Deep Learning Prerequisites: Logistic Regression in Python from Udemy

Ever wondered how AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion really work? In this course, you will learn the foundations of these groundbreaking applications.

This course is a lead-in to deep learning and neural networks - it covers a popular and fundamental technique used in machine learning, data science and statistics: logistic regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own logistic regression module in Python.

This course does not require any external materials. Everything needed (Python, and some Python libraries) can be obtained for free.

This course provides you with many practical examples so that you can really see how deep learning can be used on anything. Throughout the course, we'll do a course project, which will show you how to predict user actions on a website given user data like whether or not that user is on a mobile device, the number of products they viewed, how long they stayed on your site, whether or not they are a returning visitor, and what time of day they visited.

Another project at the end of the course shows you how you can use deep learning for facial expression recognition. Imagine being able to predict someone's emotions just based on a picture.

If you are a programmer and you want to enhance your coding abilities by learning about data science, then this course is for you. If you have a technical or mathematical background, and you want use your skills to make data-driven decisions and optimize your business using scientific principles, then this course is for you.

This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.

"If you can't implement it, you don't understand it"

Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".
My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch
Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?
After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...

Suggested Prerequisites:

calculus (taking derivatives)
matrix arithmetic
probability
Python coding: if/else, loops, lists, dicts, sets
Numpy coding: matrix and vector operations, loading a CSV file

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)

What's inside

Learning objectives

Program logistic regression from scratch in python
Describe how logistic regression is useful in data science
Derive the error and update rule for logistic regression
Understand how logistic regression works as an analogy for the biological neuron

Use logistic regression to solve real-world business problems like predicting user actions from e-commerce data and facial expression recognition
Understand why regularization is used in machine learning
Understand important foundations for openai chatgpt, gpt-4, dall-e, midjourney, and stable diffusion

Program logistic regression from scratch in python
Describe how logistic regression is useful in data science
Derive the error and update rule for logistic regression
Understand how logistic regression works as an analogy for the biological neuron
Use logistic regression to solve real-world business problems like predicting user actions from e-commerce data and facial expression recognition
Understand why regularization is used in machine learning
Understand important foundations for openai chatgpt, gpt-4, dall-e, midjourney, and stable diffusion

Syllabus

Start Here

This lecture will outline what will be learned in the course. I explain the importance of knowing the math, and provide short descriptions of each section later covered. Feel free to skip it and jump right into the 3rd video.

Difference between supervised and unsupervised learning. Difference between classification and regression. Feel free to skip this one if you already know this.

An easy first quiz

I discuss what linear classification is from a general standpoint, without invoking any specifics related to logistic regression. I provide a 2-dimensional binary classification example and go over how we would classify data into 1 of 3 cases: positive class, negative class, and don't know / not sure.

In this lecture I discuss a brief history of neural networks, and talk about how the characteristics of the neuron (action potential, signal propagation, inhibitory and excitatory behavior) are modeled in different ways: the Hodgkin Huxley mdoel, the FitzHugh Nagumo model, and the logistic model.

I show the feedforward calculation for the output of a logistic unit.

I show how to code the feedforward calculation for the output of a logistic unit in Python and numpy.

I show how we can solve for the weights in a logistic regression model if we make assumptions about the distributions of the input data (equal variance, Gaussian-distributed).

All these symbols can get confusing so this is a lecture to give a short and simple description of what each letter "stands for". These are important to get right now, because we'll use it for this course and every later course on deep learning and machine learning.

I show the cross-entropy error formula, and describe why this is used as an appropriate objective function for logistic regression.

I show how to calculate the cross-entropy error of your model in Python and numpy.

I show how to derive the likelihood and log-likelihood of your model and data, and I show how maximizing the likelihood is equivalent to minimizing the cross-entropy.

I explain how gradient descent can be used to solve for the minima of a function, and the algorithm that can be used to iteratively update the logistic weights.

I show how to code weight updates for logistic regression using gradient descent in Python and numpy.

I show how to apply regularization for logistic regression in Python.

I show how logistic regression can be used to solve the donut problem (where one class is circular and is inside another circular class).

This lecture describes how to apply logistic regression to the XOR problem by making it a 3-dimensional problem.

This is a clip from my natural language processing course. It shows you how to apply logistic regression to sentiment analysis - measuring how positive or negative a word is.

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Explores core fundamentals of deep learning, machine learning, and data science, which are standard in industry

Taught by Lazy Programmer Inc. instructors who are recognized for their expertise and teaching in data science and machine learning

Builds a beginner-friendly foundation in data science fundamentals and key tools like logistic regression and Python

Teaches practical skills in using and implementing logistic regression for real-world data analysis and prediction tasks

Utilizes a project-based approach that helps learners apply their knowledge and understanding of logistic regression to solve real-world problems

Emphasizes the importance of understanding the underlying mathematical concepts and principles behind logistic regression, rather than just relying on pre-built libraries and tools

Covers essential prerequisites such as calculus, matrix arithmetic, probability, and Python coding, ensuring a well-rounded understanding of the fundamentals

Explicitly requires learners to have some foundational knowledge and skills, such as calculus, matrix arithmetic, and Python coding, which may pose a barrier for complete beginners

Reviews summary

Foundational logistic regression: theory & code

According to learners, this course provides a strong theoretical foundation and teaches how to implement logistic regression from scratch in Python. Many appreciate the focus on the underlying math and the 'why' behind the algorithms, finding it a valuable approach that goes beyond simply using libraries. While the course is considered a highly effective prerequisite for further study in deep learning, some students found it quite challenging, emphasizing that solid prerequisites in calculus, linear algebra, and Python are truly necessary and not just recommended. The pace can be fast, requiring learners to spend extra time on the material, but the effort is often seen as rewarding for achieving a deep understanding.

Challenging but rewarding

"Found this course very difficult..."

"It moves quite fast, so be prepared to pause and rewatch."

"Be prepared to spend extra time on the material beyond the lectures."

"The effort is often seen as rewarding for achieving a deep understanding."

Prepares you for advanced topics

"Excellent prerequisite for more advanced deep learning topics."

"Great lead-in to NN and other advanced courses."

"Now I'm ready for the next course after taking this one."

"Really connects the dots for understanding later concepts."

Learn to build models from scratch

"My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch"

"Building the model from scratch gave me a deep understanding that using libraries never would."

"I appreciated the focus on theory and implementation."

"The from-scratch coding approach is invaluable for building intuition."

Provides deep understanding of the math

"This course was exactly what I needed. Building the model from scratch gave me a deep understanding..."

"The math explanations were clear but challenging. Highly recommended..."

"I appreciated the focus on theory and implementation. The derivations were crucial."

"Finally understand *why* it works and not just how to use a library."

Requires solid math and coding background

"Solid prereqs in calculus, linear algebra, and Python are definitely needed."

"Content is good, but it assumes a stronger math background than I had."

"Found this course very difficult... the math was overwhelming."

"The prerequisite knowledge isn't just suggested, it's mandatory."

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 Deep Learning Prerequisites: Logistic Regression in Python with these activities:

Practice Matrix Arithmetic and Vector Operations

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This is helpful because it is assumed knowledge for this course, especially in the derivations of error and update rules.

Browse courses on Matrix Arithmetic

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Review basic matrix and vector operations.
Complete practice problems involving matrix and vector operations.
Use numpy to perform matrix and vector operations.

Compile Notes, Assignments, Quizzes, and Exams

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This is helpful because it provides a comprehensive set of resources for review and reinforcement of course materials.

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Gather all notes, assignments, quizzes, and exams.
Organize the materials by topic.
Review the materials regularly.

Start an E-commerce Course Project

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This is helpful because it provides a practical application of logistic regression in a real-world scenario.

Browse courses on E-Commerce

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Gather data from an e-commerce website.
Preprocess the data and extract features.
Train a logistic regression model to predict user actions.
Interpret the model's results and make recommendations to improve the website.
Deploy the model to the website.

Five other activities

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Show all eight activities

Follow Tutorials on Gradient Descent and Logistic Regression

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This is helpful because it provides a deeper understanding of the algorithms and techniques used in logistic regression.

Browse courses on Gradient Descent

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Find tutorials on gradient descent and logistic regression.
Follow the tutorials and implement the algorithms in Python or another programming language.
Experiment with different parameters and datasets to see how they affect the model's performance.

Participate in Peer Practice Sessions

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This is helpful because it provides an opportunity to discuss course materials, ask questions, and work through problems with other students.

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Find a peer group or study buddy.
Meet regularly to discuss course materials and work on problems together.
Share knowledge and resources with each other.

Practice Implementing Logistic Regression from Scratch

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This is helpful because it builds a deeper understanding of the implementation details of logistic regression.

Browse courses on Logistic Regression

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Review the theory behind logistic regression.
Write code to implement the forward pass and backward pass of logistic regression.
Use your code to train a logistic regression model on a dataset.

Start a Facial Expression Recognition Project

Show steps

This is helpful because it gives you hands-on experience with a real-world application of logistic regression.

Browse courses on Facial Expression Recognition

Show steps

Gather a dataset of facial images with labeled expressions.
Preprocess the images and extract features.
Train a logistic regression model to classify the facial expressions.
Evaluate the model's performance.
Deploy the model to a web application or mobile app.

Gather and Compile Resources on Logistics Regression and AI

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This is helpful because it provides a centralized repository of information on these topics.

Browse courses on AI

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Gather articles, tutorials, and videos on logistic regression and AI.
Organize the resources by topic.
Share the resources with other students or post them online.

Career center

Learners who complete Deep Learning Prerequisites: Logistic Regression in Python will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists are responsible for collecting, cleaning, and analyzing data to extract meaningful insights. This course provides a solid foundation in logistic regression, a fundamental technique used in data science. By understanding how logistic regression works, Data Scientists can apply it to solve real-world problems, such as predicting user actions, classifying facial expressions, and optimizing business decisions.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers design, develop, and implement machine learning models. This course provides a comprehensive overview of logistic regression, which is a popular machine learning algorithm used for classification tasks. Machine Learning Engineers can use this knowledge to build robust and accurate machine learning models for various applications.

See salaries and explore the career path for Machine Learning Engineer

Statistician

Statisticians collect, analyze, interpret, and present data. This course provides a strong foundation in logistic regression, a statistical technique used to model the relationship between independent variables and a binary dependent variable. By understanding logistic regression, Statisticians can gain valuable insights from data and make informed decisions based on statistical evidence.

See salaries and explore the career path for Statistician

Business Analyst

Business Analysts use data to identify problems and opportunities within an organization. This course provides a practical understanding of logistic regression, a technique commonly used to predict outcomes based on a set of input variables. Business Analysts can leverage this knowledge to develop data-driven strategies, optimize operations, and make informed business decisions.

See salaries and explore the career path for Business Analyst

Data Analyst

Data Analysts collect, clean, and analyze data to uncover trends and patterns. This course provides a solid foundation in logistic regression, a powerful technique used to predict outcomes based on a set of input variables. By understanding logistic regression, Data Analysts can gain valuable insights from data and make data-driven decisions.

See salaries and explore the career path for Data Analyst

Software Engineer

Software Engineers design, develop, and maintain software systems. This course provides a practical understanding of logistic regression, a technique used to build predictive models. Software Engineers can use this knowledge to develop software applications that can make predictions based on data, such as spam filters or fraud detection systems.

See salaries and explore the career path for Software Engineer

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data. This course provides a strong foundation in logistic regression, a technique commonly used to predict financial outcomes. Quantitative Analysts can apply logistic regression to develop trading strategies, assess risk, and make informed investment decisions.

See salaries and explore the career path for Quantitative Analyst

Operations Research Analyst

Operations Research Analysts use mathematical and analytical techniques to optimize business operations. This course provides a practical understanding of logistic regression, a technique used to model complex systems. Operations Research Analysts can apply logistic regression to improve supply chain management, logistics, and other operational processes.

See salaries and explore the career path for Operations Research Analyst

Market Researcher

Market Researchers collect and analyze data to understand consumer behavior. This course provides a strong foundation in logistic regression, a technique used to predict consumer preferences and choices. Market Researchers can apply logistic regression to develop targeted marketing campaigns, optimize product offerings, and make informed business decisions.

See salaries and explore the career path for Market Researcher

Biostatistician

Biostatisticians apply statistical methods to medical data. This course provides a practical understanding of logistic regression, a technique used to predict health outcomes based on patient data. Biostatisticians can use logistic regression to develop diagnostic tests, evaluate the effectiveness of treatments, and make informed decisions about public health policy.

See salaries and explore the career path for Biostatistician

Epidemiologist

Epidemiologists investigate the causes and spread of diseases. This course provides a solid foundation in logistic regression, a technique used to model the relationship between risk factors and disease outcomes. Epidemiologists can apply logistic regression to identify risk factors for diseases, develop prevention strategies, and track the spread of epidemics.

See salaries and explore the career path for Epidemiologist

Actuary

Actuaries assess the financial risks associated with insurance policies. This course provides a practical understanding of logistic regression, a technique used to predict the probability of future events. Actuaries can use logistic regression to price insurance policies, manage risk, and make informed decisions about insurance products.

See salaries and explore the career path for Actuary

Credit Analyst

Credit Analysts assess the creditworthiness of borrowers. This course provides a solid foundation in logistic regression, a technique used to predict the likelihood of loan default. Credit Analysts can apply logistic regression to evaluate loan applications, manage risk, and make informed lending decisions.

See salaries and explore the career path for Credit Analyst

Insurance Underwriter

Insurance Underwriters assess the risk associated with insurance policies. This course provides a practical understanding of logistic regression, a technique used to predict the probability of future events. Insurance Underwriters can use logistic regression to evaluate insurance applications, manage risk, and make informed decisions about insurance policies.

See salaries and explore the career path for Insurance Underwriter

Risk Manager

Risk Managers identify and manage risks within organizations. This course provides a solid foundation in logistic regression, a technique used to predict the probability of future events. Risk Managers can apply logistic regression to assess risks, develop mitigation strategies, and make informed decisions about risk management.

See salaries and explore the career path for Risk Manager