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Sabrina Moore,
Rajvir Dua,
and
Neelesh Tiruviluamala
This course is aimed at anyone interested in applying machine learning techniques to scientific problems. In this course, we'll learn about the complete machine learning pipeline, from reading in, cleaning, and transforming data to running basic and advanced machine learning algorithms. We'll start with data preprocessing techniques, such as PCA and LDA. Then, we'll dive into the fundamental AI algorithms: SVMs and K-means clustering. Along the way, we'll build our mathematical and programming toolbox to prepare ourselves to work with more complicated models. Finally, we'll explored advanced methods such as random forests and neural networks. Throughout the way, we'll be using medical and astronomical datasets. In the final project, we'll apply our skills to compare different machine learning models in Python.
Register for this course and see more details by visiting:
OpenCourser.com/course/ubnzyi/machine
What's inside
Syllabus
Before the AI: Preparing and Preprocessing Data
In this module, we'll tackle the steps taken before we can use AI algorithms. We'll start with an introduction to the most prominent data preprocessing techniques including filling in missing values and removing outliers. Then we'll dive into data transformations including PCA and LDA, two methods featured heavily for dimensionality reduction. Finally, we'll learn how to code the algorithms in Python to set up your data for use in the next module.
Read more
Syllabus
Before the AI: Preparing and Preprocessing Data
In this module, we'll tackle the steps taken before we can use AI algorithms. We'll start with an introduction to the most prominent data preprocessing techniques including filling in missing values and removing outliers. Then we'll dive into data transformations including PCA and LDA, two methods featured heavily for dimensionality reduction. Finally, we'll learn how to code the algorithms in Python to set up your data for use in the next module.
Read more
Foundational AI Algorithms: K-Means and SVM
In this module, we'll dive into two of the most foundational machine learning algorithms: K-Means and support vector machines. We'll start by comparing the two branches of ML: supervised and unsupervised learning. Then, we'll go into the specific similarities and differences between K-Nearest neighbors for classification and K-Means clustering. Finally, we'll perform deep dives into K-Means and SVMs, learning the basic theory behind them and how to implement each in Python.
Advanced AI: Neural Networks and Decision Trees
In this module, we'll explore some advanced AI techniques. We'll start with tree-based algorithms, made popular because of the use of random forests for both classification and regression. Then, we'll build our way to neural networks, starting from experimentation on the different models. We'll spend some time in the Tensorflow playground getting familiar with the different mechanics behind neural networks. Finally, we'll code our own neural networks to make predictions on unseen data.
Course Project
In this module, we'll go through a course project to predict diabetes from health data. We'll compare different regressors by implementing them and checking the error on a test set.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Suitable for individuals interested in applying machine learning techniques to scientific problems
Covers essential machine learning pipeline, from data preprocessing to running algorithms
Employs medical and astronomical datasets, providing practical applications
Led by instructors with expertise in machine learning and data science
Leverages Python for practical implementation and coding exercises
Provides a comprehensive overview of machine learning techniques and algorithms
Save this course
Save Machine Learning Models in Science to your list so you can find it easily later:
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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 Machine Learning Models in Science with these
activities:
Review Python programming basics
Show steps
Refresh your understanding of Python programming basics, including data types, variables, and control flow.
Browse courses on
Python Programming
Show steps
-
Review online tutorials on Python basics
-
Solve coding problems on platforms like LeetCode or HackerRank
Participate in peer study groups
Show steps
Sharpen your understanding and critical thinking by discussing course concepts with peers in study groups.
Show steps
-
Find or create a peer study group
-
Discuss course topics, share insights, and solve problems together
Solve practice problems on machine learning concepts
Show steps
Reinforce your understanding of machine learning concepts by solving practice problems.
Show steps
-
Find practice problems on platforms like Kaggle or Coursera
-
Solve these problems using your knowledge of machine learning algorithms
Three other activities
Expand to see all activities and additional details
Show all six activities
Follow tutorials on machine learning algorithms in Python
Show steps
Deepen your understanding of machine learning algorithms by following guided tutorials and implementing them in Python.
Browse courses on
Machine Learning Algorithms
Show steps
-
Find tutorials on supervised and unsupervised machine learning algorithms
-
Implement these algorithms in Python using libraries like scikit-learn
-
Experiment with different datasets and evaluate the performance
Create a machine learning project portfolio
Show steps
Demonstrate your skills by creating a portfolio of machine learning projects that showcase your abilities.
Browse courses on
Machine Learning Projects
Show steps
-
Identify real-world problems that can be solved using machine learning
-
Develop and implement machine learning solutions for these problems
-
Document your projects and showcase your results
Contribute to open-source machine learning projects
Show steps
Enhance your skills and gain real-world experience by contributing to open-source machine learning projects.
Browse courses on
Open-Source
Show steps
-
Find open-source machine learning projects on platforms like GitHub
-
Identify areas where you can contribute based on your skills
-
Submit pull requests with your contributions
Review Python programming basics
Show steps
Refresh your understanding of Python programming basics, including data types, variables, and control flow.
Browse courses on
Python Programming
Show steps
Show steps
Show steps
- Review online tutorials on Python basics
- Solve coding problems on platforms like LeetCode or HackerRank
Participate in peer study groups
Show steps
Sharpen your understanding and critical thinking by discussing course concepts with peers in study groups.
Show steps
Show steps
Show steps
- Find or create a peer study group
- Discuss course topics, share insights, and solve problems together
Solve practice problems on machine learning concepts
Show steps
Reinforce your understanding of machine learning concepts by solving practice problems.
Show steps
Show steps
Show steps
- Find practice problems on platforms like Kaggle or Coursera
- Solve these problems using your knowledge of machine learning algorithms
Three other activities
Expand to see all activities and additional details
Show all six activities
Follow tutorials on machine learning algorithms in Python
Show steps
Deepen your understanding of machine learning algorithms by following guided tutorials and implementing them in Python.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Find tutorials on supervised and unsupervised machine learning algorithms
- Implement these algorithms in Python using libraries like scikit-learn
- Experiment with different datasets and evaluate the performance
Create a machine learning project portfolio
Show steps
Demonstrate your skills by creating a portfolio of machine learning projects that showcase your abilities.
Browse courses on
Machine Learning Projects
Show steps
Show steps
Show steps
- Identify real-world problems that can be solved using machine learning
- Develop and implement machine learning solutions for these problems
- Document your projects and showcase your results
Contribute to open-source machine learning projects
Show steps
Enhance your skills and gain real-world experience by contributing to open-source machine learning projects.
Browse courses on
Open-Source
Show steps
Show steps
Show steps
- Find open-source machine learning projects on platforms like GitHub
- Identify areas where you can contribute based on your skills
- Submit pull requests with your contributions
Career center
Learners who complete Machine Learning Models in Science will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and implement machine learning models and algorithms to solve real-world problems. This course, Machine Learning Models in Science, aligns perfectly with the core responsibilities of a Machine Learning Engineer. The course covers essential concepts such as data preprocessing, foundational AI algorithms, and advanced AI techniques. By completing this course, individuals may gain the knowledge and skills needed to pursue a career as a Machine Learning Engineer.
Machine Learning Researcher
Machine Learning Researcher
Machine Learning Researchers conduct research to advance the field of machine learning. This course, Machine Learning Models in Science, aligns perfectly with the goals of Machine Learning Researchers seeking to stay at the forefront of their field. The course covers essential concepts such as data preprocessing, foundational AI algorithms, and advanced AI techniques, which are critical for conducting cutting-edge research in machine learning. By taking this course, Machine Learning Researchers may gain the skills necessary to make significant contributions to the field.
Research Scientist
Research Scientist
Research Scientists conduct scientific research to advance knowledge and understanding in various fields. This course, Machine Learning Models in Science, aligns perfectly with the goals of Research Scientists seeking to leverage machine learning in their research. The course covers essential concepts such as data preprocessing, foundational AI algorithms, and advanced AI techniques, which are critical for conducting cutting-edge research. By taking this course, Research Scientists may gain the skills necessary to stay at the forefront of their fields.
Data Scientist
Data Scientist
Data Scientists use their expertise in statistics, programming, and machine learning to extract insights from data. This course, Machine Learning Models in Science, provides a solid foundation for those aspiring to become Data Scientists. The course covers data preprocessing techniques, foundational AI algorithms, and advanced AI techniques, which are essential skills for Data Scientists. By taking this course, individuals may enhance their ability to succeed in the field of Data Science.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data to help organizations make informed decisions. This course, Machine Learning Models in Science, can be a valuable resource for Statisticians seeking to expand their knowledge of machine learning techniques. The course covers data preprocessing techniques, foundational AI algorithms, and advanced AI techniques, which are becoming increasingly important in modern statistical analysis. By taking this course, Statisticians may enhance their ability to extract meaningful insights from data.
Software Engineer
Software Engineer
Software Engineers apply engineering principles to the design, development, and maintenance of software systems. This course, Machine Learning Models in Science, can be a valuable asset for Software Engineers seeking to specialize in machine learning. The course provides a comprehensive overview of data preprocessing techniques, foundational AI algorithms, and advanced AI techniques, which are becoming increasingly important in modern software development. By completing this course, Software Engineers may expand their skillset and increase their marketability in the tech industry.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines that enable organizations to collect, store, and process large volumes of data. This course, Machine Learning Models in Science, may be beneficial for Data Engineers seeking to enhance their understanding of machine learning techniques. The course covers data preprocessing techniques, foundational AI algorithms, and advanced AI techniques, which are becoming increasingly important in modern data engineering practices. By taking this course, Data Engineers may gain the skills needed to develop and manage robust data pipelines.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze and predict financial trends. This course, Machine Learning Models in Science, may provide Quantitative Analysts with a deeper understanding of machine learning techniques and their applications in finance. The course covers data preprocessing techniques, foundational AI algorithms, and advanced AI techniques, which are becoming increasingly important in modern quantitative analysis. By taking this course, Quantitative Analysts may gain the skills needed to develop and implement sophisticated financial models.
Health Data Analyst
Health Data Analyst
Health Data Analysts use their expertise in data analysis and healthcare to improve patient outcomes. This course, Machine Learning Models in Science, may be beneficial for Health Data Analysts seeking to enhance their understanding of machine learning techniques in healthcare. The course covers data preprocessing techniques, foundational AI algorithms, and advanced AI techniques, which are becoming increasingly important in modern healthcare data analysis. By taking this course, Health Data Analysts may gain the skills needed to develop and implement innovative solutions to improve patient care.
Data Architect
Data Architect
Data Architects design and manage data architectures that meet the needs of organizations. This course, Machine Learning Models in Science, may provide Data Architects with valuable insights into the application of machine learning techniques in data architecture. The course covers data preprocessing techniques, foundational AI algorithms, and advanced AI techniques, which are becoming increasingly important in modern data architecture practices. By taking this course, Data Architects may gain the skills needed to develop and implement scalable and efficient data architectures.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk and uncertainty in various fields. This course, Machine Learning Models in Science, may be beneficial for Actuaries seeking to enhance their understanding of machine learning techniques. The course covers data preprocessing techniques, foundational AI algorithms, and advanced AI techniques, which are becoming increasingly important in modern actuarial science. By taking this course, Actuaries may gain the skills needed to develop and implement robust risk assessment models.
Data Analyst
Data Analyst
A Data Analyst translates raw data into meaningful insights that help businesses make informed decisions. This course, Machine Learning Models in Science, can be a valuable stepping stone for those seeking a career in Data Analysis. Through the exploration of data preprocessing techniques, foundational AI algorithms, advanced AI techniques, and a course project, this course may equip aspiring Data Analysts with the skills necessary to excel in the field.
Financial Analyst
Financial Analyst
Financial Analysts use their expertise in finance and data analysis to make investment recommendations. This course, Machine Learning Models in Science, may provide Financial Analysts with valuable insights into the application of machine learning techniques in finance. The course covers data preprocessing techniques, foundational AI algorithms, and advanced AI techniques, which are becoming increasingly important in modern financial analysis. By taking this course, Financial Analysts may gain the skills needed to develop and implement sophisticated financial models.
Business Analyst
Business Analyst
Business Analysts bridge the gap between the business and IT teams, helping organizations to identify and solve business problems. This course, Machine Learning Models in Science, may provide Business Analysts with valuable insights into the application of machine learning techniques in a business context. The course covers data preprocessing techniques, foundational AI algorithms, and advanced AI techniques, which can empower Business Analysts to better understand and leverage data to drive business decisions.
Risk Analyst
Risk Analyst
Risk Analysts identify, assess, and mitigate risks to organizations. This course, Machine Learning Models in Science, may provide Risk Analysts with valuable insights into the application of machine learning techniques in risk management. The course covers data preprocessing techniques, foundational AI algorithms, and advanced AI techniques, which are becoming increasingly important in modern risk analysis practices. By taking this course, Risk Analysts may gain the skills needed to develop and implement effective risk management strategies.
For more career information including salaries, visit:
OpenCourser.com/course/ubnzyi/machine
Reading list
We've selected seven 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
Machine Learning Models in Science.
Provides a comprehensive overview of machine learning and data mining. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning. It also includes case studies and examples from various fields.
Save
Provides a comprehensive overview of deep learning. It covers a wide range of topics, including neural networks, convolutional neural networks, and recurrent neural networks. It is written by three of the leading researchers in deep learning.
Provides a comprehensive overview of pattern recognition and machine learning. It covers a wide range of topics, including Bayesian methods, support vector machines, and kernel methods. It is written by one of the leading researchers in pattern recognition.
Save
Provides a comprehensive overview of machine learning from an algorithmic perspective. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning. It is written in a clear and concise style, making it accessible to readers with no prior knowledge of machine learning.
Provides a comprehensive overview of machine learning for finance. It covers a wide range of topics, including data preprocessing, feature selection, model building, and evaluation. It also includes case studies and examples from various financial domains.
Provides a gentle introduction to machine learning. It covers a wide range of topics, including data preprocessing, feature selection, model building, and evaluation. It is written in a clear and concise style, making it accessible to readers with no prior knowledge of machine learning.
Provides a practical introduction to deep learning with Python. It covers a wide range of topics, including neural networks, convolutional neural networks, and recurrent neural networks. It is written in a clear and concise style, making it accessible to readers with no prior knowledge of deep learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ubnzyi/machine
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Effort
2 weeks, 4-6 hours per week
Level
Intermediate
Via
Coursera
Institution
LearnQuest
Instructors
Sabrina Moore
Rajvir Dua
Neelesh Tiruviluamala
Language
English
Transcripts
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This course belongs to a
collection
called
AI for Scientific Research. It's comprised of four courses:
- Capstone Project: Advanced AI for Drug Discovery
- Introduction to Data Science and scikit-learn in Python
- Neural Networks and Random Forests
- Machine Learning Models in Science (viewing)
Good to know
Suitable for individuals interested in applying machine learning techniques to scientific problems
Covers essential machine learning pipeline, from data preprocessing to running algorithms
Employs medical and astronomical datasets, providing practical applications
Led by instructors with expertise in machine learning and data science
Leverages Python for practical implementation and coding exercises
Provides a comprehensive overview of machine learning techniques and algorithms
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