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PhD student → Hydrology & Deep Learning. Teaching and Research Assistant at University of Zagreb.

The silent giant carrying your data science projects

Until now we learned about Python programming in general, which operators when to use and how to simplify repeatable tasks or make decisions using Control flow. Since in Hydrology (& Meteorology) we mostly work with a lot of numbers, we need to look further into tools that can help us deal with a large amount of numbers. Therefore, this article is covering an incredibly popular library in data science circles, Numpy.

This article is structured as follows:

  • Introduction
  • Creating arrays
  • Shape and Reshape
  • Accessing elements and slicing an Array
  • Maths and Statistics with Numpy
  • Bonus content — Speed advantages of…


The friendly Panda helping us when dealing with spreadsheet data

In the last article we saw how Numpy can make our life’s a lot easier when dealing with a large amount of numerical data. But sometimes, there are also other types of data involved, and then, Numpy is not always the best solution. In such situations, Pandas comes in handy. It has many application possibilities, but my preferred case is to read files than are not numerical only, i.e., .csv or .xlsx files that contain some kind of observations (numerical data) but also textual information or metadata about those observations.

This article is structured as follows:

  • Introduction
  • Pandas Series and…


You wanted to learn something about fi-else conditionals or loops? Look no further!

Recently I’ve started a Python programming beginner oriented series of articles. We’ve already covered topics related to starting your Python journey and last time we’ve worked with operators. Today we will continue our journey, and tackle the topic of Control flow.

Or as Jake VanderPlas in “A Whirlwind Tour of Python” would say: “Control flow is where the rubber really meets the road in programming.

I’ve decided to split the article into several parts to facilitate the learning process. The parts are as follows:

  • Introduction
  • Conditionals or Conditional statements
  • Loops
    - for
    - while
  • Controlling loops with Breaks & Continues


They may seem trivial at first glance, but believe me, they really fast get crucial

In my last article, we covered the basics, how to start coding with Python. We learned how to set up a programming environment. Also, we encountered data types, variables, and data structures. With this article we will continue our journey, today we will learn what useful thing we can do with variables or data structures.

The article is structured as follows:

  • Introduction
  • Arithmetic Operators
  • Comparison (Relational) Operators
  • Assignment Operators
  • Logical Operators
  • Bitwise Operators
  • Membership Operators
  • Identity Operators
  • Conclusion

Enjoy the reading!

Introduction

Just like when we first started with math, we learned about numbers, and then, we started to do something…


You wanted to learn codding, but had no idea where and how to start? Then this beginners guide is exactly for you!

With this article, I want to help absolute beginners with their journey in coding. My goal is to make it as interesting as possible, therefore I will use some meteorological terms and phenomena. My idea is that it’s easier to understand some concept if you can visualize it, and what is a better example than the weather, which we encounter everyday when we go to work or take a walk our dogs. 🙂

Structure of the article:

  • Introduction
  • Creating the environment
  • Jupyter Notebook
  • Data Types
  • Data Structures
  • Conclusion

Enjoy the reading! 🙂

INTRODUCTION

What is Python? According to its creator, Guido…


A simple map, to help you not get lost in the Random Forest of Decision Trees

Last time we have covered some of the most important evaluation techniques for Machine Learning model. We could easily say that when someone blindly uses only accuracy as measurements, he really “can’t see the Forest for the Trees”. :) Joking aside, we saw that when the data is imbalanced, just accuracy is often not enough to properly evaluate the model.

As the title suggests, we are continuing our journey covering popular Machine Learning techniques. On todays topic, we have Decision Trees and Random Forests. …


Basic evaluation metrics and methods for Machine Learning algorithms

Just recently I covered some basic Machine Learning algorithms, namely, K Nearest Neighbours, Linear and Polynomial Regression and Logistic Regression. In all these articles we used to popular car fuel economy dataset from Udacity and conducted some kind of classification of cars, i.e. in vehicle “size” classes or according to driven wheels. Also, every time we calculated the basic model accuracy on training and test data and tried to fit a car of our interest into the model to check it’s capabilities.

This is all fine, but sometime one could face a dataset where the classes are not that well…


A while ago I started a Coursera Course on Applied Machine Learning. In order to help others taking the course and help myself better understand the topics, I’ve decided to make short tutorials following the curriculum. My last two articles covered KNN classification and Linear (Polynomial) Regression. If interested, feel free to take a look.

Today, I will cover a technique called Logistic Regression. Even though it’s called “Regression” it is a classification method. The main difference when compared to Linear Regression is the output, where Linear Regression gives a continuous value, Logistic Regression returns a binary variable. In simple…


Recently I started a course on Applied Machine Learning by the University of Michigan on Coursera. The course covers some widely popular Machine Learning algorithms. I decided to write few brief articles regarding this topic, which are intended to help people new to this topic dive in the interesting world of Machine Learning. My last article covered the topic of K Nearest Neighbours (KNN) classification. You can take a look how to use KNN to classify cars into vehicle classes according to their engine size, cylinder count, fuel consumption and CO2 output. …


A simple K Nearest Neighbour (KNN) classification of car classes according to their fuel consumption and engine size.

Introduction

Machine Learning (ML) is a very popular term nowadays. I think it is impossible to go through a day online, without meeting the term. As many before me, and I bet, many after me, sometime ago I started my journey in this interesting area.

There are many well written and well thought online resources on this topic. I am not trying to invent the wheel new, I will just try to share my perspective, or thoughts when going through a popular online Course by the University of Michigan, Applied Machine Learning in Python. …

Karlo Leskovar

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