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Logistic Regression with Python and Numpy
Amit Yadav
Amit Yadav
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/fx675w/logistic
What's inside
Syllabus
Deep Learning Fundamentals: Logistic Regression
Welcome to this project-based course on Logistic Regression. In this 2-hour long project-based course, you will learn how to implement Logistic Regression using Python and Numpy. Logistic Regression is an important fundamental concept in Deep Learning, and even though popular machine learning frameworks have implementations of logistic regression available, learning to implement it on your own will enable you to understand the mechanics of optimization algorithm and the training and validation process. By the end of this course, you would create and train a logistic model that will be able to predict if a given image is of hand-written digit zero or of hand-written digit one. The model will be able to distinguish between images or zeros and ones, and it will do that with a very high accuracy. Not only that, your implementation of the logistic model will also be able to solve any generic binary classification problem. You will still have to train model instances on specific datasets of course, but you won’t have to change the implementation and it will be re-usable. The dataset for images of hand written digits comes from the popular MNIST dataset. This data set consists of images for the 10 hand-written digits (from 0 to 9), but since we are implementing logistic regression, and are looking to solve binary classification problems, we will work with examples of hand written zeros and hand written ones and we will ignore examples of rest of the digits.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Examines logistic regression, which is essential knowledge for working with machine learning algorithms
Develops skills for implementing machine learning algorithms from scratch, which provides a deeper understanding of machine learning
Employs a project-based approach that enables students to actively engage with the material and apply their understanding
Requires basic programming experience in Python and a fundamental understanding of machine learning theory, limiting accessibility for beginners
Focuses on logistic regression exclusively, providing a specialized perspective but potentially limiting applicability to broader machine learning tasks
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Reviews summary
Great introduction to logistic regression with numpy
Learners say that this course provides great hands-on training and that the course is very useful. A learner recommends the course to "machine learning students." The course uses Python and Numpy to introduce logistic regression. It also has an amazing project and is well-suited for beginners.
Learners studying machine learning may find this course helpful.
"It's implementation of gradient descent without the theory. Without the theory, it would not be understandable."
The course is appropriate for learners with limited knowledge of logistic regression.
"For beginners this course is great."
"Good course, very simple to understand"
The course provides opportunities to put theory into practice with hands-on training.
"great hand-on training"
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 Python and Numpy with these
activities:
Join a Study Group
Show steps
Form or join a study group with fellow students to discuss course material, work on projects, and support each other's learning.
Browse courses on
Machine Learning
Show steps
-
Identify fellow students who share your interests and learning goals.
-
Establish regular meeting times and a communication platform for the group.
-
Take turns presenting concepts and leading discussions.
Prioritize Concepts
Show steps
Review the fundamentals of machine learning before the course begins. This will help you successfully implement optimization algorithms and understand the training and validation process.
Browse courses on
Machine Learning
Show steps
-
Review basic machine learning concepts, such as supervised learning, unsupervised learning, and model evaluation.
-
Identify the key concepts and algorithms involved in logistic regression.
-
Practice implementing simple machine learning algorithms, such as linear regression, in your preferred programming language.
Connect with Experts
Show steps
Identify experts in the field of machine learning, particularly those with experience in logistic regression. Reach out to them to request guidance.
Browse courses on
Machine Learning
Show steps
-
Attend industry events, conferences, or online forums to network with professionals.
-
Reach out to professors, researchers, or practitioners via email or LinkedIn.
-
Set up informational interviews to learn from their experiences and insights.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Explore Scikit-Learn
Show steps
Follow tutorials on Scikit-Learn to learn about its features and how to apply them to machine learning tasks, including logistic regression.
Browse courses on
Machine Learning
Show steps
-
Complete tutorials that demonstrate how to load and preprocess data using Scikit-Learn.
-
Follow tutorials that cover logistic regression using Scikit-Learn, including model training, evaluation, and hyperparameter tuning.
-
Explore examples that showcase how to use Scikit-Learn for other machine learning algorithms.
Code in Numpy
Show steps
Complete coding exercises in NumPy to solidify your understanding of the library's functions and capabilities.
Browse courses on
Programming
Show steps
-
Solve coding challenges that involve data manipulation, such as creating arrays, performing mathematical operations, and filtering data.
-
Practice using NumPy functions for linear algebra operations, such as matrix multiplication and eigenvector computation.
-
Implement basic machine learning algorithms, such as linear regression, using NumPy.
Elements of Statistical Learning
Show steps
Read this book to enhance your understanding of machine learning concepts, including logistic regression. It provides a comprehensive overview of statistical learning methods.
View
The Elements of Statistical Learning: Data...
on Amazon
Show steps
-
Review the chapters on supervised learning, model selection, and regularization.
-
Focus on the sections that cover logistic regression, including its mathematical formulation and optimization techniques.
Build a Logistic Regression Model
Show steps
Develop a logistic regression model that can classify handwritten digits into '0' or '1'. This project will allow you to apply the concepts you learn in the course hands-on.
Browse courses on
Logistic Regression
Show steps
-
Gather and prepare a dataset of handwritten digits.
-
Implement the logistic regression algorithm from scratch using Python and NumPy.
-
Train and evaluate the model on the dataset.
-
Analyze the results and identify areas for improvement.
Contribute to Open Source
Show steps
Participate in open-source projects related to machine learning or logistic regression. This will allow you to apply your skills and gain valuable experience.
Browse courses on
Machine Learning
Show steps
-
Identify open-source projects on platforms like GitHub that align with your interests.
-
Contribute to the project by fixing bugs, adding new features, or improving documentation.
-
Collaborate with other contributors and learn from their expertise.
Join a Study Group
Show steps
Form or join a study group with fellow students to discuss course material, work on projects, and support each other's learning.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify fellow students who share your interests and learning goals.
- Establish regular meeting times and a communication platform for the group.
- Take turns presenting concepts and leading discussions.
Prioritize Concepts
Show steps
Review the fundamentals of machine learning before the course begins. This will help you successfully implement optimization algorithms and understand the training and validation process.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Review basic machine learning concepts, such as supervised learning, unsupervised learning, and model evaluation.
- Identify the key concepts and algorithms involved in logistic regression.
- Practice implementing simple machine learning algorithms, such as linear regression, in your preferred programming language.
Connect with Experts
Show steps
Identify experts in the field of machine learning, particularly those with experience in logistic regression. Reach out to them to request guidance.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Attend industry events, conferences, or online forums to network with professionals.
- Reach out to professors, researchers, or practitioners via email or LinkedIn.
- Set up informational interviews to learn from their experiences and insights.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Explore Scikit-Learn
Show steps
Follow tutorials on Scikit-Learn to learn about its features and how to apply them to machine learning tasks, including logistic regression.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Complete tutorials that demonstrate how to load and preprocess data using Scikit-Learn.
- Follow tutorials that cover logistic regression using Scikit-Learn, including model training, evaluation, and hyperparameter tuning.
- Explore examples that showcase how to use Scikit-Learn for other machine learning algorithms.
Code in Numpy
Show steps
Complete coding exercises in NumPy to solidify your understanding of the library's functions and capabilities.
Browse courses on
Programming
Show steps
Show steps
Show steps
- Solve coding challenges that involve data manipulation, such as creating arrays, performing mathematical operations, and filtering data.
- Practice using NumPy functions for linear algebra operations, such as matrix multiplication and eigenvector computation.
- Implement basic machine learning algorithms, such as linear regression, using NumPy.
Elements of Statistical Learning
Show steps
Read this book to enhance your understanding of machine learning concepts, including logistic regression. It provides a comprehensive overview of statistical learning methods.
View
The Elements of Statistical Learning: Data...
on Amazon
Show steps
Show steps
Show steps
- Review the chapters on supervised learning, model selection, and regularization.
- Focus on the sections that cover logistic regression, including its mathematical formulation and optimization techniques.
Build a Logistic Regression Model
Show steps
Develop a logistic regression model that can classify handwritten digits into '0' or '1'. This project will allow you to apply the concepts you learn in the course hands-on.
Browse courses on
Logistic Regression
Show steps
Show steps
Show steps
- Gather and prepare a dataset of handwritten digits.
- Implement the logistic regression algorithm from scratch using Python and NumPy.
- Train and evaluate the model on the dataset.
- Analyze the results and identify areas for improvement.
Contribute to Open Source
Show steps
Participate in open-source projects related to machine learning or logistic regression. This will allow you to apply your skills and gain valuable experience.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify open-source projects on platforms like GitHub that align with your interests.
- Contribute to the project by fixing bugs, adding new features, or improving documentation.
- Collaborate with other contributors and learn from their expertise.
Career center
Learners who complete Logistic Regression with Python and Numpy will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts clean, analyze, and interpret data in order to help businesses make better decisions. This course may be useful for Data Analysts who wish to gain a deeper understanding of the fundamentals of Logistic Regression without using popular machine learning libraries such as scikit-learn and statsmodels.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, deploy, and maintain machine learning systems. They may also work on research and development for machine learning algorithms and techniques. This course may be useful for Machine Learning Engineers who wish to gain a deeper understanding of the fundamentals of Logistic Regression without using popular machine learning libraries such as scikit-learn and statsmodels.
Data Scientist
Data Scientist
Data Scientists work to combine their knowledge of math, statistics, and programming with their understanding of business and technology in order to turn raw data into information that businesses can use to make better decisions. This course may be useful for Data Scientists who wish to gain a deeper understanding of the fundamentals of Logistic Regression without using popular machine learning libraries such as scikit-learn and statsmodels.
Software Engineer
Software Engineer
Software Engineers design, build, and maintain software systems. This course may be useful for Software Engineers who wish to gain a deeper understanding of the fundamentals of Logistic Regression without using popular machine learning libraries such as scikit-learn and statsmodels.
Business Analyst
Business Analyst
Business Analysts work to understand the business needs of an organization and to develop solutions that meet those needs. This course may be useful for Business Analysts who wish to gain a deeper understanding of the fundamentals of Logistic Regression without using popular machine learning libraries such as scikit-learn and statsmodels.
Market Researcher
Market Researcher
Market Researchers collect, analyze, and interpret data in order to help businesses make better decisions. This course may be useful for Market Researchers who wish to gain a deeper understanding of the fundamentals of Logistic Regression without using popular machine learning libraries such as scikit-learn and statsmodels.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve business problems. This course may be useful for Operations Research Analysts who wish to gain a deeper understanding of the fundamentals of Logistic Regression without using popular machine learning libraries such as scikit-learn and statsmodels.
Statistician
Statistician
Statisticians collect, analyze, and interpret data in order to help businesses make better decisions. This course may be useful for Statisticians who wish to gain a deeper understanding of the fundamentals of Logistic Regression without using popular machine learning libraries such as scikit-learn and statsmodels.
Financial Analyst
Financial Analyst
Financial Analysts use mathematical and statistical models to analyze financial data and make investment decisions. This course may be useful for Financial Analysts who wish to gain a deeper understanding of the fundamentals of Logistic Regression without using popular machine learning libraries such as scikit-learn and statsmodels.
Risk Analyst
Risk Analyst
Risk Analysts use mathematical and statistical models to assess risk and uncertainty. This course may be useful for Risk Analysts who wish to gain a deeper understanding of the fundamentals of Logistic Regression without using popular machine learning libraries such as scikit-learn and statsmodels.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. This course may be useful for Quantitative Analysts who wish to gain a deeper understanding of the fundamentals of Logistic Regression without using popular machine learning libraries such as scikit-learn and statsmodels.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk and uncertainty. This course may be useful for Actuaries who wish to gain a deeper understanding of the fundamentals of Logistic Regression without using popular machine learning libraries such as scikit-learn and statsmodels.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines. This course may be useful for Data Engineers who wish to gain a deeper understanding of the fundamentals of Logistic Regression without using popular machine learning libraries such as scikit-learn and statsmodels.
Consultant
Consultant
Consultants advise businesses on how to improve their operations. This course may be useful for Consultants who wish to gain a deeper understanding of the fundamentals of Logistic Regression without using popular machine learning libraries such as scikit-learn and statsmodels.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. This course may be useful for Product Managers who wish to gain a deeper understanding of the fundamentals of Logistic Regression without using popular machine learning libraries such as scikit-learn and statsmodels.
For more career information including salaries, visit:
OpenCourser.com/course/fx675w/logistic
Reading list
We've selected 12 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 Python and Numpy.
Provides a comprehensive overview of pattern recognition and machine learning, including logistic regression. It valuable resource for understanding the theoretical foundations of logistic regression and for learning about other pattern recognition and machine learning techniques.
Provides a comprehensive overview of statistical learning, including logistic regression. It valuable resource for understanding the theoretical foundations of logistic regression and for learning about other statistical learning techniques.
Save
Provides a comprehensive overview of deep learning, including logistic regression as a fundamental building block. It valuable resource for understanding the theoretical foundations of deep learning and for learning about other deep learning techniques.
Provides a comprehensive overview of statistical learning methods, including logistic regression. It valuable resource for understanding the theoretical foundations of logistic regression and for learning about other statistical learning techniques.
Provides a comprehensive overview of reinforcement learning, including logistic regression as a fundamental building block. It valuable resource for understanding the theoretical foundations of reinforcement learning and for learning about other reinforcement learning techniques.
Provides a probabilistic perspective on machine learning, including logistic regression. It valuable resource for understanding the theoretical foundations of logistic regression and for learning about other machine learning techniques.
Provides a comprehensive overview of deep learning, including logistic regression as a fundamental building block. It valuable resource for understanding the theoretical foundations of deep learning and for learning about other deep learning techniques.
Provides a practical introduction to machine learning for hackers and data scientists. It includes a chapter on logistic regression and provides code examples that can be used to implement logistic regression models.
Provides a practical introduction to machine learning, including logistic regression. It includes code examples that can be used to implement logistic regression models and valuable resource for learning about the practical aspects of logistic regression.
Provides a hands-on introduction to machine learning using Python libraries such as scikit-learn, Keras, and TensorFlow. It includes a chapter on logistic regression and provides code examples that can be used to implement logistic regression models.
Save
Provides a practical introduction to data mining, including logistic regression. It valuable resource for learning about the tools and techniques used in data mining and for understanding how to use logistic regression in practice.
Save
Provides a practical introduction to machine learning using R, including logistic regression. It includes code examples that can be used to implement logistic regression models and valuable resource for learning about the practical aspects of logistic regression in R.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/fx675w/logistic
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Amit Yadav
Language
English
Good to know
Examines logistic regression, which is essential knowledge for working with machine learning algorithms
Develops skills for implementing machine learning algorithms from scratch, which provides a deeper understanding of machine learning
Employs a project-based approach that enables students to actively engage with the material and apply their understanding
Requires basic programming experience in Python and a fundamental understanding of machine learning theory, limiting accessibility for beginners
Focuses on logistic regression exclusively, providing a specialized perspective but potentially limiting applicability to broader machine learning tasks
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