Deep Learning Prerequisites: Logistic Regression in Python

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  • Study type: Online
  • Starts: Anytime
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Course Description

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)

Who this course is for:

  • Adult learners who want to get into the field of data science and big data
  • Students who are thinking of pursuing machine learning or data science
  • Students who are tired of boring traditional statistics and prewritten functions in R, and want to learn how things really work by implementing them in Python
  • People who know some machine learning but want to be able to relate it to artificial intelligence
  • People who are interested in bridging the gap between computational neuroscience and machine learning

Instructor

Artificial intelligence and machine learning engineer
  • 4.6 Instructor Rating
  • 115,455 Reviews
  • 439,048 Students
  • 29 Courses

Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer.

I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition.

Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark.

I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text.

My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing.

I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School.

Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more.

Expected Outcomes

  1. 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 Course content 11 sections • 59 lectures • 6h 16m total length Expand all sections Start Here 5 lectures • 34min Introduction and Outline Preview 07:22 How to Succeed in this Course 05:51 Statistics vs. Machine Learning 09:58 Review of the classification problem 01:53 Introduction to the E-Commerce Course Project 08:52 Easy first quiz 1 question Basics: What is linear classification? What's the relation to neural networks? 10 lectures • 37min Linear Classification 04:49 Biological inspiration - the neuron 03:36 How do we calculate the output of a neuron / logistic classifier? - Theory 04:18 How do we calculate the output of a neuron / logistic classifier? - Code 04:30 Interpretation of Logistic Regression Output 05:32 E-Commerce Course Project: Pre-Processing the Data 05:24 E-Commerce Course Project: Making Predictions 03:00 Feedforward Quiz 01:24 Prediction Section Summary 01:11 Suggestion Box 03:03 Solving for the optimal weights 11 lectures • 46min Training Section Introduction 01:38 A closed-form solution to the Bayes classifier 05:59 What do all these symbols mean? X, Y, N, D, L, J, P(Y=1|X), etc. 03:37 The cross-entropy error function - Theory 02:46 The cross-entropy error function - Code 04:53 Visualizing the linear discriminant / Bayes classifier / Gaussian clouds 02:28 Maximizing the likelihood 06:34 Updating the weights using gradient descent - Theory 06:20 Updating the weights using gradient descent - Code 03:09 E-Commerce Course Project: Training the Logistic Model 06:47 Training Section Summary 02:02 Practical concerns 11 lectures • 54min Practical Section Introduction 02:45 Interpreting the Weights 04:07 L2 Regularization - Theory 08:38 L2 Regularization - Code 01:43 L1 Regularization - Theory 02:53 L1 Regularization - Code 06:13 L1 vs L2 Regularization 03:05 The donut problem 10:01 The XOR problem 06:12 Why Divide by Square Root of D? 06:32 Practical Section Summary 02:02 Checkpoint and applications: How to make sure you know your stuff 2 lectures • 8min BONUS: Sentiment Analysis 05:13 BONUS: Exercises + how to get good at this 02:48 Project: Facial Expression Recognition 6 lectures • 41min Facial Expression Recognition Project Introduction 04:51 Facial Expression Recognition Problem Description 12:21 The class imbalance problem 06:01 Utilities walkthrough 05:45 Facial Expression Recognition in Code 10:41 Facial Expression Recognition Project Summary 01:20 Background Review 1 lecture • 5min Gradient Descent Tutorial 04:30 Setting Up Your Environment (FAQ by Student Request) 2 lectures • 38min Anaconda Environment Setup 20:20 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow 17:32 Extra Help With Python Coding for Beginners (FAQ by Student Request) 5 lectures • 46min How to Uncompress a .tar.gz file 03:18 How to Code by Yourself (part 1) 15:54 How to Code by Yourself (part 2) 09:23 Proof that using Jupyter Notebook is the same as not using it 12:29 Python 2 vs Python 3 04:38 Effective Learning Strategies for Machine Learning (FAQ by Student Request) 4 lectures • 1hr How to Succeed in this Course (Long Version) 10:24 Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced? 22:04 Machine Learning and AI Prerequisite Roadmap (pt 1) 11:18 Machine Learning and AI Prerequisite Roadmap (pt 2) 16:07 1 more section Requirements Derivatives, matrix arithmetic, probability You should know some basic Python coding with the Numpy Stack Description 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) Who this course is for: Adult learners who want to get into the field of data science and big data Students who are thinking of pursuing machine learning or data science Students who are tired of boring traditional statistics and prewritten functions in R, and want to learn how things really work by implementing them in Python People who know some machine learning but want to be able to relate it to artificial intelligence People who are interested in bridging the gap between computational neuroscience and machine learning Show more Show less Featured review Craig Endert 182 courses 31 reviews Rating: 5.0 out of 5 8 months ago As usual, this professor knocked it out of the park. His mathematical representations of the content without using scikit learn made it possible to understand what it going on behind the curtain. Highly recommend this professor and his content; I will be buying more courses from him. Show more Show less Instructor Lazy Programmer Inc. Artificial intelligence and machine learning engineer 4.6 Instructor Rating 115,455 Reviews 439,048 Students 29 Courses Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer. I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition. Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark. I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text. My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing. I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more. Show more Show less Udemy Business Teach on Udemy Get the app About us Contact us Careers Blog Help and Support Affiliate Impressum Kontakt Terms Privacy policy Cookie settings Sitemap © 2021 Udemy, Inc. window.handleCSSToggleButtonClick = function (event) { var target = event.currentTarget; var cssToggleId = target && target.dataset && target.dataset.cssToggleId; var input = cssToggleId && document.getElementById(cssToggleId); if (input) { if (input.dataset.type === 'checkbox') { input.dataset.checked = input.dataset.checked ? '' : 'checked'; } else { input.dataset.checked = input.dataset.allowToggle && input.dataset.checked ? '' : 'checked'; var radios = document.querySelectorAll('[name="' + input.dataset.name + '"]'); for (var i = 0; i (function(){window['__CF$cv$params']={r:'6777105b6b84424b',m:'a580020862c775d1f45eee83ae92147075c8adfc-1627736995-1800-AWCOnP1skesLmeQAdsH/5dHULrOs7UULD0JcYyi9ympi+1D+8CUTAPYB4+dGHnZK56KxWV9rvhtdI5Pqr8ZCCS2cablcsNAYtARK5oTnkZ7sQhgOrhxrZ9ceUclGZPRRdMh4Bb0BVgBMLOtUxyvTokX6JqpO3FdqUbM8qjylSwXGWW6guynHMFbO9qeCs6HuYA==',s:[0x49e64e845a,0xe6b64ad696],}})();
  2. 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 Course content 11 sections • 59 lectures • 6h 16m total length Expand all sections Start Here 5 lectures • 34min Introduction and Outline Preview 07:22 How to Succeed in this Course 05:51 Statistics vs. Machine Learning 09:58 Review of the classification problem 01:53 Introduction to the E-Commerce Course Project 08:52 Easy first quiz 1 question Basics: What is linear classification? What's the relation to neural networks? 10 lectures • 37min Linear Classification 04:49 Biological inspiration - the neuron 03:36 How do we calculate the output of a neuron / logistic classifier? - Theory 04:18 How do we calculate the output of a neuron / logistic classifier? - Code 04:30 Interpretation of Logistic Regression Output 05:32 E-Commerce Course Project: Pre-Processing the Data 05:24 E-Commerce Course Project: Making Predictions 03:00 Feedforward Quiz 01:24 Prediction Section Summary 01:11 Suggestion Box 03:03 Solving for the optimal weights 11 lectures • 46min Training Section Introduction 01:38 A closed-form solution to the Bayes classifier 05:59 What do all these symbols mean? X, Y, N, D, L, J, P(Y=1|X), etc. 03:37 The cross-entropy error function - Theory 02:46 The cross-entropy error function - Code 04:53 Visualizing the linear discriminant / Bayes classifier / Gaussian clouds 02:28 Maximizing the likelihood 06:34 Updating the weights using gradient descent - Theory 06:20 Updating the weights using gradient descent - Code 03:09 E-Commerce Course Project: Training the Logistic Model 06:47 Training Section Summary 02:02 Practical concerns 11 lectures • 54min Practical Section Introduction 02:45 Interpreting the Weights 04:07 L2 Regularization - Theory 08:38 L2 Regularization - Code 01:43 L1 Regularization - Theory 02:53 L1 Regularization - Code 06:13 L1 vs L2 Regularization 03:05 The donut problem 10:01 The XOR problem 06:12 Why Divide by Square Root of D? 06:32 Practical Section Summary 02:02 Checkpoint and applications: How to make sure you know your stuff 2 lectures • 8min BONUS: Sentiment Analysis 05:13 BONUS: Exercises + how to get good at this 02:48 Project: Facial Expression Recognition 6 lectures • 41min Facial Expression Recognition Project Introduction 04:51 Facial Expression Recognition Problem Description 12:21 The class imbalance problem 06:01 Utilities walkthrough 05:45 Facial Expression Recognition in Code 10:41 Facial Expression Recognition Project Summary 01:20 Background Review 1 lecture • 5min Gradient Descent Tutorial 04:30 Setting Up Your Environment (FAQ by Student Request) 2 lectures • 38min Anaconda Environment Setup 20:20 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow 17:32 Extra Help With Python Coding for Beginners (FAQ by Student Request) 5 lectures • 46min How to Uncompress a .tar.gz file 03:18 How to Code by Yourself (part 1) 15:54 How to Code by Yourself (part 2) 09:23 Proof that using Jupyter Notebook is the same as not using it 12:29 Python 2 vs Python 3 04:38 Effective Learning Strategies for Machine Learning (FAQ by Student Request) 4 lectures • 1hr How to Succeed in this Course (Long Version) 10:24 Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced? 22:04 Machine Learning and AI Prerequisite Roadmap (pt 1) 11:18 Machine Learning and AI Prerequisite Roadmap (pt 2) 16:07 1 more section Requirements Derivatives, matrix arithmetic, probability You should know some basic Python coding with the Numpy Stack Description 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) Who this course is for: Adult learners who want to get into the field of data science and big data Students who are thinking of pursuing machine learning or data science Students who are tired of boring traditional statistics and prewritten functions in R, and want to learn how things really work by implementing them in Python People who know some machine learning but want to be able to relate it to artificial intelligence People who are interested in bridging the gap between computational neuroscience and machine learning Show more Show less Featured review Craig Endert 182 courses 31 reviews Rating: 5.0 out of 5 8 months ago As usual, this professor knocked it out of the park. His mathematical representations of the content without using scikit learn made it possible to understand what it going on behind the curtain. Highly recommend this professor and his content; I will be buying more courses from him. Show more Show less Instructor Lazy Programmer Inc. Artificial intelligence and machine learning engineer 4.6 Instructor Rating 115,455 Reviews 439,048 Students 29 Courses Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer. I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition. Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark. I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text. My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing. I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more. Show more Show less Udemy Business Teach on Udemy Get the app About us Contact us Careers Blog Help and Support Affiliate Impressum Kontakt Terms Privacy policy Cookie settings Sitemap © 2021 Udemy, Inc. window.handleCSSToggleButtonClick = function (event) { var target = event.currentTarget; var cssToggleId = target && target.dataset && target.dataset.cssToggleId; var input = cssToggleId && document.getElementById(cssToggleId); if (input) { if (input.dataset.type === 'checkbox') { input.dataset.checked = input.dataset.checked ? '' : 'checked'; } else { input.dataset.checked = input.dataset.allowToggle && input.dataset.checked ? '' : 'checked'; var radios = document.querySelectorAll('[name="' + input.dataset.name + '"]'); for (var i = 0; i (function(){window['__CF$cv$params']={r:'6777105b6b84424b',m:'a580020862c775d1f45eee83ae92147075c8adfc-1627736995-1800-AWCOnP1skesLmeQAdsH/5dHULrOs7UULD0JcYyi9ympi+1D+8CUTAPYB4+dGHnZK56KxWV9rvhtdI5Pqr8ZCCS2cablcsNAYtARK5oTnkZ7sQhgOrhxrZ9ceUclGZPRRdMh4Bb0BVgBMLOtUxyvTokX6JqpO3FdqUbM8qjylSwXGWW6guynHMFbO9qeCs6HuYA==',s:[0x49e64e845a,0xe6b64ad696],}})();
  3. 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 Course content 11 sections • 59 lectures • 6h 16m total length Expand all sections Start Here 5 lectures • 34min Introduction and Outline Preview 07:22 How to Succeed in this Course 05:51 Statistics vs. Machine Learning 09:58 Review of the classification problem 01:53 Introduction to the E-Commerce Course Project 08:52 Easy first quiz 1 question Basics: What is linear classification? What's the relation to neural networks? 10 lectures • 37min Linear Classification 04:49 Biological inspiration - the neuron 03:36 How do we calculate the output of a neuron / logistic classifier? - Theory 04:18 How do we calculate the output of a neuron / logistic classifier? - Code 04:30 Interpretation of Logistic Regression Output 05:32 E-Commerce Course Project: Pre-Processing the Data 05:24 E-Commerce Course Project: Making Predictions 03:00 Feedforward Quiz 01:24 Prediction Section Summary 01:11 Suggestion Box 03:03 Solving for the optimal weights 11 lectures • 46min Training Section Introduction 01:38 A closed-form solution to the Bayes classifier 05:59 What do all these symbols mean? X, Y, N, D, L, J, P(Y=1|X), etc. 03:37 The cross-entropy error function - Theory 02:46 The cross-entropy error function - Code 04:53 Visualizing the linear discriminant / Bayes classifier / Gaussian clouds 02:28 Maximizing the likelihood 06:34 Updating the weights using gradient descent - Theory 06:20 Updating the weights using gradient descent - Code 03:09 E-Commerce Course Project: Training the Logistic Model 06:47 Training Section Summary 02:02 Practical concerns 11 lectures • 54min Practical Section Introduction 02:45 Interpreting the Weights 04:07 L2 Regularization - Theory 08:38 L2 Regularization - Code 01:43 L1 Regularization - Theory 02:53 L1 Regularization - Code 06:13 L1 vs L2 Regularization 03:05 The donut problem 10:01 The XOR problem 06:12 Why Divide by Square Root of D? 06:32 Practical Section Summary 02:02 Checkpoint and applications: How to make sure you know your stuff 2 lectures • 8min BONUS: Sentiment Analysis 05:13 BONUS: Exercises + how to get good at this 02:48 Project: Facial Expression Recognition 6 lectures • 41min Facial Expression Recognition Project Introduction 04:51 Facial Expression Recognition Problem Description 12:21 The class imbalance problem 06:01 Utilities walkthrough 05:45 Facial Expression Recognition in Code 10:41 Facial Expression Recognition Project Summary 01:20 Background Review 1 lecture • 5min Gradient Descent Tutorial 04:30 Setting Up Your Environment (FAQ by Student Request) 2 lectures • 38min Anaconda Environment Setup 20:20 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow 17:32 Extra Help With Python Coding for Beginners (FAQ by Student Request) 5 lectures • 46min How to Uncompress a .tar.gz file 03:18 How to Code by Yourself (part 1) 15:54 How to Code by Yourself (part 2) 09:23 Proof that using Jupyter Notebook is the same as not using it 12:29 Python 2 vs Python 3 04:38 Effective Learning Strategies for Machine Learning (FAQ by Student Request) 4 lectures • 1hr How to Succeed in this Course (Long Version) 10:24 Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced? 22:04 Machine Learning and AI Prerequisite Roadmap (pt 1) 11:18 Machine Learning and AI Prerequisite Roadmap (pt 2) 16:07 1 more section Requirements Derivatives, matrix arithmetic, probability You should know some basic Python coding with the Numpy Stack Description 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) Who this course is for: Adult learners who want to get into the field of data science and big data Students who are thinking of pursuing machine learning or data science Students who are tired of boring traditional statistics and prewritten functions in R, and want to learn how things really work by implementing them in Python People who know some machine learning but want to be able to relate it to artificial intelligence People who are interested in bridging the gap between computational neuroscience and machine learning Show more Show less Featured review Craig Endert 182 courses 31 reviews Rating: 5.0 out of 5 8 months ago As usual, this professor knocked it out of the park. His mathematical representations of the content without using scikit learn made it possible to understand what it going on behind the curtain. Highly recommend this professor and his content; I will be buying more courses from him. Show more Show less Instructor Lazy Programmer Inc. Artificial intelligence and machine learning engineer 4.6 Instructor Rating 115,455 Reviews 439,048 Students 29 Courses Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer. I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition. Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark. I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text. My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing. I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more. Show more Show less Udemy Business Teach on Udemy Get the app About us Contact us Careers Blog Help and Support Affiliate Impressum Kontakt Terms Privacy policy Cookie settings Sitemap © 2021 Udemy, Inc. window.handleCSSToggleButtonClick = function (event) { var target = event.currentTarget; var cssToggleId = target && target.dataset && target.dataset.cssToggleId; var input = cssToggleId && document.getElementById(cssToggleId); if (input) { if (input.dataset.type === 'checkbox') { input.dataset.checked = input.dataset.checked ? '' : 'checked'; } else { input.dataset.checked = input.dataset.allowToggle && input.dataset.checked ? '' : 'checked'; var radios = document.querySelectorAll('[name="' + input.dataset.name + '"]'); for (var i = 0; i (function(){window['__CF$cv$params']={r:'6777105b6b84424b',m:'a580020862c775d1f45eee83ae92147075c8adfc-1627736995-1800-AWCOnP1skesLmeQAdsH/5dHULrOs7UULD0JcYyi9ympi+1D+8CUTAPYB4+dGHnZK56KxWV9rvhtdI5Pqr8ZCCS2cablcsNAYtARK5oTnkZ7sQhgOrhxrZ9ceUclGZPRRdMh4Bb0BVgBMLOtUxyvTokX6JqpO3FdqUbM8qjylSwXGWW6guynHMFbO9qeCs6HuYA==',s:[0x49e64e845a,0xe6b64ad696],}})();
  4. 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 Course content 11 sections • 59 lectures • 6h 16m total length Expand all sections Start Here 5 lectures • 34min Introduction and Outline Preview 07:22 How to Succeed in this Course 05:51 Statistics vs. Machine Learning 09:58 Review of the classification problem 01:53 Introduction to the E-Commerce Course Project 08:52 Easy first quiz 1 question Basics: What is linear classification? What's the relation to neural networks? 10 lectures • 37min Linear Classification 04:49 Biological inspiration - the neuron 03:36 How do we calculate the output of a neuron / logistic classifier? - Theory 04:18 How do we calculate the output of a neuron / logistic classifier? - Code 04:30 Interpretation of Logistic Regression Output 05:32 E-Commerce Course Project: Pre-Processing the Data 05:24 E-Commerce Course Project: Making Predictions 03:00 Feedforward Quiz 01:24 Prediction Section Summary 01:11 Suggestion Box 03:03 Solving for the optimal weights 11 lectures • 46min Training Section Introduction 01:38 A closed-form solution to the Bayes classifier 05:59 What do all these symbols mean? X, Y, N, D, L, J, P(Y=1|X), etc. 03:37 The cross-entropy error function - Theory 02:46 The cross-entropy error function - Code 04:53 Visualizing the linear discriminant / Bayes classifier / Gaussian clouds 02:28 Maximizing the likelihood 06:34 Updating the weights using gradient descent - Theory 06:20 Updating the weights using gradient descent - Code 03:09 E-Commerce Course Project: Training the Logistic Model 06:47 Training Section Summary 02:02 Practical concerns 11 lectures • 54min Practical Section Introduction 02:45 Interpreting the Weights 04:07 L2 Regularization - Theory 08:38 L2 Regularization - Code 01:43 L1 Regularization - Theory 02:53 L1 Regularization - Code 06:13 L1 vs L2 Regularization 03:05 The donut problem 10:01 The XOR problem 06:12 Why Divide by Square Root of D? 06:32 Practical Section Summary 02:02 Checkpoint and applications: How to make sure you know your stuff 2 lectures • 8min BONUS: Sentiment Analysis 05:13 BONUS: Exercises + how to get good at this 02:48 Project: Facial Expression Recognition 6 lectures • 41min Facial Expression Recognition Project Introduction 04:51 Facial Expression Recognition Problem Description 12:21 The class imbalance problem 06:01 Utilities walkthrough 05:45 Facial Expression Recognition in Code 10:41 Facial Expression Recognition Project Summary 01:20 Background Review 1 lecture • 5min Gradient Descent Tutorial 04:30 Setting Up Your Environment (FAQ by Student Request) 2 lectures • 38min Anaconda Environment Setup 20:20 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow 17:32 Extra Help With Python Coding for Beginners (FAQ by Student Request) 5 lectures • 46min How to Uncompress a .tar.gz file 03:18 How to Code by Yourself (part 1) 15:54 How to Code by Yourself (part 2) 09:23 Proof that using Jupyter Notebook is the same as not using it 12:29 Python 2 vs Python 3 04:38 Effective Learning Strategies for Machine Learning (FAQ by Student Request) 4 lectures • 1hr How to Succeed in this Course (Long Version) 10:24 Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced? 22:04 Machine Learning and AI Prerequisite Roadmap (pt 1) 11:18 Machine Learning and AI Prerequisite Roadmap (pt 2) 16:07 1 more section Requirements Derivatives, matrix arithmetic, probability You should know some basic Python coding with the Numpy Stack Description 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) Who this course is for: Adult learners who want to get into the field of data science and big data Students who are thinking of pursuing machine learning or data science Students who are tired of boring traditional statistics and prewritten functions in R, and want to learn how things really work by implementing them in Python People who know some machine learning but want to be able to relate it to artificial intelligence People who are interested in bridging the gap between computational neuroscience and machine learning Show more Show less Featured review Craig Endert 182 courses 31 reviews Rating: 5.0 out of 5 8 months ago As usual, this professor knocked it out of the park. His mathematical representations of the content without using scikit learn made it possible to understand what it going on behind the curtain. Highly recommend this professor and his content; I will be buying more courses from him. Show more Show less Instructor Lazy Programmer Inc. Artificial intelligence and machine learning engineer 4.6 Instructor Rating 115,455 Reviews 439,048 Students 29 Courses Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer. I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition. Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark. I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text. My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing. I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more. Show more Show less Udemy Business Teach on Udemy Get the app About us Contact us Careers Blog Help and Support Affiliate Impressum Kontakt Terms Privacy policy Cookie settings Sitemap © 2021 Udemy, Inc. window.handleCSSToggleButtonClick = function (event) { var target = event.currentTarget; var cssToggleId = target && target.dataset && target.dataset.cssToggleId; var input = cssToggleId && document.getElementById(cssToggleId); if (input) { if (input.dataset.type === 'checkbox') { input.dataset.checked = input.dataset.checked ? '' : 'checked'; } else { input.dataset.checked = input.dataset.allowToggle && input.dataset.checked ? '' : 'checked'; var radios = document.querySelectorAll('[name="' + input.dataset.name + '"]'); for (var i = 0; i (function(){window['__CF$cv$params']={r:'6777105b6b84424b',m:'a580020862c775d1f45eee83ae92147075c8adfc-1627736995-1800-AWCOnP1skesLmeQAdsH/5dHULrOs7UULD0JcYyi9ympi+1D+8CUTAPYB4+dGHnZK56KxWV9rvhtdI5Pqr8ZCCS2cablcsNAYtARK5oTnkZ7sQhgOrhxrZ9ceUclGZPRRdMh4Bb0BVgBMLOtUxyvTokX6JqpO3FdqUbM8qjylSwXGWW6guynHMFbO9qeCs6HuYA==',s:[0x49e64e845a,0xe6b64ad696],}})();
  5. 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 Course content 11 sections • 59 lectures • 6h 16m total length Expand all sections Start Here 5 lectures • 34min Introduction and Outline Preview 07:22 How to Succeed in this Course 05:51 Statistics vs. Machine Learning 09:58 Review of the classification problem 01:53 Introduction to the E-Commerce Course Project 08:52 Easy first quiz 1 question Basics: What is linear classification? What's the relation to neural networks? 10 lectures • 37min Linear Classification 04:49 Biological inspiration - the neuron 03:36 How do we calculate the output of a neuron / logistic classifier? - Theory 04:18 How do we calculate the output of a neuron / logistic classifier? - Code 04:30 Interpretation of Logistic Regression Output 05:32 E-Commerce Course Project: Pre-Processing the Data 05:24 E-Commerce Course Project: Making Predictions 03:00 Feedforward Quiz 01:24 Prediction Section Summary 01:11 Suggestion Box 03:03 Solving for the optimal weights 11 lectures • 46min Training Section Introduction 01:38 A closed-form solution to the Bayes classifier 05:59 What do all these symbols mean? X, Y, N, D, L, J, P(Y=1|X), etc. 03:37 The cross-entropy error function - Theory 02:46 The cross-entropy error function - Code 04:53 Visualizing the linear discriminant / Bayes classifier / Gaussian clouds 02:28 Maximizing the likelihood 06:34 Updating the weights using gradient descent - Theory 06:20 Updating the weights using gradient descent - Code 03:09 E-Commerce Course Project: Training the Logistic Model 06:47 Training Section Summary 02:02 Practical concerns 11 lectures • 54min Practical Section Introduction 02:45 Interpreting the Weights 04:07 L2 Regularization - Theory 08:38 L2 Regularization - Code 01:43 L1 Regularization - Theory 02:53 L1 Regularization - Code 06:13 L1 vs L2 Regularization 03:05 The donut problem 10:01 The XOR problem 06:12 Why Divide by Square Root of D? 06:32 Practical Section Summary 02:02 Checkpoint and applications: How to make sure you know your stuff 2 lectures • 8min BONUS: Sentiment Analysis 05:13 BONUS: Exercises + how to get good at this 02:48 Project: Facial Expression Recognition 6 lectures • 41min Facial Expression Recognition Project Introduction 04:51 Facial Expression Recognition Problem Description 12:21 The class imbalance problem 06:01 Utilities walkthrough 05:45 Facial Expression Recognition in Code 10:41 Facial Expression Recognition Project Summary 01:20 Background Review 1 lecture • 5min Gradient Descent Tutorial 04:30 Setting Up Your Environment (FAQ by Student Request) 2 lectures • 38min Anaconda Environment Setup 20:20 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow 17:32 Extra Help With Python Coding for Beginners (FAQ by Student Request) 5 lectures • 46min How to Uncompress a .tar.gz file 03:18 How to Code by Yourself (part 1) 15:54 How to Code by Yourself (part 2) 09:23 Proof that using Jupyter Notebook is the same as not using it 12:29 Python 2 vs Python 3 04:38 Effective Learning Strategies for Machine Learning (FAQ by Student Request) 4 lectures • 1hr How to Succeed in this Course (Long Version) 10:24 Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced? 22:04 Machine Learning and AI Prerequisite Roadmap (pt 1) 11:18 Machine Learning and AI Prerequisite Roadmap (pt 2) 16:07 1 more section Requirements Derivatives, matrix arithmetic, probability You should know some basic Python coding with the Numpy Stack Description 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) Who this course is for: Adult learners who want to get into the field of data science and big data Students who are thinking of pursuing machine learning or data science Students who are tired of boring traditional statistics and prewritten functions in R, and want to learn how things really work by implementing them in Python People who know some machine learning but want to be able to relate it to artificial intelligence People who are interested in bridging the gap between computational neuroscience and machine learning Show more Show less Featured review Craig Endert 182 courses 31 reviews Rating: 5.0 out of 5 8 months ago As usual, this professor knocked it out of the park. His mathematical representations of the content without using scikit learn made it possible to understand what it going on behind the curtain. Highly recommend this professor and his content; I will be buying more courses from him. Show more Show less Instructor Lazy Programmer Inc. Artificial intelligence and machine learning engineer 4.6 Instructor Rating 115,455 Reviews 439,048 Students 29 Courses Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer. I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition. Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark. I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text. My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing. I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more. Show more Show less Udemy Business Teach on Udemy Get the app About us Contact us Careers Blog Help and Support Affiliate Impressum Kontakt Terms Privacy policy Cookie settings Sitemap © 2021 Udemy, Inc. window.handleCSSToggleButtonClick = function (event) { var target = event.currentTarget; var cssToggleId = target && target.dataset && target.dataset.cssToggleId; var input = cssToggleId && document.getElementById(cssToggleId); if (input) { if (input.dataset.type === 'checkbox') { input.dataset.checked = input.dataset.checked ? '' : 'checked'; } else { input.dataset.checked = input.dataset.allowToggle && input.dataset.checked ? '' : 'checked'; var radios = document.querySelectorAll('[name="' + input.dataset.name + '"]'); for (var i = 0; i (function(){window['__CF$cv$params']={r:'6777105b6b84424b',m:'a580020862c775d1f45eee83ae92147075c8adfc-1627736995-1800-AWCOnP1skesLmeQAdsH/5dHULrOs7UULD0JcYyi9ympi+1D+8CUTAPYB4+dGHnZK56KxWV9rvhtdI5Pqr8ZCCS2cablcsNAYtARK5oTnkZ7sQhgOrhxrZ9ceUclGZPRRdMh4Bb0BVgBMLOtUxyvTokX6JqpO3FdqUbM8qjylSwXGWW6guynHMFbO9qeCs6HuYA==',s:[0x49e64e845a,0xe6b64ad696],}})();
  6. understand why regularization is used in machine learning Course content 11 sections • 59 lectures • 6h 16m total length Expand all sections Start Here 5 lectures • 34min Introduction and Outline Preview 07:22 How to Succeed in this Course 05:51 Statistics vs. Machine Learning 09:58 Review of the classification problem 01:53 Introduction to the E-Commerce Course Project 08:52 Easy first quiz 1 question Basics: What is linear classification? What's the relation to neural networks? 10 lectures • 37min Linear Classification 04:49 Biological inspiration - the neuron 03:36 How do we calculate the output of a neuron / logistic classifier? - Theory 04:18 How do we calculate the output of a neuron / logistic classifier? - Code 04:30 Interpretation of Logistic Regression Output 05:32 E-Commerce Course Project: Pre-Processing the Data 05:24 E-Commerce Course Project: Making Predictions 03:00 Feedforward Quiz 01:24 Prediction Section Summary 01:11 Suggestion Box 03:03 Solving for the optimal weights 11 lectures • 46min Training Section Introduction 01:38 A closed-form solution to the Bayes classifier 05:59 What do all these symbols mean? X, Y, N, D, L, J, P(Y=1|X), etc. 03:37 The cross-entropy error function - Theory 02:46 The cross-entropy error function - Code 04:53 Visualizing the linear discriminant / Bayes classifier / Gaussian clouds 02:28 Maximizing the likelihood 06:34 Updating the weights using gradient descent - Theory 06:20 Updating the weights using gradient descent - Code 03:09 E-Commerce Course Project: Training the Logistic Model 06:47 Training Section Summary 02:02 Practical concerns 11 lectures • 54min Practical Section Introduction 02:45 Interpreting the Weights 04:07 L2 Regularization - Theory 08:38 L2 Regularization - Code 01:43 L1 Regularization - Theory 02:53 L1 Regularization - Code 06:13 L1 vs L2 Regularization 03:05 The donut problem 10:01 The XOR problem 06:12 Why Divide by Square Root of D? 06:32 Practical Section Summary 02:02 Checkpoint and applications: How to make sure you know your stuff 2 lectures • 8min BONUS: Sentiment Analysis 05:13 BONUS: Exercises + how to get good at this 02:48 Project: Facial Expression Recognition 6 lectures • 41min Facial Expression Recognition Project Introduction 04:51 Facial Expression Recognition Problem Description 12:21 The class imbalance problem 06:01 Utilities walkthrough 05:45 Facial Expression Recognition in Code 10:41 Facial Expression Recognition Project Summary 01:20 Background Review 1 lecture • 5min Gradient Descent Tutorial 04:30 Setting Up Your Environment (FAQ by Student Request) 2 lectures • 38min Anaconda Environment Setup 20:20 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow 17:32 Extra Help With Python Coding for Beginners (FAQ by Student Request) 5 lectures • 46min How to Uncompress a .tar.gz file 03:18 How to Code by Yourself (part 1) 15:54 How to Code by Yourself (part 2) 09:23 Proof that using Jupyter Notebook is the same as not using it 12:29 Python 2 vs Python 3 04:38 Effective Learning Strategies for Machine Learning (FAQ by Student Request) 4 lectures • 1hr How to Succeed in this Course (Long Version) 10:24 Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced? 22:04 Machine Learning and AI Prerequisite Roadmap (pt 1) 11:18 Machine Learning and AI Prerequisite Roadmap (pt 2) 16:07 1 more section Requirements Derivatives, matrix arithmetic, probability You should know some basic Python coding with the Numpy Stack Description 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) Who this course is for: Adult learners who want to get into the field of data science and big data Students who are thinking of pursuing machine learning or data science Students who are tired of boring traditional statistics and prewritten functions in R, and want to learn how things really work by implementing them in Python People who know some machine learning but want to be able to relate it to artificial intelligence People who are interested in bridging the gap between computational neuroscience and machine learning Show more Show less Featured review Craig Endert 182 courses 31 reviews Rating: 5.0 out of 5 8 months ago As usual, this professor knocked it out of the park. His mathematical representations of the content without using scikit learn made it possible to understand what it going on behind the curtain. Highly recommend this professor and his content; I will be buying more courses from him. Show more Show less Instructor Lazy Programmer Inc. Artificial intelligence and machine learning engineer 4.6 Instructor Rating 115,455 Reviews 439,048 Students 29 Courses Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer. I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition. Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark. I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text. My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing. I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more. Show more Show less Udemy Business Teach on Udemy Get the app About us Contact us Careers Blog Help and Support Affiliate Impressum Kontakt Terms Privacy policy Cookie settings Sitemap © 2021 Udemy, Inc. window.handleCSSToggleButtonClick = function (event) { var target = event.currentTarget; var cssToggleId = target && target.dataset && target.dataset.cssToggleId; var input = cssToggleId && document.getElementById(cssToggleId); if (input) { if (input.dataset.type === 'checkbox') { input.dataset.checked = input.dataset.checked ? '' : 'checked'; } else { input.dataset.checked = input.dataset.allowToggle && input.dataset.checked ? '' : 'checked'; var radios = document.querySelectorAll('[name="' + input.dataset.name + '"]'); for (var i = 0; i (function(){window['__CF$cv$params']={r:'6777105b6b84424b',m:'a580020862c775d1f45eee83ae92147075c8adfc-1627736995-1800-AWCOnP1skesLmeQAdsH/5dHULrOs7UULD0JcYyi9ympi+1D+8CUTAPYB4+dGHnZK56KxWV9rvhtdI5Pqr8ZCCS2cablcsNAYtARK5oTnkZ7sQhgOrhxrZ9ceUclGZPRRdMh4Bb0BVgBMLOtUxyvTokX6JqpO3FdqUbM8qjylSwXGWW6guynHMFbO9qeCs6HuYA==',s:[0x49e64e845a,0xe6b64ad696],}})();