zainuleb/javascript-challenge-codes

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Created about 3 years ago
Updated about 3 years ago

zainuleb/javascript-challenge-codes

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Text-Mining.github.io

The basic operations related to structuring the unstructured data into vector and reading different types of data from the public archives are taught. Building on it we use Natural Language Processing for pre-processing our dataset. Machine Learning techniques are used for document classification, clustering and the evaluation of their models. Information Extraction part is covered with the help of Topic modeling Sentiment Analysis with a classifier and dictionary based approach Almost all modules are supported with assignments to practice. Two projects are given that make use of most of the topics separately covered in these modules.

Machine-Learning-Data-Science-and-Deep-Learning-.github.io

Machine learning implementation with Data Science, Tensorflow, Artificial Intelligence, and Neural Networks

Jupyter Notebook

socket-io-prep

Prediction-Assignment-Writeup

Prediction Assignment Writeup

fast-api-nlp

Applied-Machine-Learning-with-Python.github.io

Jupyter Notebook

Movie-Recommender-Systems-with-Python.github.io

Providing a basic recommendation system by suggesting movies that are most similar to a particular movies.

Jupyter Notebook

Introduction-to-Data-Science-in-Python.github.io

This course introduces learners to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating CSV files, and the NumPy library. The course introduces data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as group-by, merge, and pivot tables effectively. Along with that you can see tabular data, clean it, manipulate it, and run basic inferential statistical analyses.

Jupyter Notebook