Machine Learning! Let us face it has become an essential aspect of our lives. We may not realize it, but courtesy of Machine Learning, the world as we know it has become more accessible. From Facebook suggesting posts on our feeds to Siri carrying out our instructions, these are the modern intricacies resulting from Machine Learning.
Machine Learning may sound difficult with algorithms having fancy names like vector machine and logistic regression. But truth be told, these algorithms, regardless of the complicated terms, essentially capture the underlying structure of the data. It captures the relationship between the variables.
Now some of these relationships are clear. Just like with time, the value of your car decreases, understanding these relationships are more straightforward. However, certain variable relationships are not that straightforward. And this is where Python comes in.
Python is a high-level programming language that underlines code readability with significant indentation. It is an object-oriented approach that helps programmers write clear, logical code.
Now you may be asking, why Python for Machine Learning? Experts believe that Python is the best choice for Machine Learning given that it is easy to understand. So, when using Python, data validation becomes quick and free of errors. Also, Python allows programmers to do complex tasks without extensive coding.
Therefore, Python is an essential part of Machine Learning. Or at least, it makes understanding those variable relationships easier.
But how do you master Machine Learning using Python? We have recognized five steps to do the same, and listed below is how to master basic Machine Learning using Python.
Step 1: Master the Basics of Python
If you are planning to leverage Python for Machine Learning, you need to have a basic understanding of it.
Python as a programming language was created for easy readability by Guido van Rossum in 1991. Bearing similarities to the English language with influence from Mathematics, Python has become one of the most popular programming languages in the world.
Unlike other programming languages, Python uses new lines to complete a command. If you have a basic idea of programming languages, you will know that most languages use semicolons and parentheses, which can get confusing.
Also, as opposed to using curly brackets, Python uses indentation and white space to define the scope of functions and classes.
Python is relatively easy, to begin with. Several online courses are available based on your experience and knowledge of the programming language. You can also read a wide variety of books and materials available.
However, before you master Machine Learning, you need to clear your Python fundamentals.
Step 2: Understand How to Conduct Scientific Computing with Python
Most data scientists use MATLAB or Octave to build models or conduct scientific computations. However, Python’s SciPy ecosystem is open source, which includes SciPy (a SciPy library), NumPy (a numerical computation package), and other independent toolkits known as Scikits.
You may argue that the other computing models offer similar functions, but since Python is open-source, it can be used and distributed freely. Also, Python scientific computing provides a data-driven approach, which can be crucial to recognizing patterns and trends.
Therefore, most Machine Learning experts use Python to conduct scientific computing. If you plan to master Machine Learning using Python, you need to understand how to use the same to teach scientific computing.
Most Machine Learning courses teach you how to use Python for the same.
Step 3: Know the Fundamentals of Classification in Machine Learning using Python
Classification in Machine Learning allows specialists to assign label values, making it easier to categorize the data and identify particular types of it.
A typical example of classification is the spam filtration system in your email. It classifies whether an email you received is spam or not. This is because Machine Learning allows you to categorize it by implementing specific criteria.
Now classification is essential for training data. It can be classified into two segments. These include:
Binary Classification separates the outcome into two groups based on the classification rules.
Multiclass Classification segregates data into one group out of multiple groups available.
Now, Python uses libraries such as Scikit-learn and Pandas to help build classification models for Machine Learning. By using Python, you can import the data as well.
Since Python is an easy language, these can be installed and imported without much effort. This is why most Machine Learning specialists use Python libraries for classification.
And if you aim to master Machine Learning, you should learn how to classify using Python. Most Machine Learning courses will teach you how to do the same.
Step 4: Gain Knowledge in Regression
In Machine Learning, regression is the process of finding a correlation between dependent and independent variables. One of the most common examples of regression algorithms can be seen in weather models.
You feed past data to the model to get the weather prediction for the next few days.
Although it is another dominant form of supervised learning, there is a stark difference between classification and regression. While classification makes data predictions with finite classes, regression is used to predict continuous numeric data.
Now coming to using Python, well, most experts use it for linear regression. It is relatively easy and can be helpful when trying to get detailed results. The Scikit model is often used to implement regression.
Furthermore, the statsmodel in Python helps you go beyond the functionalities of the Scikit model, especially when you want to implement linear regression.
Statsmodel is a powerful open-source Python model used to make estimations, perform tests and run statistical models.
Therefore, to get well-versed in Machine Learning, you can start by learning about regression using Python. Again, there are various source materials available. You can also opt for specialized Python classes to help you through such topics.
Step 5: Comprehend Clustering Algorithms using Python
Clustering in Machine Learning is used to analyze data that are not classified. Similar data are grouped to ensure that there is uniformity.
This is why Machine Learning experts need clustering algorithms. They use it to identify and group instances similar to each other.
The Scikit library provides a suite of clustering algorithms to choose from. Some of these include Affinity Propagation, K-Means, OPTICS, Spectral clustering, Agglomerative Clustering, and Mixture of Gaussians. Each algorithm has a different function.
Most Machine Learning using Python courses teach how about these clustering algorithms.
If you want to become a master in Machine Learning, you need to become a master in Python. Python is one of the most widely used programming languages for Machine Learning. It is easy, and it is widely available. Follow the steps mentioned above to become an expert in Machine Learning using Python.
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