Text Cleaning, Spacy, NLTK, Scikit-Learn, Deep Learning, word2vec, GloVe, LSTM for Sentiment, Emotion, Spam, CV Parsing
InstructorKGP Talkie | Laxmi Kant
Free
FreeThe curriculum moves from basic data manipulation in Numpy and Pandas to advanced text processing with Spacy, NLTK, and deep learning architectures.
Instruction includes practical workflows such as building custom Python packages and deploying machine learning models using the Flask framework.
Editorial course preview
These 4 complementary views highlight concrete, legible examples from the course presentation.
This educational slide outlines fundamental Natural Language Processing techniques, listing key concepts such as Named Entity Recognition, Tokenization, Stemming, Bag of Words, and Chat Bots.
This slide introduces two prominent Python libraries, spaCy and NLTK, identifying them as essential tools for text preprocessing in natural language processing.
This slide outlines key algorithms tools for machine learning, displaying four distinct blocks representing Scikit-Learn, TensorFlow 2, Keras, and Pytorch.
This educational slide defines Natural Language Processing as a computer science field that converts text and language into numerical data for machine learning algorithms.
One uncertain signal indicates that certain sections, particularly involving NLTK, contain code that no longer functions due to missing files or deprecated modules in current library versions. This report predates the displayed update date, so while the update may have addressed it, the label does not prove a correction.
Feedback suggests the instruction may favor brief concept overviews and code examples over deep theoretical dives. This signal also predates the displayed update, meaning the update label does not guarantee a change in depth.
The curriculum aligns well with the stated goals of beginners and data scientists looking for a broad NLP toolkit.