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Python Top Libraries You Need to Create ML Model

Creating a Model of Machine Learning in Python, you need two libraries. One is 'NUMPY' and the other one is 'PANDA'.


For this project, we are using Python Libraries to Create a Model.
What Are Key Libraries You Need I have explained in the below steps. You need Two.
NUMPY - It has the capabilities of CalculationsPANDA - It has the capabilities of Data processing. To Build a model of Machine learning you need the right kind of data. So, to use data for your project, the Data should be refined. Else, it will not give accurate results. Data AnalysisData Pre-processing How to Import Libraries in Pythonimportnumpy as np # linear algebra
importpandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

How to Check NUMPY/Pandas installed After '.' you need to give double underscore on both the sides of version. 
How Many Types of Data You Need You need two types of data. One is data to build a model and the other one is data you need to test the model. Data to build…

Top 10 Key Uses of Python Language Today

Python is a high-level language and it is popular in Data Science. I am sure many developers do not know how where Python will use it. I have given the top 10 key uses of python.
 Loops

  10 top Python key Uses Complete List

  1. Python Scripts - Very much popular in scripting. You can write scripts in many areas. Like Interfaces, to access files, to access database, and to access the application programs.
  2. Python in Web Development - You can use Python in Web development. There are powerful Python frameworks present such as Django, Flask, Web2Py, and Pyramid. You can develop an application in IoS/Android/Windows.
  3. Graphical User Interface - You can develop applications, which need access to GUI. The libraries and APIs like Tkinter, PyQt, PyGTK, and wxPython allow developers to develop GUI-based apps with simple or complex interfaces. 
  4. System Programming - You can use it in System Programming. Like Protocol management, and OS operations. The Python Standard Library (PSL) has OS and POSIX bindings, which can be used for handling files, multi-threading, multi-processing, environment variables, controlling sockets, pipes, and processes. 
  5. Access to database - Python is used to connect and access data from different types of databases, be it SQL or NoSQL. APIs and connectors exist for these databases like MySQL, MSSQL, MongoDB, Oracle, PostgreSQL, and SQLite. In fact, SQLite, a lightweight relational database, now comes as a part of the Python standard distribution.
  6. Scientific Applications - Very much popular in Scientific Computing and Numeric Analysis
  7. Machine Learning/Deep Learning - Python has many libraries and frameworks like Scikit-Learn, h2o, TensorFlow, Keras, PyTorch, and even core libraries like NumPy and SciPy for not only implementing machine learning algorithms but also using them to solve real-world advanced analytics problems.
  8. Text Analytics - Python can handle text data really well and this has led to several popular libraries like NLTK, Gensim, and spaCy for natural language processing, information retrieval, and text analytics. You can also apply standard machine learning algorithms to solve problems related to text analytics.
Reference Books
  • Text Analytics with Python: A Practitioner's Guide to Natural Language Processing, Second Edition  
  • Derivatives Analytics with Python: Data Analysis, Models, Simulation, Calibration, and Hedging  
  • Python Projects 
Reference Blogs

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Though Ethereum leads in the real-time applications. The latest Hyperledger version is now ready for production applications. It has now become stable for production applications.
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