Udemy - Deep Learning Prerequisites Logistic Regression in Python
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.
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
TIPS (for getting through the course):
Watch it at 2x.
Take handwritten notes. This will drastically increase your ability to retain the information.
Write down the equations. If you don't, I guarantee it will just look like gibberish.
Ask lots of questions on the discussion board. The more the better!
Realize that most exercises will take you days or weeks to complete.
Write code yourself, don't just sit there and look at my code.
WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:
Deep Learning Prerequisites: Logistic Regression in Python Check out the lecture "What order should I take your courses in?" (available in the Appendix 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
Created by Lazy Programmer Inc.
Last updated 3/2020
English
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FILE LIST
Filename
Size
1. Start Here/1. Introduction and Outline.mp4
46.9 MB
1. Start Here/1. Introduction and Outline.srt
5.3 KB
1. Start Here/2. How to Succeed in this Course.mp4
6.4 MB
1. Start Here/2. How to Succeed in this Course.srt
4 KB
1. Start Here/3. Review of the classification problem.mp4
3 MB
1. Start Here/3. Review of the classification problem.srt
2.2 KB
1. Start Here/4. Introduction to the E-Commerce Course Project.mp4
14.8 MB
1. Start Here/4. Introduction to the E-Commerce Course Project.srt
7.6 MB
1. Start Here/5. Easy first quiz.html
152 B
2. Basics What is linear classification What's the relation to neural networks/1. Linear Classification.mp4
7.6 MB
2. Basics What is linear classification What's the relation to neural networks/1. Linear Classification.srt
5.2 KB
2. Basics What is linear classification What's the relation to neural networks/2. Biological inspiration - the neuron.mp4
9.4 MB
2. Basics What is linear classification What's the relation to neural networks/2. Biological inspiration - the neuron.srt
4.4 KB
2. Basics What is linear classification What's the relation to neural networks/3. How do we calculate the output of a neuron logistic classifier - Theory.mp4
15.2 MB
2. Basics What is linear classification What's the relation to neural networks/3. How do we calculate the output of a neuron logistic classifier - Theory.srt
80.2 MB
2. Basics What is linear classification What's the relation to neural networks/4. How do we calculate the output of a neuron logistic classifier - Code.mp4
5.8 MB
2. Basics What is linear classification What's the relation to neural networks/4. How do we calculate the output of a neuron logistic classifier - Code.srt
4.5 KB
2. Basics What is linear classification What's the relation to neural networks/5. Interpretation of Logistic Regression Output.mp4
27.9 MB
2. Basics What is linear classification What's the relation to neural networks/5. Interpretation of Logistic Regression Output.srt
6.4 KB
2. Basics What is linear classification What's the relation to neural networks/6. E-Commerce Course Project Pre-Processing the Data.mp4
11.2 MB
2. Basics What is linear classification What's the relation to neural networks/6. E-Commerce Course Project Pre-Processing the Data.srt
5.1 KB
2. Basics What is linear classification What's the relation to neural networks/7. E-Commerce Course Project Making Predictions.mp4
5.7 MB
2. Basics What is linear classification What's the relation to neural networks/7. E-Commerce Course Project Making Predictions.srt
3 KB
2. Basics What is linear classification What's the relation to neural networks/8. Feedforward Quiz.mp4
2.3 MB
2. Basics What is linear classification What's the relation to neural networks/8. Feedforward Quiz.srt
1.7 KB
2. Basics What is linear classification What's the relation to neural networks/9. Prediction Section Summary.mp4
2.2 MB
2. Basics What is linear classification What's the relation to neural networks/9. Prediction Section Summary.srt
1.5 KB
3. Solving for the optimal weights/1. Training Section Introduction.mp4
2.8 MB
3. Solving for the optimal weights/1. Training Section Introduction.srt
2 KB
3. Solving for the optimal weights/10. E-Commerce Course Project Training the Logistic Model.mp4
17.1 MB
3. Solving for the optimal weights/10. E-Commerce Course Project Training the Logistic Model.srt
5.3 KB
3. Solving for the optimal weights/11. Training Section Summary.mp4
3.4 MB
3. Solving for the optimal weights/11. Training Section Summary.srt
2.6 KB
3. Solving for the optimal weights/2. A closed-form solution to the Bayes classifier.mp4
9.1 MB
3. Solving for the optimal weights/2. A closed-form solution to the Bayes classifier.srt
7.3 KB
3. Solving for the optimal weights/3. What do all these symbols mean X, Y, N, D, L, J, P(Y=1X), etc..mp4
6.4 MB
3. Solving for the optimal weights/3. What do all these symbols mean X, Y, N, D, L, J, P(Y=1X), etc..srt
5.2 KB
3. Solving for the optimal weights/4. The cross-entropy error function - Theory.mp4
4.5 MB
3. Solving for the optimal weights/4. The cross-entropy error function - Theory.srt
4.4 KB
3. Solving for the optimal weights/5. The cross-entropy error function - Code.mp4
9.1 MB
3. Solving for the optimal weights/5. The cross-entropy error function - Code.srt
3.9 KB
3. Solving for the optimal weights/6. Visualizing the linear discriminant Bayes classifier Gaussian clouds.mp4
5.3 MB
3. Solving for the optimal weights/6. Visualizing the linear discriminant Bayes classifier Gaussian clouds.srt
2.3 KB
3. Solving for the optimal weights/7. Maximizing the likelihood.mp4
25.2 MB
3. Solving for the optimal weights/7. Maximizing the likelihood.srt
4 KB
3. Solving for the optimal weights/8. Updating the weights using gradient descent - Theory.mp4
9.3 MB
3. Solving for the optimal weights/8. Updating the weights using gradient descent - Theory.srt
8.1 KB
3. Solving for the optimal weights/9. Updating the weights using gradient descent - Code.mp4
7.3 MB
3. Solving for the optimal weights/9. Updating the weights using gradient descent - Code.srt