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Lazy Programmer Inc.

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.

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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)

Enroll now

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

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.

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How to Succeed in this Course
Statistics vs. Machine Learning

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

Introduction to the E-Commerce Course Project

An easy first quiz

Describe how a linear classifier works and understand the biological inspiration behind neural networks and logistic regression.

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.

Interpretation of Logistic Regression Output
E-Commerce Course Project: Pre-Processing the Data
E-Commerce Course Project: Making Predictions
Feedforward Quiz
Prediction Section Summary
Suggestion Box
Derive and program different methods of calculating the weights of a logistic regression model.
Training Section Introduction

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.

Visualizing the linear discriminant / Bayes classifier / Gaussian clouds

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.

E-Commerce Course Project: Training the Logistic Model
Training Section Summary
Know how to apply logistic regression to real-world problems and some classic non-linearly separable problems.
Practical Section Introduction
Interpreting the Weights
L2 Regularization - Theory

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

L1 Regularization - Theory
L1 Regularization - Code
L1 vs L2 Regularization

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.

Why Divide by Square Root of D?
Practical Section Summary
Checkpoint and applications: How to make sure you know your stuff

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.

BONUS: Exercises + how to get good at this
Apply logistic regression to a real problem - classifying facial expressions
Facial Expression Recognition Project Introduction
Facial Expression Recognition Problem Description
The class imbalance problem
Utilities walkthrough
Facial Expression Recognition in Code
Facial Expression Recognition Project Summary
Background Review
Gradient Descent Tutorial
Setting Up Your Environment (FAQ by Student Request)
Pre-Installation Check
Anaconda Environment Setup
How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
Extra Help With Python Coding for Beginners (FAQ by Student Request)
How to Uncompress a .tar.gz file
How to Code by Yourself (part 1)
How to Code by Yourself (part 2)
Proof that using Jupyter Notebook is the same as not using it
Python 2 vs Python 3
Effective Learning Strategies for Machine Learning (FAQ by Student Request)
How to Succeed in this Course (Long Version)
Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
Machine Learning and AI Prerequisite Roadmap (pt 1)
Machine Learning and AI Prerequisite Roadmap (pt 2)
Misc. topics tangentially related to the course that may help you with the course materials
What is the Appendix?
BONUS

Good to know

Know what's good
, what to watch for
, 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

Save this course

Save Deep Learning Prerequisites: Logistic Regression in Python to your list so you can find it easily later:
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Reviews summary

Well-structured course

Learners say that this course is well-structured and helpful. The instructor is knowledgeable and supportive, but some learners may need to supplement their learning with additional resources to gain a deeper understanding of certain topics.
Instructor is knowledgeable and supportive.
"The instructor here knows how to help us learn and he structured the course accordingly."
"Actually I needed a little more help in understanding Baye's rule and MLE in general, in depth."
"Great Job!!! and Great respect to the instructor."
May need to supplement with additional resources for deeper understanding of certain topics.
"Actually I needed a little more help in understanding Baye's rule and MLE in general, in depth."
"Actually I needed a little more help in understanding Baye's rule and MLE in general, in depth."

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
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
Show steps
  • 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
This is helpful because it provides a comprehensive set of resources for review and reinforcement of course materials.
Show steps
  • Gather all notes, assignments, quizzes, and exams.
  • Organize the materials by topic.
  • Review the materials regularly.
Start an E-commerce Course Project
This is helpful because it provides a practical application of logistic regression in a real-world scenario.
Browse courses on E-Commerce
Show steps
  • 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
Expand to see all activities and additional details
Show all eight activities
Follow Tutorials on Gradient Descent and Logistic Regression
This is helpful because it provides a deeper understanding of the algorithms and techniques used in logistic regression.
Browse courses on Gradient Descent
Show steps
  • 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
This is helpful because it provides an opportunity to discuss course materials, ask questions, and work through problems with other students.
Show steps
  • 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
This is helpful because it builds a deeper understanding of the implementation details of logistic regression.
Browse courses on Logistic Regression
Show steps
  • 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
This is helpful because it gives you hands-on experience with a real-world application of logistic regression.
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
This is helpful because it provides a centralized repository of information on these topics.
Browse courses on AI
Show steps
  • 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.

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 Deep Learning Prerequisites: Logistic Regression in Python.
Provides a comprehensive overview of statistical learning methods, including logistic regression. It valuable resource for anyone who wants to learn about the theory and practice of statistical learning.
Provides a comprehensive overview of logistic regression, including its theory, applications, and interpretation. It valuable resource for anyone who wants to learn about logistic regression.
Provides a comprehensive overview of probabilistic graphical models, including their theory and applications. It valuable resource for anyone who wants to learn about probabilistic graphical models.
Provides a comprehensive overview of machine learning from a probabilistic perspective, including its theory and applications. It valuable resource for anyone who wants to learn about machine learning from a probabilistic perspective.
Provides a comprehensive introduction to statistical learning methods, including logistic regression, with a focus on applications in R. It valuable resource for anyone who wants to learn about the theory and practice of statistical learning.
Provides a comprehensive overview of deep learning, including its theory and applications. It valuable resource for anyone who wants to learn about deep learning.
Provides a comprehensive introduction to machine learning, with a focus on using Python to build and deploy machine learning models. It valuable resource for anyone who wants to learn about machine learning in a hands-on way.
Provides a comprehensive overview of logistic regression, with a focus on applications in the health sciences. It valuable resource for anyone who wants to learn about logistic regression in the context of health research.
Provides a comprehensive overview of regression modeling techniques, with a focus on applications in actuarial and financial settings. It covers a wide range of topics, including linear regression, logistic regression, and generalized linear models.
Provides a comprehensive overview of reinforcement learning, including its theory and applications. It valuable resource for anyone who wants to learn about reinforcement learning.
Provides a comprehensive overview of Bayesian reasoning and machine learning, including its theory and applications. It valuable resource for anyone who wants to learn about Bayesian reasoning and machine learning.
Provides a self-paced introduction to logistic regression, with a focus on understanding the concepts and applications of the technique. It valuable resource for anyone who wants to learn about logistic regression.
Provides a practical introduction to machine learning, with a focus on using Python to build and deploy machine learning models. It valuable resource for anyone who wants to learn about machine learning in a hands-on way.

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