This is a collection of Python and Machine Learning resources that are aimed to provide continuous learning of concepts through practical code examples, different use cases and links to further readings.
It is a Help Book because you may change or add the code to see how different aspects correlate with each other and test it out using assertions. Altogether it will make your learning process to be much more interactive and expose you to very high code quality from beginning.
Python
Machine Learning
Python is an interpreted, high-level and general-purpose programming language. Created by Guido van Rossum and first released in 1991, Python's design philosophy emphasizes code readability with its notable use of significant whitespace.
Python's expansive library of open source data analysis tools, web frameworks, and testing instruments make its ecosystem one of the largest out of any programming community.
Python is an accessible language for new programmers because the community provides many introductory resources. The language is also widely taught in universities and used for working with beginner-friendly devices such as the Raspberry Pi. Python's programming language popularity
Several programming language popularity rankings exist. While it's possible to criticize that these guides are not exact, every ranking shows Python as a top programming language within the top ten, if not the top five of all languages.
The IEEE ranked Python as the #1 programming language in 2019, which continued its hot streak after ranking it #1 in 2018, #1 in 2017 and #3 top programming language in 2016. RedMonk's June 2019 ranking had Python at #3, which held consistent from previous years' rankings in 2018 and 2017.
Now I am going to share more details on why Python is popular among job seekers.
Software related services provide employment to millions of people across the globe. Candidates are recruited for different roles in software development. Here below I have listed some of the roles from the software industry where Python skills are important.
Python developer/engineer: As a Python developer, you will get the opportunity to work in different jobs. You will be working on the design and development of front end and back end components. You can work on website development with exposure to Django framework or flask framework. Exposure to Databases such as MySQL, MongoDB is desirable with SQL knowledge.
Python automation tester: Software testers can use Selenium with Python and pytest for testing Automation.
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
This is my multi-month study plan for going from mobile developer (self-taught, no CS degree) to machine learning engineer.
My main goal was to find an approach to studying Machine Learning that is mainly hands-on and abstracts most of the Math for the beginner. This approach is unconventional because it’s the top-down and results-first approach designed for software engineers.
Please, feel free to make any contributions you feel will make it better.
I'm following this plan to prepare for my near-future job: Machine learning engineer. I've been building native mobile applications (Android/iOS/Blackberry) since 2011. I have a Software Engineering degree, not a Computer Science degree. I have an itty-bitty amount of basic knowledge about: Calculus, Linear Algebra, Discrete Mathematics, Probability & Statistics from university. Think about my interest in machine learning:
Understand basic concepts, learn Python, and be able to differenciate Machine Learning, Data Mining and Deep Learning
Machine Learning Resources for Getting Started
Online Video Course
Build Intelligent Applications (Python)
Stanford Machine Learning (Octave)
Overview Papers
Beginner Machine Learning Books
Data Mining: Practical Machine Learning Tools and Techniques, Third Edition
Complete at least the Online Video Course
Start a small project for creating a Python Web Crawler application and a RestFul Service to explore data stored
Learn Neural Networks and understand Deep Learning
Online Video Courses
Books
Papers
Study one of the Machine Learning Dataset from data.gov
Design small experiments using the Datasets for studying Linear Regression, or Logistic Regression, then answer a specific question and report results
Try to port an open source algorithm code from one language to another
Get to know the Python Frameworks for Deep Learning, and focus on TensorFlow