Inspired by Google Interview University
This is my multi-month study plan for going from web developer (self-taught, no CS degree) to Machine Learning Engineer.
I work more than 12 hours per day in the public service and study in the field of business studies at the International University of Bad Honnef.
Every evening I learn for my studies and for my career as a Machine Learning Engineer.
This document is intended to serve as a kind of CV, application and profile of my abilities. Of course you can also use it in the same way, if you want to develop in the field of machine learning.
Here are some usefull articles about the deep learning and maschine learning komplex
And also my Udacity education plan for the next 3 years. After this long step-by-step learning time, I would be able to work as an ML engineer
Create stunning user experiences for better web frontends and better data discovering. This is a Nanodegree Course by Udactiy.com
Master the skills required to become a Front-End Web Developer, and start building beautiful, responsive websites optimized for mobile and desktop performance.
This program begins where our Front-End Web Developer Nanodegree program ends, and is designed to give intermediate developers the chance to build on existing front-end skills and master the newest technologies available.
This program ensures you’re supremely well-prepared to succeed in a Senior Web Developer role.
In the meanwhile I develop my python skills at the CAREER TRACK Python Developer.
As a Python Developer I could uses the python programming skills to wrangle data and build tools for data analysis.
The 2nd step into programming with python and showing first elements to the web is based on the Full Stack Web Developer Nanodegree. Since 2000 I am working with Linux, MySQL and other OpenSource Software. So the Full Stack Web Developer would refresh the knowledge and push me to a new level.
Learning Discover Insights from Data. This Learnings are co-created by Facebook, mongoDB and Zipflan Academy
Developing and thinking for new predictive models powered and co-created by Google and Kaggle
About the course: Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI.
This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
Building the future, today. ;-)