I have a Ph.D. in Mechanical Engineering and I’m an assistant professor at the University of Nis. The focus of my work is the application of hybrid optimization and machine learning methods to support decision making in the energy sector. I use Python and its numerical libraries all the time for research and consulting.

I’m versatile in several programming languages: Python, C#, C, JavaScript, etc. My passions are energy, applied mathematics, machine learning, Python, and Linux. I enjoy learning, self-improving, and moving the boundaries of own comfort zone. I’m always looking for opportunities to combine methods and automate tasks.

These are some links related to my work:

• Twitter
• LinkedIn
• ResearchGate
• Academia
• List of my publications
• Web site

Tutorials by Mirko:

• Linear Regression in Python
• Stochastic Gradient Descent Algorithm With Python and NumPy
• Split Your Dataset With scikit-learn's train_test_split()
• Hands-On Linear Programming: Optimization With Python
• SettingWithCopyWarning in pandas: Views vs Copies
• The pandas DataFrame: Make Working With Data Delightful
• Logistic Regression in Python
• NumPy, SciPy, and pandas: Correlation With Python
• Python Statistics Fundamentals: How to Describe Your Data
• pandas: How to Read and Write Files
• NumPy arange(): How to Use np.arange()

Tutorials Mirko Contributed to:

• Plot With pandas: Python Data Visualization for Beginners
• Python's reduce(): From Functional to Pythonic Style
• Using Python datetime to Work With Dates and Times
• Starting With Linear Regression in Python (Course)
• Splitting Datasets With scikit-learn and train_test_split() (Course)
• Reading and Writing Files With pandas (Course)
• The pandas DataFrame: Working With Data Efficiently (Course)
• Using NumPy's np.arange() Effectively (Course)