Deep Learning Prerequisites: The Numpy Stack in Python

What you’ll learn

  • Understand supervised machine learning (classification and regression) with real-world examples using Scikit-Learn
  • Understand and code using the Numpy stack

  • Make use of Numpy, Scipy, Matplotlib, and Pandas to implement numerical algorithms

  • Understand the pros and cons of various machine learning models, including Deep Learning, Decision Trees, Random Forest, Linear Regression, Boosting, and More!
Requirements
  • Understand linear algebra and the Gaussian distribution
  • Be comfortable with coding in Python
  • You should already know “why” things like a dot product, matrix inversion, and Gaussian probability distributions are useful and what they can be used for

Description

Welcome! This is Deep Learning, Machine Learning, and Data Science Prerequisites: The Numpy Stack in Python.

One question or concern I get a lot is that people want to learn deep learning and data science, so they take these courses, but they get left behind because they don’t know enough about the Numpy stack in order to turn those concepts into code.

In sum:

If you’ve taken a deep learning or machine learning course, and you understand the theory, and you can see the code, but you can’t make the connection between how to turn those algorithms into actual running code, this course is for you.

HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:

  • linear algebra
  • probability
  • Python coding: if/else, loops, lists, dicts, sets
  • you should already know “why” things like a dot product, matrix inversion, and Gaussian probability distributions are useful and what they can be used for

TIPS (for getting through the course):

  • Watch it at 2x.
  • Take handwritten notes. This will drastically increase your ability to retain the information. This has been proven by research!
  • Ask lots of questions on the discussion board. The more the better!
  • Realize that most exercises will take you days or weeks to complete.

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

  • Check out the lecture “What order should I take your courses in?” (available in the Appendix of any of my courses)
Who this course is for:
  • Students and professionals with little Numpy experience who plan to learn deep learning and machine learning later
  • Students and professionals who have tried machine learning and data science but are having trouble putting the ideas down in code

Created by Lazy Programmer Inc.
English

Size: 852.70 MB

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