Master NLP: Text & Speech Processing Pro Guide
Focused View
2:00:04
01 - Fundamentals of natural language processing.mp4
00:58
02 - NLP course strategy.mp4
01:05
01 - What is natural language processing (NLP).mp4
02:20
02 - What are sequences.mp4
02:58
03 - Applications of natural language processing in text data.mp4
03:52
04 - Applications of natural language processing in speech data.mp4
02:23
05 - Historical evolution of NLP tasks and techniques.mp4
04:16
06 - How computers understand sequences in NLP.mp4
00:57
01 - Text preprocessing.mp4
03:06
02 - Text preprocessing using NLTK.mp4
07:10
03 - Text representation.mp4
02:18
04 - Text representation One-hot encoding.mp4
02:06
05 - One-hot encoding using scikit-learn.mp4
03:32
06 - Text representation N-grams.mp4
02:21
07 - N-grams representation using NLTK.mp4
03:03
08 - Text representation Bag-of-words (BoW).mp4
02:01
09 - Bag-of-words representation using scikit-learn.mp4
02:29
10 - Text representation Term frequency-inverse document frequency (TF-IDF).mp4
01:50
11 - TF-IDF representation using scikit-learn.mp4
02:08
12 - Text representation Word embeddings.mp4
02:56
13 - Word2vec embedding using Gensim.mp4
09:08
14 - Embedding with pretrained spaCy model.mp4
05:07
15 - Sentence embedding using the Sentence Transformers library.mp4
03:42
16 - Text representation Pre-trained language models (PLMs).mp4
02:34
17 - Pre-trained language models using Transformers.mp4
05:43
01 - Speech representation Mel-frequency cepstral coefficients.mp4
02:10
02 - Mel-frequency cepstral coefficients (MFCCs) using librosa.mp4
03:28
03 - Speech representation Linear predictive cepstral coefficients (LPCCs).mp4
01:51
04 - Linear predictive coding (LPC) using librosa.mp4
03:58
05 - Speech representation Gammatone filterbank features.mp4
01:21
06 - Gammatone filterbank features using librosa.mp4
03:16
07 - Speech representation Spectrograms.mp4
02:25
08 - Spectrograms using fast Fourier transform (FFT) in librosa.mp4
03:24
09 - Speech representation Speech embeddings.mp4
01:53
10 - Speech embeddings using wav2vec in Transformers.mp4
05:13
01 - Algorithms for natural language processing tasks.mp4
02:05
02 - Types of algorithms in natural language processing.mp4
02:50
03 - Rule-based Regular expressions.mp4
01:51
04 - Regular expression tasks using the re library.mp4
02:42
05 - Rule-based Rule-based parsing.mp4
01:34
More details
Course Overview
This comprehensive course, led by expert Wuraola Oyewusi, covers Natural Language Processing (NLP) from foundational concepts to advanced techniques for both text and speech data. Explore traditional methods and cutting-edge industry standards using popular Python libraries.
What You'll Learn
- Fundamentals of NLP and sequence understanding
- Text preprocessing and representation techniques
- Speech feature extraction and embedding methods
Who This Is For
- Aspiring data scientists entering NLP field
- ML engineers expanding to text/speech processing
- AI practitioners updating their NLP skills
Key Benefits
- Hands-on exercises with NLTK, spaCy, and Transformers
- Coverage of both text and speech processing
- Practical implementation of pre-trained models
Curriculum Highlights
- NLP Fundamentals & Historical Evolution
- Text Processing Techniques
- Speech Feature Extraction
Focused display
Category
- language english
- Training sessions 40
- duration 2:00:04
- English subtitles has
- Release Date 2025/06/02
