Python in ML: Why You Should Use Python for Machine Learning

Artificial Intelligence (AI), the appearance or simulation of human intelligence within a machine, is popular today because it can solve a huge number of problems within specific categories using well-known algorithms. Machine Learning (ML) is a sub-category of AI that depends on large datasets to affect the computations of algorithms to allow a generalized result that an application can apply to unseen data. In other words, ML provides an experience-based method to use AI techniques to solve problems realistically. Python ML features handle the required amounts of data effectively, and offer access to a broad range of algorithms using libraries like scikit-learn and SciPy to solve the various ML problem categories. Python’s capabilities deliver in a manner that is understandable in AI Python code, and which reduces the learning curve that most developers face.

Python, AI, and Machine Learning

It’s possible to divide AI into two general areas: applied, where the AI solves a specific problem like assembly line control or trading stocks, and generalized; where the AI gains experience through various means in the form of algorithm augmentation. Python ML is part of the generalized AI branch and requires the use of sufficient amounts of data to train the algorithm. This process uses example data to change the calculations of an algorithm to solve general problems. When using Python for AI, it’s possible to reduce training time using a GPU by adding library support for libraries like Keras and TensorFlow. After training, you can configure a Python ML solution to continue learning so the solution becomes increasingly better at performing a task over time. ML is currently used in:

• Fraud detection
• Resource scheduling
• Complex analysis
• Automation
• Customer service
• Safety systems
• Machine efficiency
• Access control
• Predicting wait times

There are more ML applications. The one thing that’s striking about most of them is that they’re mundane in nature. The applications that ML is best at solving are repetitive, not very exciting, and generally things that humans wouldn’t want to do anyway. The ability of Python ML to speed calculations along is the reason why Python is used for machine learning applications.

Why is Python Good for ML?

The easiest way to discover why Python is good for ML is to test it. Even if you can’t follow along in this section, you can see that using Python for ML is not difficult to implement by reading through the steps.

1. Install a copy of Python if necessary. Check the installation by typing python --version and press Enter. The output shows the Python version number.

2. Install a copy of scikit-learn, if necessary. Check the installation by typing pip show scikit-learn and press Enter. The output shows the scikit-learn version. The scikit-learn package is one of the essentials for machine learning because it provides data analysis capability.

3. Open the Python interpreter by typing python and press Enter in a terminal window. The Python prompt appears.

4. Type from sklearn.datasets import load_iris and press Enter. This English-like syntax imports a specific function, load_iris(), from the sklearn.dataset namespace. Python doesn’t make it hard to find datasets for testing. The scikit-learn package provides several datasets and if these aren’t enough, it’s possible to find repositories for just about every other need.

5. Type data = load_iris() and press Enter. The variable data now contains the iris dataset. No other language makes it nearly as straightforward to load data for testing and experimentation purposes. Using known datasets for testing and experimentation corrects errors in your code.

6. Type print(data.data.shape) and press Enter. The size of the iris dataset, 150 rows by 4 columns, appears. Learning about the content of datasets and manipulating them in all the ways needed for machine learning, are fortes of Python.

7. Type quit() and press Enter. The Python session ends. When you experiment with the iris dataset and discover more about it, it’s clear why you want Python for your machine learning needs.

Is Python Better Than Other Languages for ML?

No single language is ultimately better than another overall. Languages have strengths in certain areas and Python has more than its share. R is Python’s major competitor in the ML arena. Compared to R, Python provides a complete language you can use for experimentation, modeling, and production systems. In addition, Python’s library offerings are more business and research-oriented than R’s. However, R holds a significant advantage in statistics, and some might say that its graphics libraries are better than Python’s for research purposes. Other popular languages used for machine learning are:

• Java/JavaScript: Is used extensively in business environments, so business developers feel comfortable using it.

• Julia: Provides high-performance numerical analysis and computation for large, complex computation-intensive problems.

• LISP: Considered the most efficient and flexible ML language for solving specific problems, rather than creating a generalized result.

Each language has advantages and disadvantages. Python outshines them in the general creation of ML applications. In addition, Python has a high appeal according to the Tiobe index, which provides a measure of overall language popularity. The use case scenario driving this popularity is ML, where 57 percent of data scientists and ML developers use it as their primary language.

Conclusion

AI and ML are complex topics that require attention to detail to obtain a good result. Even if you choose to use another language for development, Python is the best choice for learning about both AI and ML because it’s not difficult to use and you can accomplish much, with little code. Fortunately, experimentation is available using Linode. Using the smallest Linode, you can create a Python setup in minutes and start experimenting the same day. Consequently, using Python saves time, money, and effort when you need to gain entrance to the most important new technologies today.