Kamil Krzyk - My Road to AI

About

This is a repository that I have created to track my progress in AI/Data Science related topics in order to organise my knowledge and goals. Purpose of doing this is self-motivation, open source/study material for others, portfolio and TODO list.

Table of contents

• Kamil Krzyk - My Road to AI
• About
• Table of Contents
• AI Related Presentations
• Mini AI Projects
  • Problem Solving
  • Visualisation
• AI Programming Showcase
  • Raw Python
  • sklearn
  • TensorFlow
  • Keras
• Articles
• Courses & Certificates
• Sources
• Contact

AI Related Presentations

Mini AI Projects

In this section I will focus about digging up relationship and visualising data. I will try to use Machine learning and visualisation methods for problem solving.

Problem Solving

Visualisation

AI Programming Showcase

In this section I want to show off my knowledge about various AI related algorithms, frameworks, programming languages, libraries and more. Priority is to show how the algorithm works - not to solve complex and ambitious problems. Usually on classical or generated datasets.

Raw Python

Machine Learning

Deep Learning

sklearn

TensorFlow

Machine Learning

Deep Learning

Keras

Articles

Note: Delayed due to 1,5 month long preparations for organising ML/DL workshops.

Courses & Certificates

When I was younger I played a lot of computer games. I still tend to play today a little as a form of relax and to spend time with friends that live far from me. One thing that I have very enjoyed about gaming was gathering trophies. You made an effort to complete list of challenges or get a great score and then looked at list of your achievements with satisfaction. My current self have inherited this habit and as I study on daily basis I like to gather proves that I have done something - to make it more like a game where each topic is a boss that you have to clear on hard mode. Of course what's in your head is most important but if it helps to motivate you, then why not?

• Programming languages:

  • Programming for Everybody (Getting Started with Python) (Feb 2017) (Coursera - University of Michigan - Charles Severance)
  • Python Data Structures (Feb 2017) (Coursera - University of Michigan - Charles Severance)
  • Using Python to Access Web Data (Feb 2017) (Coursera - University of Michigan - Charles Severance)
  • Using Databases with Python (Feb 2017) (Coursera - University of Michigan - Charles Severance)

• Algorithms:

  • Divide and Conquer, Sorting and Searching, and Randomized Algorithms (Sep 2017) (Coursera - University of Stanford)

• AI related:

  • Machine Learning (Nov 2016 - Feb 2017) (Coursera - Stanford - Andrew Ng)
  • Deep Learning Nanodegree (Mar 2017 - Aug 2017) (Udacity - Siraj Raval, Mat Leonard, Brok Bucholtz + guest lessions by: Ian Goodfellow, Andrew Trask)
  • Neural Networks and Deep Learning (Oct 2017) (Coursera - deeplearning.ai - Andrew Ng)
  • Practical Machine Learning (Nov - Dec 2017) (DataWorkshop - Vladimir Alekseichenko)
  • Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization (Jan 2018) (Coursera - deeplearning.ai - Andrew Ng)
  • Structuring Machine Learning Projects (Mar 2018) (Coursera - deeplearning.ai - Andrew Ng)
  • Segmentation and Clustering [Free] (May 2018) (Udacity)
  • Data Science Nanodegree Term 1 (Jun 2018 - Currently in progress) (Udacity)

Sources

There is a list of sources that I have used (and found helpful in some way) or keep using in order to produce my repo content.

Contact

• Twitter: @F1sherKK
• Medium: @krzyk.kamil
• E-mail: [email protected]
• Android related open-source contributions: AzimoLabs
• LinkedIn