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Deep Learning Prerequisites: Linear Regression in Python Download Now

Deep Learning Prerequisites: Linear Regression in Python

Deep Learning Prerequisites: Linear Regression in Python

Knowledge science: Be taught linear regression from scratch and construct your individual working program in Python for knowledge evaluation.

What you’ll be taught
  • Derive and remedy a linear regression mannequin, and apply it appropriately to knowledge science issues
  • Program your individual model of a linear regression mannequin in Python
  • How one can take a by-product utilizing calculus
  • Fundamental Python programming
  • For the superior part of the course, you will want to know likelihood


This course teaches you about one fashionable method used in machine studyingknowledge science and statisticslinear regression. We cowl the speculation from the bottom up: derivation of the answer, and functions to real-world issues. We present you ways one may code their very own linear regression module in Python.

Linear regression is the best machine studying mannequin you’ll be able to be taught, but there’s a lot depth that you simply’ll be returning to it for years to come back. That’s why it’s an ideal introductory course in case you’re in taking your first steps in the fields of:

  • deep studying
  • machine studying
  • knowledge science
  • statistics

Within the first part, I’ll present you tips on how to use 1-D linear regression to show that Moore’s Regulation is true.

What’s that you simply say? Moore’s Regulation just isn’t linear?

You’re right! I’ll present you ways linear regression can nonetheless be utilized.

Within the subsequent part, we are going to lengthen 1-D linear regression to any-dimensional linear regression – in different phrases, tips on how to create a machine studying mannequin that may be taught from a number of inputs.

We’ll apply multi-dimensional linear regression to predicting a affected person’s systolic blood strain given their age and weight.

Lastly, we are going to talk about some sensible machine studying points that you simply wish to be aware of while you carry out knowledge evaluation, resembling generalizationoverfittingtrain-test splits, and so forth.

This course doesn’t require any exterior supplies. All the things wanted (Python, and a few Python libraries) might be obtained for FREE.

In case you are a programmer and also you wish to improve your coding talents by studying about knowledge science, then this course is for you. You probably have a technical or mathematical background, and also you wish to know tips on how to apply your expertise as a software program engineer or “hacker”, this course could also be helpful.

This course focuses on “how to build and understand“, not just “how to use”. Anybody can be taught to make use of an API in 15 minutes after studying some documentation. It’s not about “remembering facts”, it’s about “seeing for yourself” by way of experimentation. It should educate you tips on how to visualize what’s taking place in the mannequin internally. If you would like extra than only a superficial have a look at machine studying fashions, this course is for you.

Recommended Stipulations:

  • calculus (taking derivatives)
  • matrix arithmetic
  • likelihood
  • Python coding: if/else, loops, lists, dicts, units
  • Numpy coding: matrix and vector operations, loading a CSV file

TIPS (for getting by means of the course):

  • Watch it at 2x.
  • Take handwritten notes. It will drastically improve your capacity to retain the knowledge.
  • Write down the equations. Should you don’t, I assure it is going to simply appear like gibberish.
  • Ask plenty of questions on the dialogue board. The extra the higher!
  • Understand that almost all workouts will take you days or even weeks to finish.
  • Write code your self, don’t simply sit there and have a look at my code.


  • Try the lecture “What order should I take your courses in?” (accessible in the Appendix of any of my programs, together with the free Numpy course)
Who this course is for:
  • People who find themselves in knowledge science, machine studying, statistics and synthetic intelligence
  • Folks new to knowledge science who would really like a straightforward introduction to the subject
  • Individuals who want to advance their profession by entering into one among expertise’s trending fields, knowledge science
  • Self-taught programmers who wish to enhance their laptop science theoretical expertise
  • Analytics consultants who wish to be taught the theoretical foundation behind one among statistics’ most-used algorithms

Created by Lazy Programmer Inc.
Final up to date 7/2020
English [Auto-generated], Spanish [Auto-generated]

Measurement: 1.07 GB

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The put up Deep Learning Stipulations: Linear Regression in Python .

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