Udemy
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
Development
Web Development Data Science Mobile Development Programming Languages Game Development Database Design & Development Software Testing Software Engineering Development Tools No-Code Development
Business
Entrepreneurship Communications Management Sales Business Strategy Operations Project Management Business Law Business Analytics & Intelligence Human Resources Industry E-Commerce Media Real Estate Other Business
Finance & Accounting
Accounting & Bookkeeping Compliance Cryptocurrency & Blockchain Economics Finance Finance Cert & Exam Prep Financial Modeling & Analysis Investing & Trading Money Management Tools Taxes Other Finance & Accounting
IT & Software
IT Certification Network & Security Hardware Operating Systems Other IT & Software
Office Productivity
Microsoft Apple Google SAP Oracle Other Office Productivity
Personal Development
Personal Transformation Personal Productivity Leadership Career Development Parenting & Relationships Happiness Esoteric Practices Religion & Spirituality Personal Brand Building Creativity Influence Self Esteem & Confidence Stress Management Memory & Study Skills Motivation Other Personal Development
Design
Web Design Graphic Design & Illustration Design Tools User Experience Design Game Design Design Thinking 3D & Animation Fashion Design Architectural Design Interior Design Other Design
Marketing
Digital Marketing Search Engine Optimization Social Media Marketing Branding Marketing Fundamentals Marketing Analytics & Automation Public Relations Advertising Video & Mobile Marketing Content Marketing Growth Hacking Affiliate Marketing Product Marketing Other Marketing
Lifestyle
Arts & Crafts Beauty & Makeup Esoteric Practices Food & Beverage Gaming Home Improvement Pet Care & Training Travel Other Lifestyle
Photography & Video
Digital Photography Photography Portrait Photography Photography Tools Commercial Photography Video Design Other Photography & Video
Health & Fitness
Fitness General Health Sports Nutrition Yoga Mental Health Dieting Self Defense Safety & First Aid Dance Meditation Other Health & Fitness
Music
Instruments Music Production Music Fundamentals Vocal Music Techniques Music Software Other Music
Teaching & Academics
Engineering Humanities Math Science Online Education Social Science Language Teacher Training Test Prep Other Teaching & Academics
AWS Certification Microsoft Certification AWS Certified Solutions Architect - Associate AWS Certified Cloud Practitioner CompTIA A+ Cisco CCNA Amazon AWS CompTIA Security+ AWS Certified Developer - Associate
Graphic Design Photoshop Adobe Illustrator Drawing Digital Painting InDesign Character Design Canva Figure Drawing
Life Coach Training Neuro-Linguistic Programming Personal Development Mindfulness Meditation Personal Transformation Life Purpose Emotional Intelligence Neuroscience
Web Development JavaScript React CSS Angular PHP WordPress Node.Js Python
Google Flutter Android Development iOS Development Swift React Native Dart Programming Language Mobile Development Kotlin SwiftUI
Digital Marketing Google Ads (Adwords) Social Media Marketing Google Ads (AdWords) Certification Marketing Strategy Internet Marketing YouTube Marketing Email Marketing Google Analytics
SQL Microsoft Power BI Tableau Business Analysis Business Intelligence MySQL Data Modeling Data Analysis Big Data
Business Fundamentals Entrepreneurship Fundamentals Business Strategy Online Business Business Plan Startup Blogging Freelancing Home Business
Unity Game Development Fundamentals Unreal Engine C# 3D Game Development C++ 2D Game Development Unreal Engine Blueprints Blender
2021-03-05 07:05:32
30-Day Money-Back Guarantee
Development Data Science Deep Learning

Deep Learning Prerequisites: Linear Regression in Python

Data science, machine learning, and artificial intelligence in Python for students and professionals
Bestseller
Rating: 4.6 out of 54.6 (4,680 ratings)
26,790 students
Created by Lazy Programmer Inc.
Last updated 3/2021
English
English [Auto], Spanish [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • Derive and solve a linear regression model, and apply it appropriately to data science problems
  • Program your own version of a linear regression model in Python

Course content

9 sections • 53 lectures • 6h 25m total length

  • Preview07:41
  • Anyone Can Succeed in this Course
    12:42
  • Preview09:58

  • What is machine learning? How does linear regression play a role?
    05:13
  • What can linear regression be used for?
    1 question
  • Define the model in 1-D, derive the solution (Updated Version)
    12:43
  • Define the model in 1-D, derive the solution
    14:52
  • Coding the 1-D solution in Python
    07:38
  • Exercise: Theory vs. Code
    01:19
  • Determine how good the model is - r-squared
    05:50
  • R-squared in code
    02:15
  • Introduction to Moore's Law Problem
    02:30
  • Demonstrating Moore's Law in Code
    08:00
  • Moore's Law Derivation
    06:02
  • R-squared Quiz 1
    01:48
  • Suggestion Box
    03:03

  • Define the multi-dimensional problem and derive the solution (Updated Version)
    09:34
  • Define the multi-dimensional problem and derive the solution
    17:07
  • How to solve multiple linear regression using only matrices
    01:55
  • Coding the multi-dimensional solution in Python
    07:29
  • Polynomial regression - extending linear regression (with Python code)
    07:56
  • Predicting Systolic Blood Pressure from Age and Weight
    05:45
  • R-squared Quiz 2
    02:05

  • What do all these letters mean?
    06:23
  • Interpreting the Weights
    04:00
  • Generalization error, train and test sets
    02:49
  • Generalization and Overfitting Demonstration in Code
    07:32
  • Categorical inputs
    05:21
  • One-Hot Encoding Quiz
    02:07
  • Probabilistic Interpretation of Squared Error
    05:15
  • L2 Regularization - Theory
    04:21
  • L2 Regularization - Code
    04:13
  • The Dummy Variable Trap
    03:58
  • Gradient Descent Tutorial
    04:30
  • Gradient Descent for Linear Regression
    02:13
  • Bypass the Dummy Variable Trap with Gradient Descent
    04:17
  • L1 Regularization - Theory
    03:05
  • L1 Regularization - Code
    04:25
  • L1 vs L2 Regularization
    03:05
  • Why Divide by Square Root of D?
    06:32

  • Brief overview of advanced linear regression and machine learning topics
    05:14
  • Exercises, practice, and how to get good at this
    03:54

  • Windows-Focused Environment Setup 2018
    20:20
  • How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
    17:32

  • 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

  • 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

  • What is the Appendix?
    02:48
  • Preview05:31

Requirements

  • How to take a derivative using calculus
  • Basic Python programming
  • For the advanced section of the course, you will need to know probability

Description

This course teaches you about one popular technique used in machine learning, data science and statistics: linear 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 linear regression module in Python.

Linear regression is the simplest machine learning model you can learn, yet there is so much depth that you'll be returning to it for years to come. That's why it's a great introductory course if you're interested in taking your first steps in the fields of:

  • deep learning

  • machine learning

  • data science

  • statistics

In the first section, I will show you how to use 1-D linear regression to prove that Moore's Law is true.

What's that you say? Moore's Law is not linear?

You are correct! I will show you how linear regression can still be applied.

In the next section, we will extend 1-D linear regression to any-dimensional linear regression - in other words, how to create a machine learning model that can learn from multiple inputs.

We will apply multi-dimensional linear regression to predicting a patient's systolic blood pressure given their age and weight.

Finally, we will discuss some practical machine learning issues that you want to be mindful of when you perform data analysis, such as generalization, overfitting, train-test splits, and so on.

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

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 to know how to apply your skills as a software engineer or "hacker", this course may be useful.

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:

  • People who are interested in data science, machine learning, statistics and artificial intelligence
  • People new to data science who would like an easy introduction to the topic
  • People who wish to advance their career by getting into one of technology's trending fields, data science
  • Self-taught programmers who want to improve their computer science theoretical skills
  • Analytics experts who want to learn the theoretical basis behind one of statistics' most-used algorithms

Featured review

Andres Lopez Candanedo
Andres Lopez Candanedo
20 courses
9 reviews
Rating: 5.0 out of 55 months ago
This course is exactly what I was looking for. The instructor does an impressive job making students understand they need to work hard in order to learned. The examples are clear, and the explanations of the theory is very interesting.

Instructor

Lazy Programmer Inc.
Artificial intelligence and machine learning engineer
Lazy Programmer Inc.
  • 4.6 Instructor Rating
  • 108,604 Reviews
  • 423,515 Students
  • 28 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.

  • Udemy for 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
  • Featured courses
Udemy
© 2021 Udemy, Inc.