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python in machine learning

How to Get Started With Python in Machine Learning

Machine learning is one of the fastest-growing technologies in the world. It also leads to artificial intelligence; that is why it is one of the crucial technologies today. That is why lots of students and programmers are showing their interest in machine learning. But they don’t know where to and how to start with machine learning.

FYI Python is the leading programming language for machine learning. Here, we will explain how you can get started with Python in Machine learning. Although the major motive of this blog post is to reveal the best ways to learn Python in machine learning. Here we go:-

Steps to Get Started With Python in Machine Learning

Step 1: Basic Python Skills

Whenever you’re going to do something big with any programming language, we should start from the basics. The majority of programmers or students like you and me try to cover basic at a rapid pace without having a good command of basic concepts. But it is not the best approach; we should spend some time covering the basics first.

So whenever you’re going to start performing machine learning in Python, you should have a good command of the basics of python programming. As we know, Python is the most popular general-purpose programming language that is preferred for scientific computation and numerical computation.

Thus you can easily cover the Python basics from plenty of tutorials available over the internet. If you are familiar with Python’s most basic concepts, you can have a good command of these concepts. And it helps you decide where to start learning Python in machine learning. 

Best Ways to Cover Python Basics

There are plenty of ways to cover the basics of Python. But having a look at the concepts and trying to memorize them is not the best approach. It would be best if you tried to implement the Python basics to master them. For this, you need to install Python, and this install Anaconda can be quite helpful for Python in machine learning.

Anaconda is the industrial-strength Python implement that can be run easily on almost any operating system such as Linux, Windows, and macOS. It contains almost every package that is required to perform machine learning operations.

Some of the packages contained by Anaconda are numpy, scikit-learn, and matplotlib. Apart from that, you can also find an iPython notebook with Anaconda. IPython is an interactive environment of lots of Python tutorials from basic to advanced level.

You can cover the basics from the best books on Python programming. Now you may be thinking that why I am recommending a book for the basics of Python. The major reason behind this, if you learn the basics from the highly authoritative and trustworthy books. Then you can have a good command over them easily because these books also explain the basics with suitable examples.

Keep in mind that no one can ever change the basics of anything. Apart from that, if you have a little bit of experience with Python programming, then you can join some of the leading courses that will help you to cover the Python basics for scientific computation.

Step 2: Foundational Machine Learning Skills

Nowadays, data scientists use machine learning to perform data science operations. But do you think that data scientists have the depth knowledge of machine learning algorithms to perform data science operations?

Of course, not because the data scientist only needs to understand the machine learning algorithms and how it can help their data science projects. They need not explore how it is created as so on. In other words, they need not have theoretical knowledge of machine learning. 

To get started with Python in machine learning, you need not be a scholar to have theoretical knowledge of almost very machine learning concepts. You should have enough knowledge to practice machine learning concepts. Likewise, most coders in the world have little to no theoretical knowledge in computer science and their preferred programming knowledge. 

I would not suggest you skip all the theoretical parts of machine learning. But you can spend some time understanding the foundational machine learning skills with the help of your course materials. If the course providers present it interestingly, then you can acquire these skills easily. You can also prefer the notes of the leading universities’ students. Apart from all these, there are plenty of videos that can help you cover the foundational machine learning skills.

You can test these skills with the help of a short quiz available and the course. Or you can find over the internet to answer and check what you have learned from the theoretical part. You need not dig too deep into the theoretical part because you will not be a teacher or lecturer. It helps you to gain an idea about how the thing works in machine learning.

Step 3: Scientific Python Libraries Overview

Python in machine learning without scientific packages is nearly impossible. All these scientific packages make Python the first choice for machine learning. Python offers a massive number of open source libraries that are used to help in machine learning operations. These libraries are known as scientific Python libraries. And you can use these programming languages to perform elementary machine learning task. Some of the libraries are given below:-

  • numpymainly useful for its N-dimensional array objects
  • pandas – Python data analysis library, including structures such as dataframes
  • matplotlib – It is a 2D plotting library
  • scikit-learn – It is used for data analysis and data mining 

There are a lot more scientific Python libraries around the world. These are only essential libraries and can be used in almost every machine learning project. Seaborn is yet another data visualization library for Python.

Apart from that, lots of other packages are alternative to each other, and you can use your preferred one as per your experience with these packages. Some of the more scientific Python libraries are PyTorch, Tensorflow, Theano, and Keras. You can also try these packages to get efficiency in your machine learning projects.

Step 4: Implement Your Project

After mastering the basics, learning the foundational machine learning skills, and learning the python libraries for machine learning, it is time to implement whatever you have learned. Suppose we don’t implement what we have learned. Then there is always uncertainty whether we can do the task in our real-life situations or not.

Therefore it becomes essential for you to implement whatever you have learned in your journey to learn Python in machine learning. You can implement it by working on self-created projects. You can have the idea of machine learning projects ideas in Python from the internet. There are plenty of blog posts suggesting you the basic to advanced level projects for Python in machine learning.

Step 5: Take Necessary Help

Don’t get nervous or feel shy to take help from the experts. There are already a million machine learning programmers in the world. And most of them use Python in machine learning. Therefore, while you are at the initial stage of machine learning with Python, you can take the experts’ help. If you are stuck in the minor problems within your project, you can Google the solution for instant help.

Apart from that, you can also take the help of experts from online communities. There are a massive number of Machine learning communities around the world fulfilled with lots of experts. You can ask the community question, and the programmers will help you solve the problems. 

Conclusion

All the steps mentioned above can be a great step to get started with Python for machine learning. But keep in mind that you should need to follow the step by step process. Don’t miss a single step because if you miss a single step, then you can’t get a good command over Python in machine learning. Please make sure that you spend enough time in each step to master the concepts.

Don’t run to finish the course or finish things off within a specific time. Keep in mind that everyone has their learning abilities. Some are fast learners, and some are slow learners. Either you are fast or slow, you need to clear every concept and be a master in Python for data science.

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