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Deep Learning Prerequisites: Linear Regression in Python Udemy Free Download

Deep Learning Stipulations: Linear Regression in Python Download

Knowledge science, machine studying, and synthetic intelligence in Python for college students and professionals

Deep Learning Prerequisites Linear Regression in Python
Deep Learning Stipulations Linear Regression in Python
What you’ll be taught
  • Derive and resolve a linear regression mannequin, and apply it appropriately to knowledge science issues
  • Program your personal 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 have to to know the chance

This course teaches you about one fashionable method used in machine studying, knowledge science and statistics: linear regression. We cowl the idea from the bottom up: derivation of the answer, and functions to real-world issues. We present you ways one would possibly 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 return. That’s why it’s an amazing 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 how you can use 1-D linear regression to show that Moore’s Regulation is true.

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

You might be appropriate! I’ll present you ways linear regression can nonetheless be utilized.

Within the subsequent part, we are going to prolong 1-D linear regression to any-dimensional linear regression – in different phrases, how you can 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 conscious of while you carry out knowledge evaluation, corresponding to generalization, overfitting, train-test splits, and so forth.

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

If you’re a programmer and also you wish to improve your coding talents by studying about knowledge science, then this course is for you. When you have a technical or mathematical background, and also you wish to know how you can apply your abilities 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 quarter-hour after studying some documentation. It’s not about “remembering facts”, it’s about “seeing for yourself” through experimentation. It is going to educate you how you can visualize what’s taking place in the mannequin internally. In order for you extra than only a superficial take a look at machine studying fashions, this course is for you.

“If you can’t implement it, you don’t understand it”

  • Or as the good physicist Richard Feynman mentioned: “What I cannot create, I do not understand”.
  • My programs are the ONLY programs the place you’ll discover ways to implement machine studying algorithms from scratch
  • Different programs will educate you how you can plug in your knowledge right into a library, however do you actually need assist with 3 strains of code?
  • After doing the identical factor with 10 datasets, you understand you didn’t be taught 10 issues. You realized 1 factor, and simply repeated the identical 3 strains of code 10 instances…


Urged Stipulations:

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



  • Take a look at the lecture “Machine Learning and AI Prerequisite Roadmap” (out there in the FAQ 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
  • Individuals new to knowledge science who would really like a straightforward introduction to the subject
  • Individuals who want to advance their profession by stepping into one among expertise’s trending fields, knowledge science
  • Self-taught programmers who wish to enhance their laptop science theoretical abilities
  • Analytics specialists who wish to be taught the theoretical foundation behind one among statistics’ most-used algorithms
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