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Explore Python's top NLP libraries like NLTK, spaCy, Gensim, TextBlob, and Transformers, each specializing in tasks like tokenization, topic modeling, sentiment analysis, and state-of-the-art language processing.

Dive into Python's powerful NLP libraries: NLTK for education and research, SpaCy for efficient production applications, TextBlob for simplified tasks, and Gensim for advanced topic modeling. Discover the strengths and ideal use cases of each library to unlock the full potential of text analysis and language understanding.

Natural Language Processing (NLP) is a field that bridges artificial intelligence and human language. It aims to enable computers to understand, interpret, and generate human language for practical uses.

Uncover the factors influencing data engineering salaries, including education, company culture, and individual performance. Explore advanced negotiation strategies, salary projections, and tips for a successful career in this lucrative field.

A curated list of roadmaps, mostly about software development, which give you a clear route to improve your knowledge or skills.

Awesome Python frameworks. A curated list of awesome Python frameworks, libraries, software and resources.

A curated list of awesome things related to Data Engineering.

Jupyter Notebooks, Beyond the Basics

Beyond Just a Cache, While Redis is famous for caching, it goes further. It's fundamentally an in-memory data structure store. Think of it as a super-fast, super-flexible Swiss Army knife for storing and manipulating data within your application's working memory.

PyTorch is a popular open-source library primarily used for deep learning applications but also offers versatility in general machine learning areas.

Next.js 14 is a major release of the popular React framework that focuses on dramatically improving developer experience and performance. Here's a breakdown of its key features.

React is a JavaScript library for building user interfaces.

List top 20 data engineering tools and terms that will come up often in the field.

Pyspark An Introduction, Advantages, and Features. Pyspark is a Python library that enables programming and analysis in Apache Spark, an open-source distributed computing framework. It combines the power of Spark with the ease of Python to create a versatile and scalable data processing tool. In this article, we will explore what Pyspark is, its advantages, and features.

Data Engineering, What It Really Is and Why It Matters - This article aims to demystify data engineering by explaining its core concepts, responsibilities, and importance in the modern data landscape.

Awesome Python frameworks. A curated list of awesome Python frameworks, libraries, software and resources.

The power of Pipenv and Jupyter Notebook. A guide to using Pipenv with Jupyter Notebook.

Pipenv Cheat Sheet. Pipenv is a packaging tool for Python that solves some common problems associated with the typical workflow using pip, virtualenv, and the good old requirements.txt. It automatically creates and manages a virtual environment for your projects, as well as adds/removes packages from your Pipfile as you install/uninstall packages. It also generates the ever-important Pipfile.lock, which is used to produce deterministic builds.

Top 10 Python Libraries for Data Engineering. Data science is rapidly growing and providing immense opportunities for organizations to leverage data insights for strategic decision-making. Python is gaining popularity as the programming language of choice for data science projects. One of the primary reasons for this trend is the availability of various Python libraries that offer efficient solutions for data science tasks. In this article, we will discuss the top 10 Python libraries for data science.

Airflow is a platform to programmatically author, schedule and monitor workflows. Use airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command line utilities make performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.