The best way to learn machine learning in Python is by developing and completing small projects. Python has increasingly become a famous and influential interpreted language. This is because it is an accomplished programming language, it allows for research and development, as well as creating production systems. You can become a pro in machine learning in Python using materials and resources which are usually available for free. Python can also be applied in web applications as a scripting language. It supports wide frameworks like Django for creating and maintaining some complex applications. With the help of Django Python developers, you are able to build custom database solutions and develop customized web applications. This will, in turn, create the right solution for your project, helping your business to achieve full potential. The following easy steps can make you a knowledgeable practitioner of machine learning in Python.
1. Getting to know basic Python skills
If you want to use Python to your advantage to learn machine learning, then it is important first to gain a basic understanding of Python. Getting this understanding has been made easier by the fact that Python has widespread popularity as a programming language, and has been incorporated successfully in machine learning and scientific computing. Therefore, encountering tutorials for beginners will not be a hard task. Where you will start will entirely depend on your level of experience of Python and programming. Begin by installing Python as you will use it in machine learning packages.
2. Foundational machine learning skills
Just like in Python, mustering basic machine learning skills is important. Most of the things data scientists do usually involve the application of machine learning algorithms in non-constant degrees. The positive news is that one does not have to have a Ph. D. level of comprehension of theoretical elements of machine learning to be able to practice them. Remember, programmers do not have to fully comprehend theoretical computer science to be experts in coding.
3. Starting machine learning in Python
This is the moment where machine learning algorithms are implemented using Python's standard machine learning library – sci-kit learn. You can learn tutorials and do exercises by following the guide of iPython notebook, which offers a platform for Python execution. This book can be obtained by downloading it or viewing it online. The tutorials will first introduce you to sci-kit learn. Sci-kit is Python’s machine learning library which has been extensively used in all aspects involving machine learning. You can also go through machine learning notebooks which will allow you to focus on strategies to examine the various models of sci-kit learn.
4. Machine learning topics
After gaining the foundational knowledge in sci-kit, one can now explore to a great extent the diverse common and crucial algorithms. It is recommended to begin with K- means clustering as it is one of the most famous machine learning algorithms. This algorithm is easy to use and usually the best means of tackling unsupervised learning problems. From here, move to classification and study the popular methods – Decision trees. From there you can do a linear regression to solve classification problems and finish with logistic regression.