Logistic Regression with Python and Numpy from Coursera

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.

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

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

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

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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

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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

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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

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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

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Identify experts in the field of machine learning, particularly those with experience in logistic regression. Reach out to them to request guidance.

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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

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Explore Scikit-Learn

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Follow tutorials on Scikit-Learn to learn about its features and how to apply them to machine learning tasks, including logistic regression.

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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

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Complete coding exercises in NumPy to solidify your understanding of the library's functions and capabilities.

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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

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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

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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

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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

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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

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Participate in open-source projects related to machine learning or logistic regression. This will allow you to apply your skills and gain valuable experience.

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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 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.

See salaries and explore the career path for Data Analyst

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.

See salaries and explore the career path for Machine Learning Engineer

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.

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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.

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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.

See salaries and explore the career path for Business Analyst

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.

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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.

See salaries and explore the career path for Operations Research Analyst

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.

See salaries and explore the career path for Statistician

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.

See salaries and explore the career path for Financial 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.

See salaries and explore the career path for Risk 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.

See salaries and explore the career path for Quantitative Analyst

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.

See salaries and explore the career path for Actuary

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.

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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.

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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.

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