This section of my blog focus on using Machine Learning Methods for data science. I discuss various Machine Learning algorithms that I have been using during my career.
As we enter the new year, it’s important to constantly update and improve our skills to stay competitive in the job market. Here are some top skills to consider learning in 2023: 1. ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING Artificial intelligence and machine learning continue to be in high demand across a variety of industries. From …
Integrated development environments (IDEs) are software tools that provide a comprehensive set of features for writing, debugging, and testing code. They are particularly useful for data scientists, as they can help streamline the process of working with data and building machine learning models. Here are the top ten IDEs for data scientists: Jupyter Notebook: Jupyter Notebook is an …
Battle Between IDEs? Which One To Use For Data Science? Read More »
Django is a popular web framework for building dynamic, database-driven websites with Python. If you want to host your Django website online, there are many free hosting platforms available. Here are the top ten free Django hosting platforms you can consider: PythonAnywhere: PythonAnywhere is a hosting platform specifically designed for Python web applications. It provides a range of …
Power BI and Python are both powerful tools for data analysis and visualization, but they are designed for different purposes and may be more appropriate in different situations. Here are some factors to consider when deciding whether to use Power BI or Python: The complexity of the analysis: Power BI is designed to be user-friendly and easy to use, …
Microsoft’s PowerBI Vs. Python | What Do I Use And When? Read More »
There are many talented Python teachers on the internet who can help you learn this popular programming language. Here are the top ten Python teachers you can find online: Corey Schafer: Corey is a self-taught software developer who runs a popular YouTube channel called Corey Schafer. His Python tutorials are clear, concise, and easy to follow, and he covers a …
As a data scientist, you may find it useful to automate repetitive tasks or create graphical user interfaces (GUIs) to make your work more efficient and accessible to others. Python offers a range of libraries that can help you do this. Here are the top ten GUI automation libraries for Python: PyAutoGUI: This library allows you to …
TOP 10 LIBRARIES TO AUTOMATE YOUR APPS | TOP 10 GUI AUTOMATION PYTHON LIBRARIES Read More »
As a data scientist, you will often find yourself working with the command line interface (CLI) to manage and manipulate data, run scripts, and perform various tasks. Here are the top ten commands that can be useful for data scientists: ls: For Windows, it is dir.This command lists the files and directories in a directory. You can …
TOP 10 CMD (COMMAND LINE INTERFACE) COMMANDS TO LEARN FOR DATA SCIENTISTS Read More »
Optical character recognition (OCR) is a field of computer science and artificial intelligence that focuses on the automated recognition of text in images and documents. Python is a popular programming language for OCR, and there are a wide variety of libraries available for working with text recognition. Here are the top 10 Python libraries for OCR: Tesseract …
TOP PYTHON LIBRARIES FOR OCR (OPTICAL CHARACTER RECOGNISATION) Read More »
Natural language processing (NLP) is a field of computer science and artificial intelligence that focuses on the interaction between computers and human languages. Python is a popular programming language for NLP, and there are a wide variety of libraries available for working with natural language data. Here are the top 10 Python libraries for NLP: NLTK spaCy …
TOP TEN PYTHON LIBRARIES FOR NATURAL LANGUAGE PROCESSING (NLP) Read More »
Machine Learning is not always boring, it is sometimes others laughing at your poor self. Its better if you start laughing at these jokes by yourself! Here are a few for you, enjoy! There are two kinds of data scientists. 1.) Those who can extrapolate from incomplete data. Data science is 80% preparing data, 20% …
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