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Introduction to Machine Learning for Coders!

fast.ai via Independent

1 Review 183 students interested

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Overview

Welcome to Introduction to Machine Learning for Coders! taught by Jeremy Howard (Kaggle's #1 competitor 2 years running, and founder of Enlitic). Learn the most important machine learning models, including how to create them yourself from scratch, as well as key skills in data preparation, model validation, and building data products.
There are around 24 hours of lessons, and you should plan to spend around 8 hours a week for 12 weeks to complete the material. The course is based on lessons recorded at the University of San Francisco for the Masters of Science in Data Science program. We assume that you have at least one year of coding experience, and either remember what you learned in high school math, or are prepared to do some independent study to refresh your knowledge.

 

Syllabus

1—INTRODUCTION TO RANDOM FORESTS

2—RANDOM FOREST DEEP DIVE

3—PERFORMANCE, VALIDATION AND MODEL INTERPRETATION

4—FEATURE IMPORTANCE, TREE INTERPRETER

5—EXTRAPOLATION AND RF FROM SCRATCH

6—DATA PRODUCTS AND LIVE CODING

7—RF FROM SCRATCH AND GRADIENT DESCENT

8—GRADIENT DESCENT AND LOGISTIC REGRESSION

9—REGULARIZATION, LEARNING RATES AND NLP

10— MORE NLP AND COLUMNAR DATA

11—EMBEDDINGS

12— COMPLETE ROSSMANN, ETHICAL ISSUES

Review for Introduction to Machine Learning for Coders!
4.0 Based on 1 reviews

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Anonymous
4.0 2 months ago
Anonymous completed this course.
This is a worthwhile course, but it's important to understand its pros and cons. I had initially heard about fast.ai through its deep learning course, but decided to take this one as a refresher before I jumped in.

I completed it a few weeks ago, and so far here are my thoughts.

THE GOOD:

1). It's style of instruction is a nice break from the way most MOOC's are typically taught. Jeremy goes over material in a very straightforward manner, and he's good at breaking down topics in a way that's easy to understand. I thought the classroom format was al…
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