Machine Learning (ML) is a coming-age technology that needs no introduction today. One of the key aspects of this dynamic technology is Python, a well-known programming language that has now become the topmost choice for data scientists, researchers, as well as software developers working in the area of ML and artificial intelligence.

As the demand for ML data scientists and experts continues to rise, it is the best time to explore how to start ML with Python.

In this post, we will learn more about Python for ML and explore some of the best courses and other resources specifically designed for students, academicians, and researchers looking to learn more about Python in ML.

Introduction to Python

With more than 8.2 million developers across the globe using Python and its demand growing further, it has become one of the most widely used programming languages in 2024. Python is a commonly known programming language commonly used for a variety of purposes including web development, software development, system scripting, and more. Apart from this,

• You can use Python on a server to create robust web applications
• Python can easily connect to database systems and read and modify files.
• You can use Python for rapid prototyping.
• Python can be leveraged to manage big data and perform complex mathematical operations with ease.

Besides, with over 40% of developers preferring Python as a programming language, the language has made its mark among ML professionals worldwide.

Here are some of the features of Python that make it most relevant in the area of ML.

1. Easy-to-Interpret Syntax

The easy-to-read and intuitive syntax of Python makes it a preferred programming language for developers and data scientists alike. Further, Python is also known for its object-oriented programming features that allow developers a detailed method to organize and plan their code accordingly.

2. An Extensive Set of Libraries

Another feature that makes Python stand out from other common programming languages is its extensive set of library ecosystems. Python comes with multiple frameworks and libraries. Among the most widely used and popular Python libraries for ML are:

• Scikit-learn: It is a popular machine-learning library that provides a variety of tools for a range of tasks such as classification, clustering, and more
• NumPy: NumPy is one of the most basic Python libraries for efficient numerical computations and array operations.
• TensorFlow: TensorFlow is another ML library specifically known for its robust ecosystem and distributed computing capabilities.
• PyTorch: PyTorch is a powerful ML library and is perfect for training complex neural networks.
• Pandas: Pandas is one of the robust Python libraries for data analysis and manipulation, offering intuitive data structures such as DataFrames and Series.

3. Cross-Platform Compatibility

The cross-platform compatibility feature of Python helps software developers and data scientists to create a platform-agnostic code. This kind of flexibility allows easy development of apps that can be used on multiple operating systems such as Windows and Mac.

Why Python for Machine Learning?

Python is used for ML more than any other programming language as it is an easy-to-learn and flexible language with the above-mentioned features, such as simple syntax, cross-platform compatibility, and an abundance of extensive libraries and frameworks.

As one of the most popular, high-level, open-source programming languages, Python is becoming a preferred choice for a wide range of ML tasks. Among these include data analysis, deep learning, and more.

Some of the most popular tools for completing ML tasks with Python include Cy-Kit Learn, Pytorch, Tensorflow, OpenCV, Theano, and ML Pack. Each of these libraries helps achieve a specific ML-related task. For instance, OpenCV is an image manipulation library that is commonly used for image recognition.

How to Learn ML Using Python [Step-by-Step Procedure]

If you are looking to learn ML using Python, here is a step-wise procedure you can follow to do the same:

Step 1: Get Thorough with Programming Basics for ML

The first step in the process of learning ML with Python is to gain a clear understanding of programming. Most developers and data researchers use either Python or R programming language to build ML models. With Python, it is easier to switch later if you move into fields like data analysis in the future. Once you get a good understanding of Python concepts, it is time to apply these concepts to resolve issues.

Step 2: Begin with Data Collection in Python

Once you know the basics of coding in Python, the next step is to start learning data collection and preprocessing in Python. Since an increasing number of organizations need data collection methods for their daily workflows, using APIs to gather this data is also a great option, depending on the task assigned.

Step 3: Do Data Analysis in Python

The next step is to start understanding the concept of data analysis with Python. Data analysis involves finding patterns in massive amounts of data and identifying deeper insights. The idea here is to gain a deeper knowledge of relationships between different variables in the given dataset and offer recommendations based on these insights.

Step 4: Learn ML with Python

This is the stage where you can begin learning ML with Python. Start with learning how to use Python to build predictive ML models first followed by learning how to run these models on real-world datasets. This will give you a better idea of how ML works like in practice, and then you can learn more about the workings of each ML algorithm.

Machine Learning with Python Courses

If you’re a beginner to Python and ML and wondering where to start, read on.

There are several Python machine-learning course available that introduces you to various ML techniques and methods.

Apart from learning how ML is more descriptive than Statistics, these courses also let you know the techniques to use the Scikit learn toolkit and other advanced techniques such as predictive models and building ensembles.

Here are some of the other things and skills that you will learn while enrolling for ML Python courses

• Understanding different types of ML algorithms and when to use them
• Writing Python code that implements different classification techniques, including decision trees, K-Nearest neighbors (KNN), and more
• Evaluating the results on a particular data set using evaluation metrics

Learning Python and Machine Learning through IEEE BLP Courses

Python is the de facto language for varied machine-learning applications today. It is quite a simple, concise, and platform-agnostic language with a plethora of libraries available to facilitate even the most complex machine-learning tasks.

If you are looking to learn more about ML with Python, IEEE BLP offers some of the best courses and a host of other resources.

IEEE BLP gives you the convenience of microlearning along with completely immersive eLearning modules that you can access anytime, anywhere. This kind of flexibility allows you to learn at your own convenience and pace, making it easier to fit your studies as per your individual schedule.

Sign up for IEEE BLP today and take the first step towards mastering ML with Python from a trusted leader and a well-known name in technical education.

FAQs

1. Is Python good for ML?

Yes. Python is widely known for its scalability and great performance in ML. Its ease of use, versatility, and extensive libraries make it a perfect choice for scaling ML operations.

2. What type of Python is used in ML?

Scikit-learn is one of the popular libraries in ML that developers use. It supports both supervised and unsupervised ML algorithms.

3. Can I study ML with Python?

Yes. There are several courses and other resources available to learn ML with Python. It brings great power and versatility to various ML environments.

4. Can Python be used for AI?

Yes. Python is the major code or programming language for both AI and ML. It is more popular than Java because of several advantages it brings, such as good visualization options, flexibility, platform independence, and more.