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Axel Sirota
Machine Learning is changing the world and at the very core of machine learning are advanced statistical models. With this course, you will know how to create an ML application for problems that appear at your work and understand the basis behind it
Read more
Machine Learning is changing the world and at the very core of machine learning are advanced statistical models. With this course, you will know how to create an ML application for problems that appear at your work and understand the basis behind it
Read more
Machine Learning is changing the world and at the very core of machine learning are advanced statistical models. With this course, you will know how to create an ML application for problems that appear at your work and understand the basis behind it
When you look at the core of machine learning, there are advanced statistical models. In this course, Interpreting Data with Advanced Statistical Models, you will gain the ability to effectively understand how to create an ML application that will be able to revolutionize the problems that appear at your work. First, you will learn the basic of Machine learning. Next, you will discover linear regression in a more general pattern, expanding to multiple and polynomial features. Continuing, you will explore how to classify with Logistic Regression, SVMs, and Bayesian methods. Finally, you will learn the intrinsic patterns of data with unsupervised techniques such as K Means and PCA. When you’re finished with this course, you will have the skills and knowledge of Machine Learning needed to apply it in a real-world application.
Register for this course and see more details by visiting:
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What's inside
Syllabus
Course Overview
Getting Started with Machine Learning
Finding Those Models
Predicting Linear Relationships with Regression
Read more
Syllabus
Course Overview
Getting Started with Machine Learning
Finding Those Models
Predicting Linear Relationships with Regression
Read more
Understanding Regression Models in Depth
The Problem of Correct Classification
Large Margin and Bayesian Classification
The Subtle Art of Not Needing Labels: Unsupervised Learning
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides foundational knowledge in Machine Learning, particularly in statistical models
Offers a comprehensive overview of various statistical models used in Machine Learning, including linear regression, logistic regression, support vector machines, and Bayesian methods
Empowers learners to create Machine Learning applications that can solve real-world problems
Imparts skills in unsupervised learning techniques such as K Means and PCA, which are valuable for extracting patterns from data without labels
Appropriate for individuals seeking to understand the fundamentals of Machine Learning and apply statistical models to solve problems
Requires learners to have basic knowledge of Machine Learning concepts and statistical methods
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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 Interpreting Data with Advanced Statistical Models with these
activities:
Seek a mentor in advanced statistical modeling
Show steps
Provides guidance and support from an experienced professional, accelerating your learning and understanding of advanced statistical modeling.
Browse courses on
Mentorship
Show steps
-
Identify potential mentors in your field or network.
-
Reach out to the mentors and express your interest in learning advanced statistical modeling.
Refresh Linear Algebra skills
Show steps
Strengthen your understanding of linear algebra before starting this course.
Browse courses on
Linear Algebra
Show steps
-
Review the basics of linear algebra, including vectors, matrices, and linear transformations.
-
Practice solving systems of linear equations.
-
Calculate eigenvalues and eigenvectors of matrices.
Review foundational statistics and probability
Show steps
Refreshes your knowledge of statistics and probability, which are essential concepts for understanding advanced statistical models.
Browse courses on
Statistics
Show steps
-
Review statistics concepts such as mean, standard deviation, and variance.
-
Practice solving probability problems involving conditional probability, Bayes' theorem, and random variables.
16 other activities
Expand to see all activities and additional details
Show all 19 activities
Solve Linear Regression Problems
Show steps
Solve a variety of linear regression problems to develop a strong understanding of its concepts.
Browse courses on
Linear Regression
Show steps
-
Establish the linear relationship between variables
-
Estimate model parameters and make predictions
Complete Classification with scikit-learn tutorial
Show steps
Gain hands-on experience with classification algorithms using Python's scikit-learn library.
Browse courses on
Classification
Show steps
-
Install scikit-learn and import the necessary modules.
-
Load and explore a dataset for classification.
-
Train a classification model using scikit-learn's algorithms.
-
Evaluate the performance of the model using metrics such as accuracy and F1-score.
Classify Data with SVMs
Show steps
Follow guided tutorials on SVM classification to learn how to effectively classify data using this technique.
Browse courses on
Support Vector Machines
Show steps
-
Understand the concepts of hyperplanes and margins
-
Apply SVM algorithms to classify data
-
Evaluate SVM models
Attend 'Pandas for Data Manipulation and Analysis' workshop
Show steps
Enhance your data handling skills by attending a workshop focused on using Pandas for data manipulation and analysis.
Browse courses on
Pandas
Show steps
-
Register for the workshop.
Solve advanced statistical modeling problems
Show steps
Provides practice in applying advanced statistical modeling techniques, improving your understanding and problem-solving skills.
Browse courses on
Regression
Show steps
-
Identify the appropriate statistical model for a given problem.
-
Apply the model to solve the problem and interpret the results.
-
Evaluate the performance of the model.
Discuss Classification Techniques with Bayesian Methods
Show steps
Participate in peer study groups to delve into the nuances of Bayesian classification techniques.
Show steps
-
Review the basics of probability and Bayes' Theorem
-
Apply Bayesian methods to classify data
-
Compare Bayesian approaches with other classification techniques
Build a Machine Learning Model
Show steps
Build a machine learning model to apply the concepts learned in this course to real-world applications.
Show steps
-
Define the problem and collect data
-
Prepare data for modeling
-
Build and train the model
-
Evaluate the model
-
Deploy the model
Explore advanced statistical modeling techniques
Show steps
Expands your knowledge of advanced statistical modeling techniques beyond what is covered in the course.
Browse courses on
Regression
Show steps
-
Find tutorials on advanced statistical modeling techniques, such as Bayesian modeling or time series analysis.
-
Follow the tutorials and apply the techniques to practical problems.
Visualize Data with Unsupervised Techniques
Show steps
Create visual representations of data using unsupervised techniques to gain deeper insights.
Browse courses on
Unsupervised Learning
Show steps
-
Understand the concepts of clustering and dimensionality reduction
-
Apply K Means algorithm to cluster data
-
Apply PCA to reduce data dimensionality
Create a visual representation of an advanced statistical model
Show steps
Helps you visualize and understand the concepts behind advanced statistical models, improving your comprehension.
Browse courses on
Data Visualization
Show steps
-
Choose an advanced statistical model.
-
Create a visual representation of the model, using charts, graphs, or diagrams.
-
Explain the model and its components using the visual representation.
Develop a machine learning model to predict customer churn
Show steps
Apply the concepts learned in this course by building a practical machine learning model to solve a real-world problem, such as predicting customer churn.
Browse courses on
Machine Learning Projects
Show steps
-
Define the problem statement and collect a suitable dataset.
-
Explore and preprocess the data.
-
Develop and train a machine learning model (optional: try multiple algorithms).
-
Evaluate the model's performance.
Contribute to an open-source project related to advanced statistical modeling
Show steps
Provides practical experience and exposure to real-world applications of advanced statistical modeling, deepening your understanding.
Browse courses on
Open Source
Show steps
-
Find an open-source project related to advanced statistical modeling.
-
Identify areas where you can contribute based on your skills and interests.
-
Make contributions to the project, such as bug fixes, feature enhancements, or documentation improvements.
Develop a machine learning application using advanced statistical models
Show steps
Provides hands-on experience in applying advanced statistical models to real-world problems, enhancing your practical skills.
Browse courses on
Machine Learning
Show steps
-
Identify a problem that can be solved using advanced statistical models.
-
Collect and prepare data for the problem.
-
Develop a machine learning model using the data and statistical techniques.
-
Evaluate and deploy the model to solve the problem.
Create a Python script collection for data preparation and modeling
Show steps
Consolidate your learning by creating a collection of Python scripts that encapsulate the data preparation, modeling, and evaluation techniques covered in this course.
Show steps
-
Gather relevant Python scripts from the course materials and other sources.
-
Organize and document the scripts for clarity.
-
Create a README file to explain the purpose and usage of each script.
Contribute to a machine learning open-source project
Show steps
Enhance your practical understanding by contributing to an open-source machine learning project on platforms like GitHub.
Browse courses on
Community Involvement
Show steps
-
Identify a suitable open-source project.
-
Find a specific issue or feature to work on.
-
Submit a pull request with your contribution.
Write a blog post summarizing the key concepts of machine learning
Show steps
Strengthen your understanding of the core concepts of machine learning by summarizing them in a blog post.
Browse courses on
Technical Writing
Show steps
-
Identify and outline the major concepts of machine learning.
-
Research and gather information from reliable sources.
-
Write a draft of the blog post, explaining the concepts clearly and concisely.
Seek a mentor in advanced statistical modeling
Show steps
Provides guidance and support from an experienced professional, accelerating your learning and understanding of advanced statistical modeling.
Browse courses on
Mentorship
Show steps
Show steps
Show steps
- Identify potential mentors in your field or network.
- Reach out to the mentors and express your interest in learning advanced statistical modeling.
Refresh Linear Algebra skills
Show steps
Strengthen your understanding of linear algebra before starting this course.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review the basics of linear algebra, including vectors, matrices, and linear transformations.
- Practice solving systems of linear equations.
- Calculate eigenvalues and eigenvectors of matrices.
Review foundational statistics and probability
Show steps
Refreshes your knowledge of statistics and probability, which are essential concepts for understanding advanced statistical models.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Review statistics concepts such as mean, standard deviation, and variance.
- Practice solving probability problems involving conditional probability, Bayes' theorem, and random variables.
16 other activities
Expand to see all activities and additional details
Show all 19 activities
Solve Linear Regression Problems
Show steps
Solve a variety of linear regression problems to develop a strong understanding of its concepts.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Establish the linear relationship between variables
- Estimate model parameters and make predictions
Complete Classification with scikit-learn tutorial
Show steps
Gain hands-on experience with classification algorithms using Python's scikit-learn library.
Browse courses on
Classification
Show steps
Show steps
Show steps
- Install scikit-learn and import the necessary modules.
- Load and explore a dataset for classification.
- Train a classification model using scikit-learn's algorithms.
- Evaluate the performance of the model using metrics such as accuracy and F1-score.
Classify Data with SVMs
Show steps
Follow guided tutorials on SVM classification to learn how to effectively classify data using this technique.
Browse courses on
Support Vector Machines
Show steps
Show steps
Show steps
- Understand the concepts of hyperplanes and margins
- Apply SVM algorithms to classify data
- Evaluate SVM models
Attend 'Pandas for Data Manipulation and Analysis' workshop
Show steps
Enhance your data handling skills by attending a workshop focused on using Pandas for data manipulation and analysis.
Browse courses on
Pandas
Show steps
Show steps
Show steps
- Register for the workshop.
Solve advanced statistical modeling problems
Show steps
Provides practice in applying advanced statistical modeling techniques, improving your understanding and problem-solving skills.
Browse courses on
Regression
Show steps
Show steps
Show steps
- Identify the appropriate statistical model for a given problem.
- Apply the model to solve the problem and interpret the results.
- Evaluate the performance of the model.
Discuss Classification Techniques with Bayesian Methods
Show steps
Participate in peer study groups to delve into the nuances of Bayesian classification techniques.
Show steps
Show steps
Show steps
- Review the basics of probability and Bayes' Theorem
- Apply Bayesian methods to classify data
- Compare Bayesian approaches with other classification techniques
Build a Machine Learning Model
Show steps
Build a machine learning model to apply the concepts learned in this course to real-world applications.
Show steps
Show steps
Show steps
- Define the problem and collect data
- Prepare data for modeling
- Build and train the model
- Evaluate the model
- Deploy the model
Explore advanced statistical modeling techniques
Show steps
Expands your knowledge of advanced statistical modeling techniques beyond what is covered in the course.
Browse courses on
Regression
Show steps
Show steps
Show steps
- Find tutorials on advanced statistical modeling techniques, such as Bayesian modeling or time series analysis.
- Follow the tutorials and apply the techniques to practical problems.
Visualize Data with Unsupervised Techniques
Show steps
Create visual representations of data using unsupervised techniques to gain deeper insights.
Browse courses on
Unsupervised Learning
Show steps
Show steps
Show steps
- Understand the concepts of clustering and dimensionality reduction
- Apply K Means algorithm to cluster data
- Apply PCA to reduce data dimensionality
Create a visual representation of an advanced statistical model
Show steps
Helps you visualize and understand the concepts behind advanced statistical models, improving your comprehension.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose an advanced statistical model.
- Create a visual representation of the model, using charts, graphs, or diagrams.
- Explain the model and its components using the visual representation.
Develop a machine learning model to predict customer churn
Show steps
Apply the concepts learned in this course by building a practical machine learning model to solve a real-world problem, such as predicting customer churn.
Browse courses on
Machine Learning Projects
Show steps
Show steps
Show steps
- Define the problem statement and collect a suitable dataset.
- Explore and preprocess the data.
- Develop and train a machine learning model (optional: try multiple algorithms).
- Evaluate the model's performance.
Contribute to an open-source project related to advanced statistical modeling
Show steps
Provides practical experience and exposure to real-world applications of advanced statistical modeling, deepening your understanding.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Find an open-source project related to advanced statistical modeling.
- Identify areas where you can contribute based on your skills and interests.
- Make contributions to the project, such as bug fixes, feature enhancements, or documentation improvements.
Develop a machine learning application using advanced statistical models
Show steps
Provides hands-on experience in applying advanced statistical models to real-world problems, enhancing your practical skills.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify a problem that can be solved using advanced statistical models.
- Collect and prepare data for the problem.
- Develop a machine learning model using the data and statistical techniques.
- Evaluate and deploy the model to solve the problem.
Create a Python script collection for data preparation and modeling
Show steps
Consolidate your learning by creating a collection of Python scripts that encapsulate the data preparation, modeling, and evaluation techniques covered in this course.
Show steps
Show steps
Show steps
- Gather relevant Python scripts from the course materials and other sources.
- Organize and document the scripts for clarity.
- Create a README file to explain the purpose and usage of each script.
Contribute to a machine learning open-source project
Show steps
Enhance your practical understanding by contributing to an open-source machine learning project on platforms like GitHub.
Browse courses on
Community Involvement
Show steps
Show steps
Show steps
- Identify a suitable open-source project.
- Find a specific issue or feature to work on.
- Submit a pull request with your contribution.
Write a blog post summarizing the key concepts of machine learning
Show steps
Strengthen your understanding of the core concepts of machine learning by summarizing them in a blog post.
Browse courses on
Technical Writing
Show steps
Show steps
Show steps
- Identify and outline the major concepts of machine learning.
- Research and gather information from reliable sources.
- Write a draft of the blog post, explaining the concepts clearly and concisely.
Career center
Learners who complete Interpreting Data with Advanced Statistical Models will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use their knowledge of statistics, programming, and machine learning to extract insights from data. This course teaches the advanced statistical models that are at the core of machine learning, which would be extremely valuable to succeed in this field.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and deploy machine learning models. This course would be a great foundation for this role, as it teaches the advanced statistical models that are at the core of machine learning.
Data Analyst
Data Analyst
Data Analysts use their skills in statistics, programming, and data visualization to analyze data and make recommendations. This course would be helpful for Data Analysts who want to learn more about the advanced statistical models that are used in machine learning.
Statistician
Statistician
Statisticians use their knowledge of statistics to collect, analyze, and interpret data. This course would be helpful for Statisticians who want to learn more about the advanced statistical models that are used in machine learning.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course would be helpful for Software Engineers who want to learn more about the advanced statistical models that are used in machine learning.
Financial Analyst
Financial Analyst
Financial Analysts use their knowledge of finance and economics to analyze financial data and make recommendations. This course would be helpful for Financial Analysts who want to learn more about the advanced statistical models that are used in machine learning.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. This course would be helpful for Product Managers who want to learn more about the advanced statistical models that are used in machine learning.
Consultant
Consultant
Consultants provide advice and guidance to businesses. This course would be helpful for Consultants who want to learn more about the advanced statistical models that are used in machine learning.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use their knowledge of mathematics, statistics, and computer science to develop and implement financial models. This course would be helpful for Quantitative Analysts who want to learn more about the advanced statistical models that are used in machine learning.
Teacher
Teacher
Teachers instruct students in a variety of subjects. This course would be helpful for Teachers who want to learn more about the advanced statistical models that are used in machine learning.
Marketing Manager
Marketing Manager
Marketing Managers are responsible for developing and executing marketing campaigns. This course would be helpful for Marketing Managers who want to learn more about the advanced statistical models that are used in machine learning.
Account Manager
Account Manager
Account Managers are responsible for managing relationships with clients. This course would be helpful for Account Managers who want to learn more about the advanced statistical models that are used in machine learning.
Sales Manager
Sales Manager
Sales Managers are responsible for managing sales teams and developing sales strategies. This course would be helpful for Sales Managers who want to learn more about the advanced statistical models that are used in machine learning.
Business Analyst
Business Analyst
Business Analysts use their knowledge of business and technology to analyze business processes and make recommendations. This course would be helpful for Business Analysts who want to learn more about the advanced statistical models that are used in machine learning.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use their knowledge of mathematics, statistics, and computer science to solve complex problems in business and industry. This course would be helpful for Operations Research Analysts who want to learn more about the advanced statistical models that are used in machine learning.
For more career information including salaries, visit:
OpenCourser.com/course/1i9rrs/interpreting
Reading list
We've selected 15 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
Interpreting Data with Advanced Statistical Models.
Provides a comprehensive overview of information theory, inference, and learning algorithms. It good resource for those who want to learn about the theoretical foundations of machine learning.
Classic in the field of statistical learning. It provides a comprehensive overview of statistical methods for data analysis, including linear regression, logistic regression, and decision trees.
Save
Comprehensive overview of deep learning, a subfield of machine learning that uses artificial neural networks to learn from data. It good resource for those who want to learn about the theoretical foundations of deep learning.
Provides a comprehensive overview of probabilistic graphical models, a type of machine learning model that represents the relationships between variables in a graphical format. It good resource for those who want to learn about the theoretical foundations of machine learning.
Provides a comprehensive overview of machine learning, covering both supervised and unsupervised learning. It good resource for those who want to learn about the theoretical foundations of machine learning.
Provides a comprehensive overview of machine learning from a Bayesian and optimization perspective. It good resource for those who want to learn about the theoretical foundations of machine learning.
Is an introduction to reinforcement learning, a subfield of machine learning that focuses on learning how to make decisions in order to maximize reward. It good resource for those who want to learn about the theoretical foundations of reinforcement learning.
Practical guide to machine learning with Python. It covers a wide range of topics, from data preparation to model evaluation. It good resource for those who want to learn how to use machine learning to solve real-world problems.
Provides a comprehensive overview of machine learning for audio, speech, and music processing, a type of machine learning that focuses on the analysis of audio data. It good resource for those who want to learn about the theoretical foundations of machine learning.
Provides a comprehensive overview of Bayesian statistics, a type of statistical inference that uses probability theory to update beliefs as new evidence becomes available. It good resource for those who want to learn about the theoretical foundations of machine learning.
Provides a comprehensive overview of causal inference, a type of statistical inference that aims to identify the causal relationships between variables. It good resource for those who want to learn about the theoretical foundations of machine learning.
Save
Provides a comprehensive overview of machine learning techniques for text analysis, a type of machine learning that focuses on the analysis of text data. It good resource for those who want to learn about the theoretical foundations of machine learning.
Save
Provides a comprehensive overview of machine learning for computer vision, a type of machine learning that focuses on the analysis of visual data. It good resource for those who want to learn about the theoretical foundations of machine learning.
Provides a comprehensive overview of machine learning for natural language processing, a type of machine learning that focuses on the analysis of natural language data. It good resource for those who want to learn about the theoretical foundations of machine learning.
Practical guide to machine learning for those who want to learn how to use machine learning to solve real-world problems. It covers a wide range of topics, from data preparation to model evaluation.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/1i9rrs/interpreting
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Time
189 hours
Level
Advanced
Via
Pluralsight
Instructor
Axel Sirota
Language
English
Good to know
Provides foundational knowledge in Machine Learning, particularly in statistical models
Offers a comprehensive overview of various statistical models used in Machine Learning, including linear regression, logistic regression, support vector machines, and Bayesian methods
Empowers learners to create Machine Learning applications that can solve real-world problems
Imparts skills in unsupervised learning techniques such as K Means and PCA, which are valuable for extracting patterns from data without labels
Appropriate for individuals seeking to understand the fundamentals of Machine Learning and apply statistical models to solve problems
Requires learners to have basic knowledge of Machine Learning concepts and statistical methods
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