This bundle of e-books is specially crafted for beginners. Everything from Python basics to the deployment of Machine Learning algorithms to production in one place. Become a Machine Learning Superhero TODAY!

Why MACHINE Learning?


Why should you invest in this e-books bundle?

The world is investing in machine learning and so can you

Today Machine Learning and Deep Learning are some of the most demanding skills in the market. Companies like Google, Tesla, Amazon, and Microsoft are in constant search of talent. This is a skill that can land you your dream job.

The machine learning market is growing and it is expected to be more than $35B in the next few years. The world is investing in machine learning and so can you.

Crafted for beginners

Learning Machine Learning (pun intended) can be challenging as you might already know. Probably the biggest problem is that it is not a single skill that you need to develop, but a set of skills. That is why we crafted this bundle of 5 e-books and guides specially crafted for beginners – Ultimate Guide to Machine Learning with Python.

In this bundle, you will learn the necessary skills: Math for Machine Learning, Python for Data Science, how to perform Data Analysis, how to build and use the most popular Machine Learning algorithms, what are Neural Networks and how to implement them, and finally how to deploy these systems into production.

1M+ Happy Students

More than 1M readers are already learning from the free content we provide in our blog.

Everything in one place

Ultimate Guide to Machine Learning with Python is a bundle of 5 e-books and guides that will help you develop the necessary skills and become a Machine Learning Engineer.

These books cover both theoretical and technical skills. You will not learn only how some machine learning algorithms function, but why you should use them. 

Ultimate Guide to Machine Learning with Python covers a wide range of libraries like NumPy, Pandas, SeaBorn, Sci-Kit Learn, and PyTorch.

Best Buy

Machine Learning and Deep Learning courses can be quite expensive. The provided bundle contains information and skills for which you would need to buy around 10 separate courses. 

“This book wonderfully sums the information otherwise scattered, which is a priceless time and energy saver.”

Whoever tried to jump into the world of Machine learning knows it is not a walk in the park. In order to master it, one would need to obtain a diverse set of skills and knowledge from different areas. Finding the right learning materials and selecting what is important is not an easy task, but it is crucial if we want to stay on top of emerging technology trends. This book wonderfully sums the information otherwise scattered, which is a priceless time and energy saver.

Down to Earth explanations that are easy to digest prove that Nikola is really an expert on the subject. His well-structured and to-the-point guides and instructions will get you running in no time. Whether you are a software developer or a data science professional, this book gets you covered.

Milos Davidovic

Co-CTO, Vega IT

“Nikola’s books are different. Wrote by the engineer to engineers.”

There are so many ML trainings and books. Someone would say – great I have enough material for learning. But… Is it really like that? Especially for someone who is getting into this topic the first time? Most of the books are like reading Don Quixote without knowing all letters.  Nikola’s books are different. Wrote by the engineer to engineers. Explains all important concepts from math over general theory to real examples. Nikola’s passion for ML resulted in a great blog and great books that will be suitable for beginners as well as experts. Happy for having the chance to read books and happy to recommend them to others.
Zeljko Todorivic

Senior Software Developre, Microsoft


 Check out table of content and previews

Basics of Machine Learning

In the first section of this book, we cover some basic topics of machine learning. We define the problems machine learning is trying to solve, and we define the types of machine learning. Apart from that, we talk about the anatomy of machine learning algorithms and learn about performance metrics that we use throughout this book.

Machine Learning Algorithms (preview)

The second chapter of this book is reserved for machine learning algorithms you will face out there. We explore regression algorithms, classification algorithms, clustering algorithms, ensemble learning, and so on. Also, we implement these algorithms from scratch with Python and utilize existing solutions from Sci-Kit Learn Library.

This includes algorithms like Decision Trees, Random Forest, Linear Regression, SVM, etc.

Personally, I found a lot of value in implementing machine learning from scratch because that proved to me that I understood how they work and how I can use them. That is why this is also included in this book.

You can check out the preview of this chapter here.


The third part of this book covers regularization. We reveal what regularization is and how to use it. We learn about different options that we can use from Sci-Kit Learn Library.

Optimization (preview)

The fourth chapter is all about optimization. We learn how the most popular optimization technique – stochastic gradient descent works, and we explore its variations. Also, we learn about more advanced optimization techniques like AdaGrad and Adam. We implement those algorithms from scratch too and use existing implementations from Sci-Kit Learn. In this chapter, we learn how to optimize hyperparameters as well.

You can check out the preview of this chapter here.

Deploying Machine Learning Model (preview)

The fifth chapter of this book covers one burning topic – the deployment of machine learning algorithms. We learn about REST API and how to deploy machine learning algorithms with Flask and Docker. We learn how to do the same thing with more advanced technology – FastApi.

You can check out the preview of this chapter here.

Deep Learning with PyTorch (preview)

The sixth chapter covers deep learning and neural networks. In this chapter, we learn about motivations for this type of learning. We cover topics such as neurons, connections, activation functions, and convolutional neural networks. We learn how to use PyTorch for the implementation of these topics. We also learn how we can deploy neural networks with TorchServe.

You can check out the preview of this chapter here.

Best Practices

The seventh chapter is focused on some of the best practices when it comes to machine learning.

It is clear and concise and exceeded my expectations.

Another great book by Nikola. It is clear and concise and exceeded my expectations. I was surprised by the level of detail on the optimization topic. I would highly recommend reading the book if you are new to machine learning.

Boban Miksin

CTO, Vega IT

“The biggest plus is the friendly, human tone in which the author leads you through this inherently complex subject.”

If you want to start dabbling in Machine Learning, but don’t know where to begin, you can count on this book to lay the groundwork. It will take you from “how it all started”, over mathematical models, across code snippets, all the way to detailed instructions on to deploy your work and use it in real life. An all-in-one package for learning Machine Learning! 

All the technical details aside, the biggest plus is the friendly, human tone in which the author leads you through this inherently complex subject.

Ana Marija Ćirić

Project Manager, m-pioneers GmbH



Ultimate Guide to Machine Learning with Python is designed to help you become Machine Learning Superhero

“Many useful and otherwise hard-to-find recipes are collected within this comprehensive guide.”

The content on Nikola’s blog has been my go-to place for efficient learning of new concepts in the field of AI, as well as for revisiting some old ones for better understanding. I am delighted to see those fragments of experience organized within this book.

It is written in a spirit of a person giving a helping hand to a very dear friend. Besides a well-balanced combination of theory and implementation, many useful and otherwise hard-to-find recipes are collected within this comprehensive guide to help the readers on their journeys at all stages of seniority.

Stevan Ostrogonac

Data Scientist, Poslovi Infostud



Become Python superhero

Python for

Data Science


Python is the most popular programming language. One of the main reasons for Python’s popularity is due to the rise of data science as a field in general.

In this guide, we cover everything from the basics of Python to more complex topics like Object-Oriented programming, Unit Testing and libraries like NumPy and Pandas.



Just enough math to make you dangerous

Mathematics for Machine Learning


The most common question that I get at meetups and conferences is: “How much math should I know?”. This guide got you covered!

While some people will argue that even this much math is too much, in my humble opinion, knowing this bare minimum will help you understand concepts of machine learning and AI in more depth.



Visualize – Prepare – Win

Guide to

Data Analysis


One of the main tasks of data scientists is to visualize and analyze data. Before we can use some fancy machine learning or deep learning model, we need to understand the data we are dealing with.

In this guide, we cover data visualization with libraries like Matplotlib and Seaborn. Also, we learn how to use those visualization techniques for exploratory data analysis and feature engineering.



Neural networks are beautiful

Neural Network Zoo

There are many different architectures of neural networks that are used for different types of problems. Here we listed visual representation of some of the architectures that are out there.



We got your back

SUPPORT Discord 

Private Facebook group in which all students can ask questions and connect with like-minded people.

About the AUTHOR


From Developer to Developers with love

Nikola Živković is a software developer with over 10 years of experience in the industry. He’s earned a Master’s degree in Computer Science from the University of Novi Sad in 2011, but by then he had already been working for several companies. During the time span in the industry, he has worked on large enterprise systems, as well as on small web projects. 

In the past couple of years he has been specializing in Data Science (Machine Learning and Deep Learning to be precise) and his goal is to unite this knowledge together with some best traditional programming practices.

He is also experienced as a speaker and author, talking at meet-ups and conferences, and as a guest lecturer at the University of Novi Sad. You can find his online courses on Pack Publishing and Educative. 


In case you are still wondering…

Which Plan should I buy?

That mostly depends on your skill level and the budget. The Premium bundle that contains all bonuses is the best value for money and the best option if you are a complete beginner. However, the main book itself contains 270+ pages of goodies that will help you build production-ready Machine Learning Models.

Could I just find all this on the Internet for free?

Nowadays you can find most things online. However, finding the correct and well-explained solution takes time and effort. The internet is full of time-wasters and outdated solutions. This bundle contains fresh and up-to-date information. Don’t waste your time and money anymore.

Where can I find Examples of the Content?

We provide a lot of free content (200+ free articles) on our blog – rubikscode.net. Also, you can find previews of some chapters of the book here.

Is this bundle really worth the price?

This bundle is quite an investment. However, this bundle contains content from different areas, for which otherwise you should buy separate courses for. Instead of spending money on 10 courses, you can learn it all in this program.

If you have more questions shoot us a mail at info@rubikscode.net

It covers everything, from basic algorithms to examples of how to expose them through API.”

This book is an excellent jumping board to the world of machine learning. It covers everything, from basic algorithms to examples of how to expose them through API and the best practices to use along the way. All that comes with a well-written Python code and math background that will tickle your brain, forcing you to dive deep into the topic.  

I would recommend “Ultimate guide to machine learning with Python” not only to ones starting in the machine learning world but also to those already comfortable in it, as it is a good place to revisit fundamentals. And, I have to add, this is the kind of book that you will read twice.

Kosta Kupresak

Software Developer, Vega IT


 Pick one of the options and become Machine Learning Superhero today!

Interested in Team Licence. Check here.