This repo tracks my progress in AI/Machine Learning/Data Science/Python field. It was inspired by Kamil Krzyk's F1sherKK-MyRoadToAI.
Things soon to happen or in progress.
| Name | Description | Other |
|---|---|---|
| Machine learning by Andrew Ng @ Coursera | 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. | I implemented the exercises using Python, not Octave |
Online courses I have finished
| Name/Link | Description | Other |
|---|---|---|
| DataWorkshop 5 day challenge | Very basics of Python, Anaconda, Jupyter, Google Cloud, Scikit-learn. The result was a basic model which predicted bus delays in Krakow. | Certificate |
| Python A-Z™: Python For Data Science With Real Exercises @ Udemy | Teaches statistical analysis, data mining and visualization using Python, Numpy, Matplotlib, Pandas, Seaborn. | Certificate |
| Pandas tutorial @ Kaggle | Short hands-on challenges to perfect data manipulation skills | |
| Official Python tutorial @ python.org | Introduces the reader informally to the basic concepts and features of the Python language and system. |
Live sessions with a mentor I have attended.
| Name/Link | Description | Other |
|---|---|---|
| Introduction to Recommendation Systems - part 1, part 2, part 3 @ PyData Warsaw 2018 | This introduction aims in presenting theoretical background as well as practical exercises of basic building blocks of Recommendation Systems. After this tutorial you will be able to implement your won Recommendation System from scratch using real data. | |
| Playing with CNN using Fashion-MNIST. Classification and what else can be done on it? @ PyData Warsaw 2018 | Fashion-MNIST seems to currently be the first choice if you want to experiment mostly with different classification models. However, classification is not the only problem you can try with this dataset. In the workshop we will move beyond that and build simple clothes recommendation system using siamese networks architecture. | |
| Structuring machine learning models by using pipelines @ PyData Warsaw 2018 | Writing code that is modular and maintainable is a standard in software development. Yet many Data Science projects are messy and hard to maintain which reflects that it was constructed by experimenting. The tutorial is meant as an introduction to organizing your code in pipelines. We will walk through the code from a Kaggle competition in which Pawel's team took 1st place. | |
| Serverless Approach to Working with Data @ PyData Warsaw 2018 | Serverless ideas, in particular Function as a Service (FaaS), are gaining popularity. A lot has been said about serverless in context of web applications, but not so much when it comes to data engineering and analytics. During the presentation you will learn how to use the serverless approach for data-driven applications for data collection, storage and analytics. | |
| Introduction to image classification using Deep Learning @ Programistok | In this workshop we will explain the basic theory concerning neural networks (Multilayer Perceptron, Convolutionary Networks). We will build a neural network using the Keras library and apply it to solve simple problems in the field of image classification. | My blog post about this workshop |
Interesting talks, meetups, conferences I have attended.
| Name/Link | Other |
|---|---|
| PyData Warsaw 2018 | |
| DevForge | |
| DataWorkshop Club Conf 2018 | My blog post about this event |
| Pystok | The ones I attended: 33, 34, 36, 37 - PyTorch, BioPython |