The first step to making sure that the course you choose will be worth your time and effort is to check who’s behind it. If it was designed by a renowned institution or a well-known development company, then it’s probably what you’re looking for.
Next, it is also vital to consider why exactly you need the course and ask yourself: what do I want to understand and what skills do I want to improve? That will help you start your journey with machine learning in Python.
Fortunately, we’ve got you covered in that area, and to make things super easy for you we’ve compiled the list below.
You need to remember, however, that taking part in a course is a bit like studying—it definitely highlights key issues, but at the same time, it encourages you to do further research or even requires that you look for some answers on your own.
Even if you have very little or no knowledge of Python, you may enroll in The Complete Machine Learning Course with Python by Udemy. It will help you get an idea of the foundations of deep learning and building machine learning models aimed at solving various problems.
Machine Learning with Python by Coursera is a training course that requires basic Python skills including Python data analysis. It will teach you the purpose of ML, its applications, and algorithms.
With intermediate Python skills and an interest in AI and deep learning, you are ready to participate in Deep Learning Specialization by Coursera. It will teach you how to build and train neural network architectures and master their industry applications such as speech recognition, machine translation, chatbots, and more.
If you’re already familiar with Python syntax, Introduction to Machine Learning by Deepsense is recommended training for you. It will show you machine learning basics and techniques, as well as introduce you to the process of managing data science.
Proficient Python users will appreciate the comprehensiveness of Machine Learning by Stanford, which is one of the most popular machine learning courses ever. It requires programming experience in Python, as all class assignments are in this language.
Browsing through courses and learning platforms, you may be tempted to think that their price is a reflection of their quality. This is not necessarily the case.
It is true, however, that participating in a free course may not bring the results you expect, but for reasons that have nothing to do with its quality. “I usually abandon free courses due to motivation issues,” Łukasz Eckert smiles. “It’s a common mechanism: when you have to pay for something, you’re usually more determined to work, since you made a commitment.”
It is difficult to separate theory from practice; combining the two is the best way to go. The Stanford course, for example, teaches you the theory and then shows you how to put it into practice.
There are courses, however, that only describe methods and their assumptions, so you should carefully read the descriptions to make sure you’ve found what you were looking for.
A nice way to bring the theory and practice together is also reading scientific papers that will make you familiar with code theory and implementation methods.
Their authors sometimes provide a link to their GitHub, where you can see how they implemented a given method, plus you get access to a script that allows you to use benchmarks they created. This type of resource is most desirable for those who want to see how something works.
ML guides and handbooks are coming out in large numbers, proving that ML is thriving and becoming one of the most exciting and fast-paced IT fields.
There are many books dealing with very specific problems, and the deeper you delve into the world of machine learning, the more frequently you’ll reach resources that are tailored to your needs.
There are, however, a few general books that Python users may appreciate—books that offer an introduction to the ML world as well as an overview of more sophisticated techniques for more skilled Python programmers.
Introduction to Machine Learning with Python by Andreas Muller and Sarah Guido is one of the most frequently recommended books for newcomers. It is indeed “an introduction,” which means that experienced users are not its target group.
You should, however, have at least minimum experience with Python before reading it. The book will teach you basic concepts and applications of ML and demonstrate how to create a machine learning application with Python and the scikit-learn library.
If you already know the essentials of Python programming, Machine Learning Using Python by Daneyal Anis will help you get familiar with libraries like NumPy, seaborn, and scikit-learn, as well as the basics of building predictive machine learning models. The clear structure of the book and step-by-step examples make the reading accessible and enjoyable.
Machine Learning with Python: A Practical Beginner’s Guide by Oliver Theobald, as the title suggests, teaches its readers how to code basic machine learning models using Python, clean and manage the data with the use of machine learning libraries, and develop your data management skills with the help of Python. It also provides you with an explanation of key ML concepts and a general overview of specific algorithms.
Advanced Machine Learning with Python by John Hearty is a perfect source of knowledge on the latest, cutting-edge machine learning techniques, including those that are generally applicable yet demanding. It is recommended reading for Python developers willing to improve their skills, learn about top data science trends, or even enter an ML contest.
An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, and Rob Tibshirani is an informative read appreciated mostly by math lovers. The book covers both machine learning and classical statistics, while also giving you a theoretical basis for ML, thanks to which you shouldn’t have any problems with other data analysis methods.
The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, and Jerome Friedman is valuable reading for statisticians, but if you’re interested in data mining and want to focus on concepts rather than mathematics, this book will help you understand the elementary statistics-based ideas used in neural networks, classification trees, and more.
Pattern Recognition and Machine Learning by Christopher Bishop is probably the only book that applies graphical models to machine learning. They’re used to illustrate probability distributions and help you understand basic probability theory.
You don’t need to have any idea of pattern recognition or machine learning concepts, but being familiar with basic linear algebra and multivariate calculus may be more than helpful if you want to make the best use of this book.
You may be wondering if it’s necessary to know math well to be able to use Python for ML effectively. It turns out that it isn’t. “What you should actually have some idea of is electronics, not mathematics,” says Łukasz.
“You can learn how to use Python for ML purposes effectively and understand the core of the matter sometimes even without knowing that math exists at all. You should bear in mind, however, that if you want to keep getting better, mathematical skills may prove useful. It all depends on what you eventually have to deal with.”
Most challenges you’ll face while using Python in ML are typical engineering problems, so you need to know how to make things work. From a data scientist’s point of view, solving such problems doesn’t require mathematical fluency, either. Logical thinking, engineering skills, and some experience are enough to deal with ML.
For those who truly want to take their skills to the next level, commercial work is unavoidable, as it’s not possible to truly develop them while working in a purely theoretical environment. Putting one’s knowledge into practice is essential in this field.
A great learning opportunity for experienced Python users is also entering competitions organized by the members of the vibrant Python community.
When performing the competition tasks, you have a chance to use actual data used by companies, cleaned and prepared exclusively for the contestants. The authors of the best solutions describe them and make their code available for learning and production.
Participating in competitions and challenges such as those announced on Kaggle, DrivenData, AIcrowd, or Zindi gives you the chance to learn production tricks that no course will tell you about, as those tricks are usually very situation-specific.
Speaking of the specificity of challenges, at a later stage of learning you may start focusing on problems related to your particular tasks. This is when blogs come to your rescue. Writings by authors who discuss specific methods and sort out specific cases are indispensable to an experienced Python user.
Just check out these four:
Reading blogs may be a must for your future development. The more you read them, the more similar they may seem later, but staying on top of things means being familiar with the content currently on those blogs.
Understandably, those who are about to start using Python for ML focus on learning the basics. Once you do it, however, you should consider specializing in one particular aspect of ML, for instance, natural language processing.
“Each area has its subareas and subproblems. Once you begin to specialize in them, you can call yourself a researcher,” says Łukasz. “Clients who have specific needs usually look for programmers who already have some experience with this kind of task.”
This proves that choosing a specialization may be an investment in your future career based on working with real people and solving real problems.
Choosing an area that focuses your abilities in a given direction seems to be a natural step. The options offered to Python users by the ML industry include working as a data engineer, a machine learning engineer, or a data scientist. You can also combine the skills used in all these areas when working with MLOps, a tool that manages the machine learning lifecycle through scalability and automation.
But is picking a specialization easy? It is if you already have some commercial experience, since you’re not able to learn everything at home, i.e. without having access to the libraries and real data you use when working commercially.
This is because you’re not able to learn how to solve certain problems until you face them at the commercial level, e.g. having to fix a bug that starts wreaking havoc at 3 AM.
Even though ML is developing rapidly, Python itself doesn’t change too much. That means we work with Python based on what has already been done. New resources continue to appear, but they usually concern the basics.
The changes in the world of Python we need to keep up with are modifications of libraries. They are, however, becoming more and more user-friendly, responding to users’ needs and facilitating the process of learning for those who are only beginning to use them.
As a learner, you may also go beyond using the resources that are already there. Some Python users prefer to learn by running their own blogs. By presenting your ideas, trying to assess different working methods and their effectiveness, and possibly interacting with the readers of your blog, you become a contributor to the Python community and learn more than you could imagine.
The number of resources available to Python users—including its libraries that can be used in a more and more intuitive manner—are a natural reason to believe that Python will play a key role in the future of machine learning.
Python code, which is known for its readability and brevity, is perfect for ML projects. Python’s simplicity and stability, as well as its huge community that offers you help and support, make it a great language for machine learning.
As you can see, there are many options for learning ML in Python. You may have a preference for one over another or even find several that work well together. In any case, we hope this article will help you choose the resources that suit your needs best.
Here at STX Next, we have a talented team of machine learning engineers who are passionate about finding solutions to our client’s problems. If you have a basic understanding and knowledge of ML, and would like to join the largest Python software house in Europe with over 17 years of experience, check out our current job openings.
If you enjoyed this article and would like to learn more about machine learning or Python, we have other free resources you might find interesting, such as:
And in case you have any questions or need some extra help not only with Python or ML but any technology, feel free to contact us directly—we’d be more than happy to support you as you find the best solution to build your next product!