What is Machine learning?
Machine learning is a branch of artificial intelligence that encompasses methods or algorithms for building models impulsively from data. In divergence to a system that performs a task according to explicit rules, a machine learning system learns from experience. Although a rules-based system performs a task in the same way (for better or worse) every time, training can improve the performance of a machine learning system by exposing the algorithm to more data.
Why data scientists prefer Python?
1. Large selection of libraries and paintings
One of the things that makes Python such a popular choice, in general, is the abundance of libraries and frameworks that make coding easier and save development time. Machine learning and deep learning are extremely well supported.NumPy, which is used for scientific computing, SciPy for advanced computing, and Scikit-Learn for mining and data analysis, are among the most popular libraries and work together with structures as powerful as TensorFlow, CNTK, and Apache Spark. In terms of machine learning and deep learning, these libraries and structures are essentially Python, while some, like PyTorch, are written specifically for Python.
2. Simplicity
Known for its concise and readable code, Python is virtually unmatched in ease of use and simplicity, especially for new developers. This has several benefits for machine learning and deep learning. Python has simple syntax means that it is a faster development application than many programming languages and allows the developer to test algorithms quickly without having to implement them.
In addition, easy-to-read code is invaluable for collaborative coding or when machine learning or deep learning projects change hands between development teams. This is especially true when a project contains a large amount of custom business logic or third-party components.
3. Plenty of support
Python is an open-source programming language and is supported by many resources and high-quality documentation. There is also a large and active community of developers eager to provide advice and support at all stages of the development process.
Why is Python most preferred for Machine Learning?
Data scientists face complex problems and the problem-solving process has four main stages: data ingestion and cleaning, data mining, data modeling, and data visualization.
Now, let’s take a look at the steps involved in troubleshooting data science problems and what Python data mining packages should be essential in your toolkit as a data scientist:
- Data collection and cleaning
- Data mining
- Data modeling
- Visualization and interpretation of data
1. Data collection and cleaning
Python allows you to play with almost all types of data available in a variety of formats, such as B. CSV (comma-separated value), TSV (tab-separated value), or JSON from the web.
Regardless of whether you want to import SQL tables directly into your code or need to scratch a website, Python will help you accomplish these tasks with its dedicated libraries, such as PyMySQL or BeautifulSoup. The former allows you to effortlessly connect to a MySQL database to execute queries and extract data, while the latter helps to read XML and HTML-like data. After extracting and replacing the values, you should also look for missing records during the data cleaning phase and replace the non-values accordingly.
If you get stuck with a specific dataset, thanks to the strong and vibrant Python community, you can find a solution by doing a Google search on that dataset and Python.
2. Data mining
After your data is collected and stored, make sure that it is standardized for all collected data. Once you have your data, you need to find out what business question to answer and then turn that question into a data science question.
To do this, examine the data to identify its properties and divide it into different types, such as numeric, ordinal, nominal, categorical, and so on, to provide the necessary processing.
Once the data has been categorized by type, NumPy and Pandas, Python data analysis libraries, can help you get the information out of the data, being able to manipulate it easily and efficiently.
3. Data modeling
This is a very important stage in the data science process, where you would strive to minimize the dimensionality of your data set. Python has many advanced libraries that allow you to take advantage of machine learning to perform tasks associated with data modeling. The Scikit-Learn code library offers an instinctual user interface and helps you apply machine learning algorithms to your data without complexity. After you finish modeling the data, you need to view and interpret the data to get actionable insights.
4. Visualization and interpretation of data
Python has many data visualization packages. Matplotlib is the most widely used library for creating basic charts and graphs. If you need well-designed advanced diagrams, you can also try another Python library, Plotly.
Another Python library, IPython, helps to visualize interactive data and supports the use of a GUI toolkit. If you want to embed your results in interactive web pages, you can use the nbconvert function to convert your IPython or Jupyter notebooks into large pieces of HTML.
After viewing the data, the presentation of your data is of utmost importance. It must be done in such a way that the results depend on the questions you asked at the beginning of your project. To know more visit us at Python Training in Pune
