analyst makes investment recommendations. Machine learning is beginning to make an impact on investment decisions. Accordingly, Financial Analysts with machine learning knowledge are likely to see career benefits. This course will be useful to Financial Analysts who want to keep up with the times.
Consultant
Consultant
A Consultant advises businesses on how to improve performance. Increasingly, Consultants are working with businesses to implement machine learning solutions. Aspiring Consultants with machine learning knowledge will be highly sought-after.
Product Manager
Product Manager
A Product Manager develops and manages products. As machine learning is increasingly incorporated into products, Product Managers will need to be familiar with the basics of machine learning. This course provides a foundation for Product Managers to build on.
Marketing Manager
Marketing Manager
A Marketing Manager promotes products and services. Machine learning tools are becoming more common in marketing, from data analysis to content creation. A basic understanding of machine learning, such as the one this course offers, will help Marketing Managers stand out from the competition.
Sales Manager
Sales Manager
A Sales Manager leads a sales team. Sales Managers with knowledge of machine learning are better equipped to understand and use data to improve sales performance. This introductory course in machine learning is an excellent place to start.
Systems Analyst
Systems Analyst
A Systems Analyst designs, implements, and maintains computer systems. Many organizations are moving to cloud-based systems that incorporate machine learning for data processing, security, and more. Systems Analysts with machine learning knowledge are in high demand.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst develops and implements mathematical models and techniques to solve real-world problems, often for businesses. Machine learning is an important tool for Operations Research Analysts. This beginner-friendly course will be helpful to Operations Research Analysts with limited machine learning experience.
For more career information including salaries, visit:
OpenCourser.com/course/ck7dyy/introduction
Reading list
We've selected 14 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Introduction to Machine Learning.
Provides hands-on experience with popular machine learning libraries such as Scikit-Learn, Keras, and TensorFlow. It offers practical examples and exercises, making it a valuable resource for learners who want to apply machine learning techniques to real-world problems.
Provides a clear and concise introduction to machine learning concepts and algorithms, using Python as the programming language. It covers a wide range of topics, from supervised and unsupervised learning to deep learning and reinforcement learning.
Provides a comprehensive overview of machine learning from a probabilistic perspective. It covers a wide range of topics, from Bayesian inference to graphical models and deep learning. This book is suitable for learners with a strong background in mathematics and statistics.
Save
Provides a comprehensive overview of deep learning concepts and algorithms. It covers a wide range of topics, from convolutional neural networks to recurrent neural networks and generative adversarial networks. This book is suitable for learners with a strong background in mathematics and computer science.
Provides a comprehensive overview of statistical learning methods with a focus on sparsity. It covers a wide range of topics, from the lasso to the elastic net and group lasso. This book is suitable for learners with a strong background in mathematics and statistics.
Provides a practical introduction to machine learning for non-experts. It covers a wide range of topics, from data preparation to model evaluation and deployment. This book is suitable for learners with little or no prior knowledge of machine learning.
Provides a comprehensive overview of statistical learning methods, with a focus on data mining, inference, and prediction. It covers a wide range of topics, from linear regression to tree-based methods and support vector machines.
Provides a comprehensive overview of pattern recognition and machine learning. It covers a wide range of topics, from Bayesian inference to graphical models and deep learning.
Provides a comprehensive overview of linear algebra, with a focus on applications in machine learning and data science. It covers a wide range of topics, from matrix theory to optimization and statistical learning.
Provides a comprehensive overview of convex optimization, with a focus on applications in machine learning and data science. It covers a wide range of topics, from linear programming to semidefinite programming and distributed optimization.
Provides a comprehensive overview of information theory, inference, and learning algorithms. It covers a wide range of topics, from entropy and mutual information to Bayesian inference and reinforcement learning.
Provides a comprehensive overview of reinforcement learning, with a focus on the theoretical foundations of the field. It covers a wide range of topics, from Markov decision processes to temporal difference learning and deep reinforcement learning.
Provides a comprehensive overview of natural language processing, with a focus on Python as the programming language. It covers a wide range of topics, from text classification to machine translation and information retrieval.
Save
Provides a comprehensive overview of computer vision, with a focus on algorithms and applications. It covers a wide range of topics, from image formation to object recognition and scene understanding.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ck7dyy/introduction
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Time
240 hours
Level
Introductory
Via
Pluralsight
Instructor
A Cloud Guru
Language
English
Good to know
Outlines the difference between related but distinct fields such as data science and artificial intelligence
Utilizes a conversational and engaging tone, making complex machine learning concepts more accessible
Applicable both in industry and academia, providing real-world examples to enhance understanding
Avoids using technical jargon and Greek symbols, catering specifically to beginners in machine learning
Designed for those with limited mathematical backgrounds, focusing on intuitive explanations and practical applications
Covers a broad range of machine learning topics, including regression, classification, and decision trees
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