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Swetha Kolalapudi
If you don't know the question, you probably won't get the answer right. This course is all about asking the right machine learning questions, modeling real-world situations as one of several well understood machine learning problems.
Read more
If you don't know the question, you probably won't get the answer right. This course is all about asking the right machine learning questions, modeling real-world situations as one of several well understood machine learning problems.
Read more
If you don't know the question, you probably won't get the answer right. This course is all about asking the right machine learning questions, modeling real-world situations as one of several well understood machine learning problems.
Machine learning is behind some of the coolest technological innovations today, Contrary to popular perception, however, you don't need to be a math genius to successfully apply machine learning. As a data scientist facing any real-world problem, you first need to identify whether machine learning can provide an appropriate solution. In this course, How to Think About Machine Learning Algorithms, you'll learn how to identify those situations. First, you will learn how to determine which of the four basic approaches you'll take to solve the problem: classification, regression, clustering or recommendation. Next, you'll learn how to set up the problem statement, features, and labels. Finally you'll plug in a standard algorithm to solve the problem. At the end of this course, you'll have the skills and knowledge required to recognize an opportunity for a machine learning application and seize it.
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What's inside
Syllabus
Course Overview
Introducing Machine Learning
Classifying Data into Predefined Categories
Solving Classification Problems
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Syllabus
Course Overview
Introducing Machine Learning
Classifying Data into Predefined Categories
Solving Classification Problems
Read more
Predicting Relationships between Variables with Regression
Solving Regression Problems
Recommending Relevant Products to a User
Clustering Large Data Sets into Meaningful Groups
Wrapping up and Next Steps
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Identifies the most appropriate machine learning approach for the problem presented
Offers a clear and concise overview of machine learning, its applications, and the different types of machine learning problems
Provides a structured approach to problem-solving using machine learning, emphasizing the importance of problem formulation and feature engineering
Covers various types of machine learning algorithms, including classification, regression, clustering, and recommendation systems
Emphasizes the practical application of machine learning and provides real-world examples and case studies
Led by a knowledgeable instructor with expertise in machine learning and data science
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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 How to Think About Machine Learning Algorithms with these
activities:
Review Linear Algebra Concepts
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Linear algebra is a prerequisite for this course. Reviewing its key concepts will help you succeed from day one.
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Linear Algebra
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-
Go over your notes or a textbook to refresh your memory on the basics of linear algebra.
-
Solve a few practice problems or take a quiz to test your understanding.
Create a Notebook of Course Notes and Assignments
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Organizing your notes and assignments in one place will help you stay organized and make reviewing easier.
Show steps
-
Create a notebook or digital folder for the course.
-
Regularly add notes, assignments, and any other relevant materials to the notebook.
Try out Beginner Guide to NumPy Tutorial
Show steps
Follow tutorials to understand the basic operations and data structures of NumPy to prepare for when we cover these in class.
Show steps
-
Visit the official Beginners Guide to NumPy page.
-
Follow the tutorial in the Quickstart section.
-
Check the Concepts section for more advanced concepts.
Five other activities
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Show all eight activities
Review 'Python Machine Learning' by Raschka and Mirjalili
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This book provides a comprehensive overview of machine learning concepts and techniques using Python. Reviewing it will enhance your understanding of the topics covered in this course.
View
Machine Learning with PyTorch and Scikit-Learn:...
on Amazon
Show steps
-
Read the selected chapters relevant to the course.
-
Take notes and highlight key concepts.
-
Complete the practice exercises at the end of each chapter.
Practice List Comprehensions
Show steps
List comprehensions are a key part of Python. This activity helps you practice manipulating data to get more comfortable using them.
Show steps
-
Find a list comprehension example online or in the course materials.
-
Write out the for loop equivalent of the list comprehension.
-
Compare the two and note the similarities and differences.
Write a Summary of Machine Learning Algorithms
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Summarizing the concepts will help you retain the information and improve your understanding of various machine learning algorithms.
Browse courses on
Machine Learning Algorithms
Show steps
-
Review the different types of machine learning algorithms covered in the course.
-
Write a summary of each algorithm, including its purpose, advantages, and disadvantages.
Implement K-Means Clustering
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K-Means Clustering is an important unsupervised learning algorithm. This activity will help you implement it from scratch to gain a deeper understanding.
Browse courses on
K-Means Clustering
Show steps
-
Review the K-Means Clustering algorithm.
-
Choose a dataset and preprocess it.
-
Implement the K-Means Clustering algorithm in Python.
-
Evaluate the performance of your implementation.
Build a Simple Machine Learning Model
Show steps
Hands-on experience is crucial in solidifying your understanding. This project will allow you to apply the concepts learned in the course.
Show steps
-
Choose a simple dataset and a machine learning algorithm that you want to use.
-
Implement the algorithm in Python and train the model.
-
Evaluate the model's performance and make any necessary adjustments.
-
Write a report summarizing your project.
Review Linear Algebra Concepts
Show steps
Linear algebra is a prerequisite for this course. Reviewing its key concepts will help you succeed from day one.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Go over your notes or a textbook to refresh your memory on the basics of linear algebra.
- Solve a few practice problems or take a quiz to test your understanding.
Create a Notebook of Course Notes and Assignments
Show steps
Organizing your notes and assignments in one place will help you stay organized and make reviewing easier.
Show steps
Show steps
Show steps
- Create a notebook or digital folder for the course.
- Regularly add notes, assignments, and any other relevant materials to the notebook.
Try out Beginner Guide to NumPy Tutorial
Show steps
Follow tutorials to understand the basic operations and data structures of NumPy to prepare for when we cover these in class.
Show steps
Show steps
Show steps
- Visit the official Beginners Guide to NumPy page.
- Follow the tutorial in the Quickstart section.
- Check the Concepts section for more advanced concepts.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Review 'Python Machine Learning' by Raschka and Mirjalili
Show steps
This book provides a comprehensive overview of machine learning concepts and techniques using Python. Reviewing it will enhance your understanding of the topics covered in this course.
View
Machine Learning with PyTorch and Scikit-Learn:...
on Amazon
Show steps
Show steps
Show steps
- Read the selected chapters relevant to the course.
- Take notes and highlight key concepts.
- Complete the practice exercises at the end of each chapter.
Practice List Comprehensions
Show steps
List comprehensions are a key part of Python. This activity helps you practice manipulating data to get more comfortable using them.
Show steps
Show steps
Show steps
- Find a list comprehension example online or in the course materials.
- Write out the for loop equivalent of the list comprehension.
- Compare the two and note the similarities and differences.
Write a Summary of Machine Learning Algorithms
Show steps
Summarizing the concepts will help you retain the information and improve your understanding of various machine learning algorithms.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Review the different types of machine learning algorithms covered in the course.
- Write a summary of each algorithm, including its purpose, advantages, and disadvantages.
Implement K-Means Clustering
Show steps
K-Means Clustering is an important unsupervised learning algorithm. This activity will help you implement it from scratch to gain a deeper understanding.
Browse courses on
K-Means Clustering
Show steps
Show steps
Show steps
- Review the K-Means Clustering algorithm.
- Choose a dataset and preprocess it.
- Implement the K-Means Clustering algorithm in Python.
- Evaluate the performance of your implementation.
Build a Simple Machine Learning Model
Show steps
Hands-on experience is crucial in solidifying your understanding. This project will allow you to apply the concepts learned in the course.
Show steps
Show steps
Show steps
- Choose a simple dataset and a machine learning algorithm that you want to use.
- Implement the algorithm in Python and train the model.
- Evaluate the model's performance and make any necessary adjustments.
- Write a report summarizing your project.
Career center
Learners who complete How to Think About Machine Learning Algorithms will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data scientists play a key role in the field of machine learning by leveraging data to create new products and services. This course provides the foundational knowledge data scientists use every day. You will become proficient in key methods for supervised learning, unsupervised learning, and model selection. Take this course and learn the skills and knowledge needed to succeed in data science.
Statistician
Statistician
Statisticians use data to solve a wide range of problems, from predicting election outcomes to evaluating the effectiveness of medical treatments. This course covers core statistical modeling and analysis techniques, and how to apply these techniques using coding. Take this course to build the foundation in statistics useful in a variety of fields.
Machine Learning Engineer
Machine Learning Engineer
Machine learning engineers build and maintain machine learning systems. This course provides a foundation in machine learning that is essential for this role. You will learn to formulate problems as machine learning problems, and gain hands-on experience building and evaluating models. Machine learning engineers typically need an advanced degree but this course would be a foundational introduction to the field.
Data Analyst
Data Analyst
Data analysts collect, clean, and analyze data to help businesses make informed decisions. This course covers the core principles of data analytics, including data visualization, statistical analysis, and machine learning. With this course, you can start or advance your career in data analytics.
Software Engineer
Software Engineer
Software engineers design, develop, and maintain software systems. While not a data-focused role, this course can provide valuable foundational knowledge for software engineers working with machine learning models. You will learn how to select and implement machine learning algorithms, and gain experience working with data. This knowledge may be particularly useful for software engineers who work on developing data-driven applications or integrating machine learning into existing systems.
Financial Analyst
Financial Analyst
Financial analysts use data to make informed investment recommendations. This course provides a solid foundation in machine learning that can be applied to financial data. You will learn how to use machine learning to identify trends, predict future performance, and make better investment decisions. Financial analysts typically need an advanced degree in a quantitative field, but this course would be a useful introduction to machine learning.
Marketing Manager
Marketing Manager
Marketing managers develop and execute marketing campaigns to promote products and services. This course covers the fundamentals of machine learning, which can be used to improve marketing campaigns. You will learn how to use machine learning to segment customers, personalize marketing messages, and measure campaign effectiveness. Marketing managers typically need a bachelor's degree in marketing or a related field, but this course would be a helpful introduction to machine learning.
Product Manager
Product Manager
Product managers oversee the development and launch of new products and services. This course covers the basics of machine learning, which can be used to improve product development. You will learn how to use machine learning to identify customer needs, develop product features, and test product prototypes. Product managers typically need a bachelor's degree in engineering or a related field, but this course would be a helpful introduction to machine learning.
Operations Research Analyst
Operations Research Analyst
Operations research analysts use data to improve the efficiency of business processes. This course provides a foundation in machine learning that can be applied to operations research problems. You will learn how to use machine learning to optimize schedules, allocate resources, and make better decisions. Operations research analysts typically need an advanced degree in operations research or a related field, but this course would be a useful introduction to machine learning.
Business Analyst
Business Analyst
Business analysts use data to help businesses make better decisions. This course provides a foundation in machine learning that can be applied to business problems. You will learn how to use machine learning to identify trends, predict future performance, and make better decisions. Business analysts typically need a bachelor's degree in business or a related field, but this course would be a helpful introduction to machine learning.
Consultant
Consultant
Consultants provide advice to businesses on a variety of topics, including machine learning. This course provides a foundation in machine learning that can be useful for consultants working with clients on machine learning projects. You will learn how to identify machine learning opportunities, develop machine learning solutions, and communicate machine learning results. Consultants typically need a bachelor's degree in business or a related field, but this course would be a helpful introduction to machine learning.
Quantitative Analyst
Quantitative Analyst
Quantitative analysts use data to make investment decisions. This course provides a foundation in machine learning that can be applied to financial data. You will learn how to use machine learning to identify trends, predict future performance, and make better investment decisions. Quantitative analysts typically need an advanced degree in a quantitative field, but this course would be a useful introduction to machine learning.
Risk Manager
Risk Manager
Risk managers identify and manage risks to businesses. This course provides a foundation in machine learning that can be applied to risk management problems. You will learn how to use machine learning to identify risks, assess the likelihood and impact of risks, and develop strategies to mitigate risks. Risk managers typically need a bachelor's degree in business or a related field, but this course would be a helpful introduction to machine learning.
Actuary
Actuary
Actuaries use data to assess and manage financial risks. This course provides a foundation in machine learning that can be applied to actuarial problems. You will learn how to use machine learning to model risk, predict future events, and make better decisions. Actuaries typically need an advanced degree in mathematics or a related field, but this course would be a useful introduction to machine learning.
Economist
Economist
Economists study the production, distribution, and consumption of goods and services. This course provides a foundation in machine learning that can be applied to economic problems. You will learn how to use machine learning to model economic data, predict economic trends, and make better economic decisions. Economists typically need an advanced degree in economics or a related field, but this course would be a helpful introduction to machine learning.
For more career information including salaries, visit:
OpenCourser.com/course/yhfbfw/how
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
How to Think About Machine Learning Algorithms.
Provides a practical introduction to machine learning using popular Python libraries such as Scikit-Learn, Keras, and TensorFlow. It offers hands-on examples and exercises to help readers understand the concepts and apply them to real-world problems.
Save
Comprehensive reference on deep learning, covering the latest research and techniques. It valuable resource for researchers and practitioners who want to stay up-to-date on the latest developments in this field.
Provides a probabilistic perspective on machine learning, focusing on the underlying mathematical principles. It valuable resource for researchers and practitioners who want to gain a deeper understanding of the theoretical foundations of machine learning.
Save
Provides a comprehensive introduction to pattern recognition and machine learning, covering a wide range of topics, including supervised and unsupervised learning, statistical learning theory, and neural networks.
Provides a practical introduction to machine learning using the R programming language. It offers hands-on examples and exercises to help readers understand the concepts and apply them to real-world problems.
Provides a practical introduction to machine learning for programmers and data scientists. It focuses on hands-on examples and exercises to help readers learn the basics of machine learning and apply them to real-world problems.
Provides a comprehensive introduction to machine learning, covering a wide range of topics, including supervised and unsupervised learning, feature engineering, and model evaluation.
Provides a practical introduction to machine learning using Python. It offers hands-on examples and exercises to help readers understand the concepts and apply them to real-world problems.
Provides a comprehensive introduction to artificial intelligence, covering a wide range of topics, including machine learning, natural language processing, and computer vision.
Provides a comprehensive introduction to machine learning algorithms, covering a wide range of topics, including supervised and unsupervised learning, statistical learning theory, and neural networks.
Provides a practical introduction to machine learning for data analysts and data scientists. It focuses on hands-on examples and exercises to help readers understand the concepts and apply them to real-world problems.
Provides a concise introduction to machine learning, covering the основные concepts and algorithms. It valuable resource for beginners who want to gain a basic understanding of machine learning.
Is not recommended for supplemental reading because it is too basic for this course, which is targeted to data scientists.
Is not recommended for supplemental reading because it is too basic for this course, which is targeted to data scientists.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/yhfbfw/how
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Time
188 hours
Level
Introductory
Via
Pluralsight
Instructor
Swetha Kolalapudi
Language
English
Good to know
Identifies the most appropriate machine learning approach for the problem presented
Offers a clear and concise overview of machine learning, its applications, and the different types of machine learning problems
Provides a structured approach to problem-solving using machine learning, emphasizing the importance of problem formulation and feature engineering
Covers various types of machine learning algorithms, including classification, regression, clustering, and recommendation systems
Emphasizes the practical application of machine learning and provides real-world examples and case studies
Led by a knowledgeable instructor with expertise in machine learning and data science
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