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Snehan Kekre
Welcome to this project-based course on Logistic with NumPy and Python. In this project, you will do all the machine learning without using any of the popular machine learning libraries such as scikit-learn and statsmodels. The aim of this project and is to implement all the machinery, including gradient descent, cost function, and logistic regression, of the various learning algorithms yourself, so you have a deeper understanding of the fundamentals. By the time you complete this project, you will be able to build a logistic regression model using Python and NumPy, conduct basic exploratory data analysis, and implement gradient descent from scratch. The prerequisites for this project are prior programming experience in Python and a basic understanding of machine learning theory.
Read more
Welcome to this project-based course on Logistic with NumPy and Python. In this project, you will do all the machine learning without using any of the popular machine learning libraries such as scikit-learn and statsmodels. The aim of this project and is to implement all the machinery, including gradient descent, cost function, and logistic regression, of the various learning algorithms yourself, so you have a deeper understanding of the fundamentals. By the time you complete this project, you will be able to build a logistic regression model using Python and NumPy, conduct basic exploratory data analysis, and implement gradient descent from scratch. The prerequisites for this project are prior programming experience in Python and a basic understanding of machine learning theory.
Read more
Welcome to this project-based course on Logistic with NumPy and Python. In this project, you will do all the machine learning without using any of the popular machine learning libraries such as scikit-learn and statsmodels. The aim of this project and is to implement all the machinery, including gradient descent, cost function, and logistic regression, of the various learning algorithms yourself, so you have a deeper understanding of the fundamentals. By the time you complete this project, you will be able to build a logistic regression model using Python and NumPy, conduct basic exploratory data analysis, and implement gradient descent from scratch. The prerequisites for this project are prior programming experience in Python and a basic understanding of machine learning theory.
This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, NumPy, and Seaborn pre-installed.
Register for this course and see more details by visiting:
OpenCourser.com/course/2c9i3r/logistic
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What's inside
Syllabus
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Explores logistic regression, which is standard in many industries
Taught by Snehan Kekre, who is recognized for their work in machine learning
Provides a solid foundation for understanding the fundamentals of machine learning
Uses hands-on activities to reinforce learning
Prerequisites are recommended, but not strictly required
Requires some prior programming experience in Python
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Reviews summary
Deep dive into logistic regression fundamentals
According to students, this course offers a largely positive experience, particularly for those seeking a deep understanding of logistic regression. Learners appreciate the from-scratch implementation using NumPy, which they found invaluable for grasping underlying machine learning algorithms and their mechanics. The hands-on projects on the Rhyme platform are frequently highlighted as a strength, providing practical application. While it's highly beneficial for solidifying foundational knowledge, some learners found it challenging, suggesting a strong grasp of Python and NumPy is crucial. Its specific scope means it excels at its niche but isn't a broad ML overview, and some learners found the prerequisites were more demanding than expected.
Highly focused on logistic regression mechanics, not broad ML.
"My only minor critique is that it's quite specific; don't expect a broad ML overview."
"This was not what I expected and felt too academic for my professional needs. The Rhyme platform was fine, but the content wasn't for me."
"It's too focused on the low-level implementation. I prefer courses that teach how to use existing libraries efficiently."
Rhyme platform provides a smooth, pre-configured coding experience.
"The hands-on project environment on Rhyme was seamless, making it easy to focus on the code."
"Everything is already set up directly in your internet browser so you can just focus on learning."
"The Rhyme environment worked well."
Practical, code-based learning without high-level libraries.
"You will do all the machine learning without using any of the popular machine learning libraries such as scikit-learn."
"The approach of building logistic regression without relying on pre-built libraries is excellent for a deeper dive."
"Learning to implement gradient descent and logistic regression from the ground up provided immense clarity."
Course delivers profound insights into ML algorithms' core.
"Implementing everything from scratch with NumPy solidified my grasp of gradient descent and the cost function."
"This course finally connected all the dots on how [ML libraries] actually work under the hood."
"I now understand the underlying mechanisms of logistic regression far better."
Requires strong Python/NumPy, beyond basic understanding.
"I struggled with the pace... NumPy array operations and the mathematical derivations were sometimes hard to follow."
"Make sure your NumPy is strong before diving in, as it's heavily used."
"It's definitely for those comfortable with linear algebra and calculus."
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 Logistic Regression with NumPy and Python with these
activities:
Review Machine Learning Theory
Show steps
Refresh your knowledge of basic machine learning theory to strengthen your foundation for the course.
Browse courses on
Machine Learning
Show steps
-
Review concepts such as supervised learning, classification, and model evaluation.
Python Basics
Show steps
Begin by reviewing Python basics to strengthen your programming knowledge and prepare for the course.
Browse courses on
Python
Show steps
-
Review the basics of Python syntax and data types.
-
Practice writing simple Python functions.
Introduction to Machine Learning
Show steps
Expand your understanding of machine learning principles by reviewing a foundational book on the subject.
View
Machine Learning, revised and updated edition...
on Amazon
Show steps
-
Read the chapters relevant to logistic regression and machine learning theory.
-
Solve practice problems and exercises from the book.
Five other activities
Expand to see all activities and additional details
Show all eight activities
NumPy Tutorial
Show steps
Complete a tutorial on NumPy to familiarize yourself with its capabilities for numerical computations.
Browse courses on
NumPy
Show steps
-
Go through the NumPy tutorial and understand its core concepts.
-
Practice using NumPy arrays and functions in a Python script.
Peer Discussion
Show steps
Engage in discussions with peers to exchange understandings and perspectives on course topics.
Show steps
-
Join a study group or online forum to connect with other learners.
-
Participate in discussions and share your insights.
Logistic Regression Exercise
Show steps
Engage in practice drills to reinforce your understanding of logistic regression concepts.
Browse courses on
Logistic Regression
Show steps
-
Solve a series of logistic regression exercises.
-
Implement logistic regression from scratch using Python and NumPy.
Logistic Regression Project
Show steps
Solidify your learning by completing a project where you apply logistic regression to a real-world dataset.
Browse courses on
Logistic Regression
Show steps
-
Gather and explore a suitable dataset.
-
Train a logistic regression model using the dataset.
-
Evaluate the performance of your model.
Contribute to Open-Source
Show steps
Contribute to open-source projects related to logistic regression or machine learning to gain practical experience.
Browse courses on
Open-Source
Show steps
-
Identify open-source projects that align with the course topics.
-
Contact project maintainers to discuss potential contributions.
-
Collaborate with others to develop and contribute code.
Review Machine Learning Theory
Show steps
Refresh your knowledge of basic machine learning theory to strengthen your foundation for the course.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Review concepts such as supervised learning, classification, and model evaluation.
Python Basics
Show steps
Begin by reviewing Python basics to strengthen your programming knowledge and prepare for the course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review the basics of Python syntax and data types.
- Practice writing simple Python functions.
Introduction to Machine Learning
Show steps
Expand your understanding of machine learning principles by reviewing a foundational book on the subject.
View
Machine Learning, revised and updated edition...
on Amazon
Show steps
Show steps
Show steps
- Read the chapters relevant to logistic regression and machine learning theory.
- Solve practice problems and exercises from the book.
Five other activities
Expand to see all activities and additional details
Show all eight activities
NumPy Tutorial
Show steps
Complete a tutorial on NumPy to familiarize yourself with its capabilities for numerical computations.
Browse courses on
NumPy
Show steps
Show steps
Show steps
- Go through the NumPy tutorial and understand its core concepts.
- Practice using NumPy arrays and functions in a Python script.
Peer Discussion
Show steps
Engage in discussions with peers to exchange understandings and perspectives on course topics.
Show steps
Show steps
Show steps
- Join a study group or online forum to connect with other learners.
- Participate in discussions and share your insights.
Logistic Regression Exercise
Show steps
Engage in practice drills to reinforce your understanding of logistic regression concepts.
Browse courses on
Logistic Regression
Show steps
Show steps
Show steps
- Solve a series of logistic regression exercises.
- Implement logistic regression from scratch using Python and NumPy.
Logistic Regression Project
Show steps
Solidify your learning by completing a project where you apply logistic regression to a real-world dataset.
Browse courses on
Logistic Regression
Show steps
Show steps
Show steps
- Gather and explore a suitable dataset.
- Train a logistic regression model using the dataset.
- Evaluate the performance of your model.
Contribute to Open-Source
Show steps
Contribute to open-source projects related to logistic regression or machine learning to gain practical experience.
Browse courses on
Open-Source
Show steps
Show steps
Show steps
- Identify open-source projects that align with the course topics.
- Contact project maintainers to discuss potential contributions.
- Collaborate with others to develop and contribute code.
Career center
Learners who complete Logistic Regression with NumPy and Python will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists apply scientific methods to extract knowledge and insights from data in various forms, both structured and unstructured. This course will teach you the fundamentals of data science, including how to collect, clean, and analyze data, and how to build and evaluate machine learning models.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. They work in a variety of fields, including healthcare, finance, and manufacturing. This course will teach you the basics of machine learning, how to use Python and NumPy to build and evaluate machine learning models, and how to deploy models to production.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. They work in investment banks, hedge funds, and other financial institutions. This course will teach you the basics of quantitative finance, how to use Python and NumPy to perform financial analysis, and how to build and evaluate financial models.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk. They work in insurance companies, pension funds, and other financial institutions. This course will teach you the basics of actuarial science, how to use Python and NumPy to perform actuarial analysis, and how to build and evaluate actuarial models.
Data Analyst
Data Analyst
A Data Analyst takes raw data and performs statistical analysis to uncover patterns or trends. Its main purpose is to help companies make informed decisions. This course will teach you the basics of statistical analysis, how to use Python and NumPy to perform data analysis, and how to communicate your findings to others.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data. They work in a variety of fields, including healthcare, finance, and marketing. This course will teach you the basics of statistics, how to use Python and NumPy to perform statistical analysis, and how to communicate your findings to others.
Financial Analyst
Financial Analyst
Financial Analysts analyze financial data to make investment recommendations. They work in investment banks, hedge funds, and other financial institutions. This course will teach you the basics of financial analysis, how to use Python and NumPy to perform financial analysis, and how to communicate your findings to others.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve business problems. They work in a variety of industries, including healthcare, finance, and manufacturing. This course will teach you the basics of operations research, how to use Python and NumPy to solve business problems, and how to communicate your findings to others.
Risk Analyst
Risk Analyst
Risk Analysts analyze risk and develop strategies to mitigate risk. They work in a variety of industries, including healthcare, finance, and manufacturing. This course will teach you the basics of risk analysis, how to use Python and NumPy to perform risk analysis, and how to communicate your findings to others.
Biostatistician
Biostatistician
Biostatisticians apply statistical methods to solve problems in medicine and public health. They work in a variety of settings, including hospitals, research institutions, and pharmaceutical companies. This course will teach you the basics of biostatistics, how to use Python and NumPy to analyze biomedical data, and how to communicate your findings to others.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines. They work in a variety of industries, including healthcare, finance, and manufacturing. This course will teach you the basics of data engineering, how to use Python and NumPy to build and maintain data pipelines, and how to manage and secure data.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They work in a variety of industries, including healthcare, finance, and manufacturing. This course will teach you the basics of software engineering, how to use Python and NumPy to develop software systems, and how to test and deploy software.
Epidemiologist
Epidemiologist
Epidemiologists study the distribution and patterns of health and disease in populations. They work in a variety of settings, including public health departments, hospitals, and research institutions. This course will teach you the basics of epidemiology, how to use Python and NumPy to analyze epidemiological data, and how to communicate your findings to others.
Business Analyst
Business Analyst
Business Analysts analyze business processes and identify opportunities for improvement. They work in a variety of industries, including healthcare, finance, and manufacturing. This course will teach you the basics of business analysis, how to use Python and NumPy to analyze business data, and how to communicate your findings to others.
Medical Physicist
Medical Physicist
Medical Physicists apply the principles of physics to medicine. They work in a variety of settings, including hospitals, research institutions, and medical device companies. This course will teach you the basics of medical physics, how to use Python and NumPy to analyze medical data, and how to communicate your findings to others.
For more career information including salaries, visit:
OpenCourser.com/course/2c9i3r/logistic
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
Logistic Regression with NumPy and Python.
This hands-on guide offers a practical approach to machine learning using Python, covering fundamental concepts and techniques, including logistic regression. It provides step-by-step instructions and real-world examples that enhance the understanding of machine learning algorithms.
An Introduction to Statistical Learning provides a comprehensive overview of statistical learning methods, including logistic regression. It valuable resource for those looking to gain a deeper understanding of the theory and practice of statistical learning.
This textbook provides a comprehensive introduction to statistical learning methods, including logistic regression. It offers a practical approach with a focus on implementation in R, making it suitable for learners who wish to apply these methods in their own research or projects.
This well-regarded textbook provides a comprehensive overview of statistical learning methods, including logistic regression. It offers a balance of theory and applications, with a strong emphasis on practical implementation.
Save
Data Mining: Practical Machine Learning Tools and Techniques provides a comprehensive overview of data mining and machine learning, including logistic regression. It valuable resource for those looking to gain a deeper understanding of the theory and practice of data mining and machine learning.
This practical reference focuses specifically on logistic regression, providing detailed guidance on model building, interpretation, and validation. It covers various applications and includes case studies to illustrate the use of logistic regression in real-world settings.
This authoritative text provides a thorough treatment of logistic regression, including its theoretical underpinnings and practical applications in various domains. It offers detailed explanations, worked examples, and case studies to help readers develop a strong understanding of the method.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow provides a practical introduction to machine learning, including logistic regression. It great resource for those looking to get started with machine learning using popular Python libraries.
Python Machine Learning provides a comprehensive overview of machine learning using Python, including logistic regression. It valuable resource for those looking to gain a deeper understanding of the theory and practice of machine learning using Python.
Machine Learning in Action provides a practical introduction to machine learning, including logistic regression. It great resource for those looking to get started with machine learning using Python.
This theoretical text provides a rigorous foundation in machine learning, covering logistic regression and other fundamental algorithms. It offers mathematical proofs, detailed explanations, and exercises to help readers develop a strong understanding of the theoretical aspects of machine learning.
Save
Focuses specifically on logistic regression models for ordinal response variables. It provides a detailed treatment of the theory and application of these models, making it a valuable resource for researchers and practitioners working with ordinal data.
Save
This comprehensive reference provides a broad overview of pattern recognition and machine learning, including logistic regression. It offers a rigorous mathematical treatment and in-depth discussions of various machine learning algorithms, making it suitable for advanced learners seeking a deeper understanding.
Save
Deep Learning provides a comprehensive overview of deep learning, including logistic regression. It valuable resource for those looking to gain a deeper understanding of the theory and practice of deep learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/2c9i3r/logistic
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