https://github.com/noqcks/0_to_ml_engineer

Im teaching myself how to do machine learning via the internet and storing materials here.
https://github.com/noqcks/0_to_ml_engineer

machine-learning

Last synced: 22 days ago
JSON representation

Im teaching myself how to do machine learning via the internet and storing materials here.

• Host: GitHub
• URL: https://github.com/noqcks/0_to_ml_engineer
• Owner: noqcks
• Created: 2017-02-28T19:21:20.000Z (almost 8 years ago)
• Default Branch: master
• Last Pushed: 2017-12-18T19:48:51.000Z (about 7 years ago)
• Last Synced: 2024-10-24T17:11:04.724Z (2 months ago)
• Topics: machine-learning
• Language: DIGITAL Command Language
• Homepage:
• Size: 28.4 MB
• Stars: 5
• Watchers: 2
• Forks: 1
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# 0 to ML Engineer

I will put materials and coursework here that I'm using to teach myself machine

learning. Eventually I'm hoping to use this knowledge to get a job doing machine

learning!

I have already brushed up on Linear Algebra, Probability, and Calculus before I

started learning the following materials. All three of these topics are

important in machine learning.

## Skills

The list of skills I hope to learn are largely influenced by the skills

needed to acquire a job doing machine learning.

The most detailed job posting I've seen on this was for a lead data scientist

position that was posted by the Government of Ontario (located [here](lead_data_scientist_job_posting.pdf)).

I have roughly created my coursework based on the skills listed in this job posting.

- large scale distributed data acquisition

- data cleaning & normalization

- data storage

- information extraction

- RESTful APIs

- data authentication

- data visualization

- design and build machine learning infrastructure including model training and

serving API requests

- Elasticsearch data storage

- HBase

- Kafka

- Tesserect

## Courses

Introduction:

- [x] 1. Udacity: Intro to Data Analysis

- [x] 2. Udacity: Intro to Machine Learning

The Meat:

- [x] 3. Udacity: Machine Learning For Trading

- [x] 4. Udacity: Deep Learning From Google

- [x] 5. Udacity: Intro to Hadoop and Mapreduce

### 1. Udacity: Intro To Data Analysis

https://www.udacity.com/course/intro-to-data-analysis--ud170

folder: [intro\_to\_data_analysis/](intro_to_data_analysis/)

review: A nice intro to the numpy and pandas libraries for python.

### 2. Udacity: Intro to Machine Learning

https://www.udacity.com/course/intro-to-machine-learning--ud120

folder: [intro\_to\_machine_learning](intro_to_machine_learning/)

review: This was an excellent course for a beginner to machine learning. It gently

introduces you to the general process of machine learning (data probing, feature selection,

algo selection, evaluation), while keeping the level of math to a minimum.

### 3. Udacity: Machine Learning For Trading

https://www.udacity.com/course/machine-learning-for-trading--ud501

folder: N/A

review: I didn't actually do this course because it was so bad. There was no coding

exercises, and depth of the material was very shallow, so I passed on it.

time taken: N/A

### 4. Udacity: Deep Learning from Google

https://www.udacity.com/course/deep-learning--ud730

folder: [deep_learning](deep_learning/)

review: I found it was much easier to get information on neural networks through

blog posts and reading tensorflow documentation. I completed the course, but

some of the questions and exercises weren't structured very well. YMMV

### 5. Udacity: Intro to Hadoop and Mapreduce

https://www.udacity.com/course/intro-to-hadoop-and-mapreduce--ud617

folder: N/A

review: The course had an excellent structure and the concepts were logically

ordered. I think I completed this in half a day. It was very nice to finally

understand hadoop.