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Deep Learning Prerequisites: Logistic Regression in Python

Data science, machine learning, and artificial intelligence in Python for students and professionals
4.7
4.7/5
(3,277 reviews)
25,037 students
Created by

9.2

CourseMarks Score®

9.9

Freshness

9.2

Feedback

8.0

Content

Platform: Udemy
Video: 6h 16m
Language: English
Next start: On Demand

Top Deep Learning courses:

Detailed Analysis

CourseMarks Score®

9.2 / 10

CourseMarks Score® helps students to find the best classes. We aggregate 18 factors, including freshness, student feedback and content diversity.

Freshness Score

9.9 / 10
This course was last updated on 5/2021.

Course content can become outdated quite quickly. After analysing 71,530 courses, we found that the highest rated courses are updated every year. If a course has not been updated for more than 2 years, you should carefully evaluate the course before enrolling.

Student Feedback

9.2 / 10
We analyzed factors such as the rating (4.7/5) and the ratio between the number of reviews and the number of students, which is a great signal of student commitment.

New courses are hard to evaluate because there are no or just a few student ratings, but Student Feedback Score helps you find great courses even with fewer reviews.

Content Score

8.0 / 10
Video Score: 8.5 / 10
The course includes 6h 16m video content. Courses with more videos usually have a higher average rating. We have found that the sweet spot is 16 hours of video, which is long enough to teach a topic comprehensively, but not overwhelming. Courses over 16 hours of video gets the maximum score.
The average video length is 8 hours 47 minutes of 111 Deep Learning courses on Udemy.
Detail Score: 10.0 / 10

The top online course contains a detailed description of the course, what you will learn and also a detailed description about the instructor.

Extra Content Score: 5.5 / 10

Tests, exercises, articles and other resources help students to better understand and deepen their understanding of the topic.

This course contains:

0 article.
0 resource.
0 exercise.
0 tests or quizzes.

Table of contents

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)

You will learn

✓ 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

Requirements

• Derivatives, matrix arithmetic, probability
• You should know some basic Python coding with the Numpy Stack

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

How much does the Deep Learning Prerequisites: Logistic Regression in Python course cost? Is it worth it?

The course costs $19.99. And currently there is a 82% discount on the original price of the course, which was $109.99. So you save $90 if you enroll the course now.
The average price is $15.1 of 111 Deep Learning courses. So this course is 7% cheaper than the average Deep Learning course on Udemy.

Does the Deep Learning Prerequisites: Logistic Regression in Python course have a money back guarantee or refund policy?

YES, Deep Learning Prerequisites: Logistic Regression in Python has a 30-day money back guarantee. The 30-day refund policy is designed to allow students to study without risk.

Are there any SCHOLARSHIPS for this course?

Currently we could not find a scholarship for the Deep Learning Prerequisites: Logistic Regression in Python course, but there is a $90 discount from the original price ($109.99). So the current price is just $19.99.

Who is the instructor? Is Lazy Programmer Inc. a SCAM or a TRUSTED instructor?

Lazy Programmer Inc. has created 29 courses that got 116,539 reviews which are generally positive. Lazy Programmer Inc. has taught 457,898 students and received a 4.6 average review out of 116,539 reviews. Depending on the information available, Lazy Programmer Inc. is a TRUSTED instructor.
Artificial intelligence and machine learning engineer
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.

9.2

CourseMarks Score®

9.9

Freshness

9.2

Feedback

8.0

Content

Platform: Udemy
Video: 6h 16m
Language: English
Next start: On Demand

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